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Impact of Sex on Quality of Life After Acute Coronary Syndromes
Journal: BMJ Open ManuscriptFor ID peerbmjopen-2019-034034 review only Article Type: Original research
Date Submitted by the 03-Sep-2019 Author:
Complete List of Authors: Koh, Youlin; Alfred Hospital, Cardiology Stehli, Julia; Alfred Hospital, Cardiology; Monash University Faculty of Medicine Nursing and Health Sciences Martin, Catherine; Monash University, Centre of Cardiovascular Research and Education in Therapeutics Brennan, Angela; Monash University, Centre of Cardiovascular Research and Education in Therapeutics Dinh, Diem; Monash University, Centre of Cardiovascular Research and Education in Therapeutics Lefkovits, Jeffrey; Royal Melbourne Hospital, Cardiology Department; Monash University Zaman, Sarah; Monash University, Centre of Cardiovascular Research and Education in Therapeutics
Sex, Quality of life, Acute coronary syndrome, Mood disorders, Mobility Keywords: limitation
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1 2 3 4 Impact of Sex on Quality of Life After Acute Coronary Syndromes 5 6 7 Youlin Koh (MBBS) 1, Julia Stehli (MD) 1,2 Catherine Martin (PhD) 3, Angela Brennan (CCRN) 3, Diem 8 9 T. Dinh (PhD) 3, Jeffrey Lefkovits (MBBS) 3,4, Sarah Zaman (MBBS, PhD) 5, 6 10 11 12 13 1 Cardiology Department, The Alfred Hospital, Melbourne, Australia 14 15 2 Department of Medicine, Nursing and Health Sciences, Monash University, Melbourne, Australia 16 17 18 3 Monash University,For Centre of peer Cardiovascular review Research and Education only in Therapeutics 19 20 4 Cardiology Department, Royal Melbourne Hospital, Melbourne, Australia 21 22 5 Monash Cardiovascular Research Centre, Monash University, Melbourne, Australia 23 24 6 Monash Heart, Monash Medical Centre, Melbourne, Australia 25 26 27 28 29 Corresponding author details: 30 31 Dr Sarah Zaman 32 33 Monash Heart, Monash Medical Centre 34 35 36 246 Clayton Road, Clayton 37 38 Melbourne, Victoria, Australia, 3168 39 40 Email: [email protected] 41 42 Telephone: +613 9594 6666 43 44 45 Fax:+613 9594 6475 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
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1 2 3 ABSTRACT 4 5 6 Objective: Women have reported higher mortality and major adverse cardiovascular events (MACE) 7 8 following acute coronary syndromes (ACS) compared to men. Sex differences in quality of life (QoL), 9 10 depression and anxiety following ACS are not as well described. 11 12 13 14 Methods: Consecutive patients treated with percutaneous coronary intervention (PCI) for ACS were 15 16 17 prospectively recruited from 30 metropolitan centres as part of the Victorian Cardiac Outcomes 18 For peer review only 19 Registry (VCOR) in this cohort study. Patients were followed throughout their time in hospital and at 20 21 30 days. QoL was assessed using the EuroQol-5D (EQ-5D-3L) instrument by telephone at 30 days. 22 23 Multivariate analysis to determine independent predictors of QoL was performed. 24 25 26 27 28 Results: A total of 16,517 patients underwent PCI for ACS (22.9% females). Compared to men, 29 30 women were significantly older (median 68.7 years, interquartile [IQR] 59.0-78.0 vs 62.0 years, IQR 31 32 54.0-71.0) with more diabetes, cerebrovascular disease and renal failure but were less likely to 33 34 35 present with cardiac arrest. At 30-days 2,497 (64.7% females) completed the QoL EQ-5D-3L 36 37 instrument. As for the primary outcome, female sex was independently associated with moderate or 38 39 severe impairment in all EQ-5D-3L domains including mobility (OR 2.38, 95% CI: 2.06-2.75, p<0.001), 40 41 personal care (OR 2.14, 95% CI 1.73-2.66, p<0.001), activities of daily living (OR 1.84, 95% CI 1.63- 42 43 44 2.08, p<0.001) , pain/discomfort (OR 1.44, 95% CI 1.24-1.67, p<0.001), and anxiety/depression (OR 45 46 1.49, 95% CI 1.30-1.70, p<0.001). There was no significant difference between the sexes with regard 47 48 to major adverse cardiovascular (secondary) outcomes. 49 50 51 52 53 Conclusions: Female sex was a predictor of poorer QoL following PCI for ACS including significantly 54 55 higher pain, discomfort, anxiety and depression. This was independent of age, comorbidities and 56 57 ACS presentation. 58 59 60
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1 2 3 ARTICLE SUMMARY 4 5 6 Strengths and limitations of this study 7 8 Consecutive patients were approached for recruitment with large numbers obtained 9 10 First of its kind in the Australian literature to examine the impact of sex on quality of life 11 12 following an acute coronary event. 13 14 15 Highlights the need for early screening of women for impaired mobility and reduced mood 16 17 following an acute coronary event. 18 For peer review only 19 Sex measured rather than gender role, which encourages us to develop accurate measures 20 21 for gender for future studies. 22 23 24 Socio-economic status not measured in this study. 25 26 27 28 KEYWORDS 29 30 Quality of life; sex; acute coronary syndrome; mood disorders; mobility limitation 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
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1 2 3 INTRODUCTION 4 5 6 Numerous sex differences have been detected in the outcomes of patients with coronary artery 7 8 disease. Perhaps the most striking is that women who have suffered an acute coronary syndrome 9 10 (ACS) have higher mortality rates than men.[1-3] In the same vein, women receive significantly less 11 12 invasive angiography and timely revascularisation, compared to men.[1] Women with ACS have 13 14 more comorbidities, such as diabetes and cerebrovascular disease, and are on average 5-7 years 15 16 17 older, than men. However, these differences cannot fully account for the significant sex 18 For peer review only 19 discrepancies observed in mortality and morbidity following ACS. This has prompted investigation 20 21 into non-physical factors such as sex differences in access to care and patient-centred outcomes 22 23 including symptoms, functional status or health-related quality of life (QoL).[3-5] Among these 24 25 26 patient-centred outcome data, women have been observed to report lower health-related QoL, 27 28 compared to men [6-8]. This may be partly explained by biological factors such as coronary anatomy 29 30 and ejection fraction, but sex has also been considered to play a prominent part.[3] Our prospective 31 32 large cohort study, utilising data from the Australian Victorian Cardiac Outcomes Registry (VCOR), 33 34 35 aimed to further characterise potential sex differences in reported quality of life following an acute 36 37 coronary syndrome, known to be a psychologically impactful event. 38 39 40 41 METHODS 42 43 44 This study was carried out according to the principles of the STROBE (Strengthening the Reporting of 45 46 Observational Studies in Epidemiology) checklist for cohort studies.[9] 47 48 49 50 Data Source and patient population 51 52 53 VCOR is an Australian, state-based clinical quality registry designed to monitor the performance and 54 55 outcome of percutaneous coronary interventions (PCI) in Victoria. VCOR was established in 2012 and 56 57 is engaged at all 30 Victorian hospitals (13 public and 17 private).[10] VCOR prospectively collects 58 59 baseline demographic, procedural characteristics, in-hospital outcome and 30-day outcome data on 60
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1 2 3 all patients who undergo PCI at a given facility through a secure web-based data collection system. 4 5 6 All data entry personnel are registered with VCOR and data is entered by trained hospital staff. Data 7 8 integrity is ensured with regular audit activities conducted by the central registry.[11] VCOR is 9 10 currently funded by the Victorian Department of Health and Human Services. Quality of life (QoL) 11 12 data is collected at 30 day by means of the EQ-5D-3L questionnaire. Patients are contacted over the 13 14 phone and invited to complete the questionnaire. The study was approved by the institutional ethics 15 16 17 committee of each hospital with an opt-out consent model. 18 For peer review only 19 20 21 This research was performed without patient involvement. Patients were not invited to comment 22 23 on the study design and were not consulted to develop patient relevant outcomes or interpret the 24 25 26 results. Patients were not invited to contribute to the writing or editing of this document for 27 28 readability or accuracy. 29 30 31 32 Inclusion and exclusion criteria 33 34 35 The current study included consecutive patients who received successful or attempted PCI for an 36 37 ACS from 2013-2016. Those with all EQ-5D-3L questions missing and those who had died before 30 38 39 days were excluded from the quality of life analysis. ACS included ST elevation MI (STEMI), non-ST 40 41 elevation MI (NSTEMI) and unstable angina (UA). STEMI was defined as elevated biomarkers and 42 43 44 new or presumed new ST-segment elevation in two or more contiguous leads. NSTEMI was defined 45 46 as presence of elevated biomarkers and at least one of either, ECG changes (ST segment depression 47 48 or T wave abnormalities), or ischaemic symptoms. UA was defined as at least one of either, ECG 49 50 changes (ST segment depression or T wave abnormalities), or ischaemic symptoms in the absence of 51 52 53 elevated biomarkers. Left ventricular ejection fraction (LVEF) was collected during the index 54 55 admission and four weeks post-MI for STEMI patients and within six months prior to the index 56 57 admission and four weeks post-discharge for NSTEMI patients. LVEF could be assessed by 58 59 echocardiogram, LV angiography, cardiac magnetic resonance imaging, or nuclear imaging. Normal 60
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1 2 3 LVEF was defined as a LVEF>50%, mild dysfunction as a LVEF 45–50%, moderate dysfunction as a 4 5 6 LVEF 35-44% and severe dysfunction as LVEF<35%. 7 8 9 10 Primary and secondary outcomes 11 12 The primary endpoint was 30-day quality of life assessment using the EQ-5D-3L, an instrument for 13 14 describing and valuing health states. The EQ-5D-3L is composed of five dimensions: mobility, self- 15 16 17 care, usual activities, pain/discomfort and anxiety/depression for which the patients can answer 18 For peer review only 19 with no, some or extreme problems. Patients are also asked to rank on a scale of 1-100, their overall 20 21 health state at the time of completing the questionnaire.[12] Patient responses are used to derive a 22 23 composite EQ-5D-3L index score, which was adjusted for in our analysis as per Australian values.[13] 24 25 26 The index score was categorized into 1, 0.5-0.9 and <0.5. An index score of 1 is consistent with 27 28 perfect health. Scores of 0.5-0.9 reflect a lower health utility, and scores below 0.5 reflect lowest 29 30 health utility. In addition to the above, we asked patients to rate their own health state on the EQ- 31 32 VAS (Visual Analogue Scale) on a scale of 0-100, with higher scores reflecting better self-perception 33 34 35 of current health state. 36 37 38 39 Secondary outcomes were 30-day major adverse cardiac events (MACE), major adverse cardiac and 40 41 cerebrovascular events (MACCE), and major bleeding. 30-day MACE was defined as new or recurrent 42 43 44 MI, stent thrombosis and target vessel revascularisation. 30-day MACCE was defined as MACE with 45 46 the inclusion of stroke. Major bleeding was defined according to the international Bleeding 47 48 Academic Research Consortium (BARC) and encompassed categories three and five which included 49 50 bleeding that required blood transfusion, cardiac tamponade, intracranial haemorrhage and/or any 51 52 53 fatal bleeding. 54 55 56 57 Statistical analysis 58 59 60
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1 2 3 Categorical variables were analysed using Chi-square tests or Fisher’s exact tests and expressed as 4 5 6 numbers and percentages. Normally distributed continuous variables were compared using an 7 8 independent t-test and expressed as mean and standard deviation, whereas skewed data was 9 10 compared using Wilcoxon rank-sum and expressed as median and interquartile range (IQR). 11 12 Univariate ordered logistic regression was used to analyse the EQ-3D-3L questionnaire, whereas 13 14 univariate and multivariate ordered logistic regression was used to analyse the EQ-3D-3L index 15 16 17 category values. For the QOL questions an OR > 1 refers to a relative change from a good QOL 18 For peer review only 19 response towards a poorer QOL response, whereas an OR > 1 for the index category refers to a 20 21 relative change from a low (poor) index response towards a high (good) index response. An OR < 1 22 23 for the QOL questions refers to a relative change from a poor QOL response towards a good QOL 24 25 26 response, whereas an OR < 1 for the index category refers to a relative change from a high (good) 27 28 index response towards a low (poor) index response. A P value <0.05 was considered statistically 29 30 significant for all analyses. Statistical analyses were performed using Stata version 14. 31 32 33 34 35 RESULTS 36 37 From a total of 17,132 consecutive ACS patients treated with PCI, 615 patients died by 30 days and 38 39 were excluded from the 30-day QoL analysis. Of the remaining 16,517 patients, 7,440 (58.7%) males 40 41 and 2,223 (57.8%) females fully completed the 30-day EQ-5D-3L questionnaire and another 875 42 43 44 (6.8%) males and 274 (6.9%) females partially completed the EQ-5D-3L questionnaire. 45 46 47 48 Baseline and procedural characteristics 49 50 The baseline and procedural characteristics and discharge medications are shown in Table 1. Women 51 52 53 were significantly older than men (median 68.7 years, interquartile range [IQR] 59.0-78.0 vs 62.0 54 55 years [IQR 54.0-71.0]) with more diabetes, history of cerebrovascular disease and renal impairment. 56 57 Men had a higher rate of previous coronary artery bypass grafting (CABG), previous PCI, impaired 58 59 LVEF and pre-hospital cardiac arrest. Women had higher rates of UA and NSTEMI, whereas men 60
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1 2 3 presented with higher rates of STEMI. Radial vascular access was more commonly used in men than 4 5 6 women (p<0.001). The majority of coronary lesions were successfully treated, with most patients 7 8 received drug-eluting stents. Women had a higher incidence of right coronary artery (RCA) and left 9 10 anterior descending (LAD) lesions, whereas men had more left main and circumflex (LCx) lesions. 11 12 13 14 Missing data vs non-missing data patients 15 16 17 Of the 12,544 patients recruited, 4,696 (37.4%) had all QOL missing and were removed from the 18 For peer review only 19 analysis. Patients who did not provide a complete response (missing data) to the EQ-5L-3D 20 21 questionnaire were analysed against those who provided a complete response. Those who had 22 23 missing data presented more acutely with slightly higher rates of STEMI, cardiogenic shock, out-of- 24 25 26 hospital cardiac arrest and intubation. They also had higher rates of documented cerebrovascular 27 28 disease suggesting more widespread vasculopathy at baseline. However, missing data patients were 29 30 discharged home at the same rate as non-missing data patients, suggesting similar recovery to 31 32 baseline. They also had higher rates of cardiac rehabilitation referrals, perhaps reflecting recognition 33 34 35 of their sicker baseline status. These results are summarised in Table 2. 36 37 38 39 30-day Quality of Life Outcomes 40 41 Thirty-day QoL results are presented in Table 3 and summarised graphically in Figure 1. In each 42 43 44 assessed domain, women were significantly more likely than men to report the presence of a 45 46 problem. Women reported significantly higher mobility problems compared to men (20.0% vs 9.4%). 47 48 Radial access was associated with fewer mobility problems (10.6% vs 13.6%, p<0.001), though this 49 50 effect was not significant in females. On the EQ-VAS, women’s ratings of their own health state were 51 52 53 significantly lower than that of men (75.0 vs 80.0, p <0.001), despite similar EQ-5D index scores. 54 55 56 57 Women reported significantly higher personal care issues compared to men (7.8% vs 3.8%,) and a 58 59 higher inability to perform usual activities (27.9% vs 17.2%). Women were significantly more likely to 60
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1 2 3 report moderate pain or discomfort compared to men (16.6% vs 12.2%). They were also more likely 4 5 6 to experience moderate or severe symptoms of anxiety and depression, compared to men (24.1% vs 7 8 17.6%). 9 10 11 12 Women had significantly poorer mobility (OR 2.38, 95% CI 2.06-2.75, p<0.001) and higher personal 13 14 care issues (OR 2.14, 95% CI 2.06-2.75, p<0.001) compared to men, on univariate analysis. Usual 15 16 17 activities were affected to a lesser extent but still poorer in women compared to men (OR 1.84, 95% 18 For peer review only 19 CI 1.63-2.08, p<0.001), as were perceptions of bodily pain (OR 1.44, 95% CI 1.24-1.67, p<0.001) and 20 21 mental ill-health (OR 1.49, 95% CI 1.30-1.70, p<0.001), respectively. 22 23 24 25 26 The multivariate predictors of quality of life outcomes as assessed by the overall EQ-5D index score 27 28 are shown in Table 4. Female sex was a significant independent predictor of poorer QoL at 30 days 29 30 (OR 0.57,95% CI 0.50-0.64, p<0.001), along with increasing age, presence of diabetes, 31 32 cerebrovascular disease, left ventricular dysfunction, renal failure, previous CABG or PCI, and in- 33 34 35 hospital major bleeding. 36 37 38 39 Secondary endpoints 40 41 All secondary endpoints are shown in Table 5, after exclusion of the 615 patients who died before 30 42 43 44 days. Female sex was not associated with increased rates of major bleeding, recurrent myocardial 45 46 infarction, stroke, major adverse cardiovascular or combined cardiovascular and cerebrovascular 47 48 events in patients who survived to 30-days. 49 50 51 52 53 DISCUSSION 54 55 This study showed that female sex was an independent predictor of poorer QoL at thirty days 56 57 following an acute coronary event. Women were significantly more likely than men to rate their QoL 58 59 60
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1 2 3 poorly in all domains of the EQ-5D-3L, including mobility, self-care, usual activities, pain, discomfort, 4 5 6 anxiety and depression, and had a poorer self-perception of their health state. 7 8 9 10 Comorbid conditions 11 12 The reasons for poorer overall QoL in women is likely multifactorial - influenced by both biological 13 14 sex and gender as a social construct. Our results add to previous research demonstrating a reduced 15 16 17 QoL in women following ACS in addition to their higher premorbid conditions and older age.[3, 8, 14, 18 For peer review only 19 15] The presence of a chronic condition such as CAD, cerebrovascular disease or renal failure is 20 21 known to predict poorer EQ-5D-3L scores following ACS.[14, 16] It is possible that women with ACS, 22 23 due to their older age and medical comorbidities are also more frail. We know that frailty in turn is 24 25 26 associated with poorer outcomes after ACS, but is difficult to measure and correct for.[17]. Further, 27 28 women had numerically, but not significantly higher rates of major bleeding, recurrent myocardial 29 30 infarction, stroke, or major cardiovascular and cerebrovascular events, compared to men. However, 31 32 this was after the exclusion of 30-day deaths, limiting conclusions drawn from these findings. 33 34 35 36 37 Physical health factors 38 39 We identified greater physical disability in women compared to men at 30 days based on the scores 40 41 of Mobility, Personal Care and Usual Activities in the EQ-5D-3L. These findings are consistent with 42 43 44 previous research and may be explained by an increased prevalence of angina amongst women [8, 45 46 14]. They are also likely influenced by other factors, including the observed increased prevalence of 47 48 diabetes and older age in women with ACS.[18, 19] We found that radial access was associated with 49 50 improved mobility QoL scores, although this effect was not statistically significant in women, 51 52 53 potentially due to smaller numbers. In support of this, the literature has shown that women 54 55 undergoing radial access preferred it for future interventions over those who underwent femoral 56 57 access.[20] 58 59 60
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1 2 3 Our study demonstrated that women reported higher levels of pain and discomfort following ACS. 4 5 6 Ongoing pain is an issue as this tends to occur in older women, is associated with higher 7 8 cardiometabolic risk, and negatively impacts participation in cardiac rehabilitation. [21]. 9 10 Further, women showed increased symptoms of anxiety and depression following ACS. Higher 11 12 baseline rates of depression and anxiety are also associated with lower rates of cardiac rehabilitation 13 14 attendance.[22] Mental health diagnoses disproportionately affect younger women post ACS and 15 16 17 are associated with poorer health related QoL.[15] Our results add to the literature in stable CAD, 18 For peer review only 19 where women have been found to report more depressive symptoms, with similar adverse QoL 20 21 effects.[8] 22 23 24 25 26 How can we improve QoL in women? 27 28 The lower health-related quality of life in women following an ACS invariably leads to loss of 29 30 productive years and subsequent increased cost to our economy.[23] It highlights the need for 31 32 strategies to improve female patients’ QoL. Increasing uptake of cardiac rehabilitation has been 33 34 35 shown to have a positive impact on QoL.[24] We know that women are significantly less likely to be 36 37 referred to, and complete cardiac rehab, compared to men.[1] Development of female-specific 38 39 cardiac rehab programs may be one way to address this, along with approaches that target mental 40 41 ill-health by providing psychological counselling and support. Women continue to receive less radial 42 43 44 access use compared to males, an important finding given that femoral access has been associated 45 46 with increased complications, particularly major bleeding.[25] Ensuring a radial-first approach in 47 48 women may be another way to combat poorer QoL. As demonstrated by our older study cohort, 49 50 frailty also needs to be considered in the presentation of female ACS, with an emphasis on a 51 52 53 multidisciplinary approach involving not only cardiology, but also endocrinology, geriatrics and allied 54 55 health. 56 57 58 59 LIMITATIONS 60
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1 2 3 Measures of QoL are heterogenous, both within and between instruments with consequent 4 5 6 limitations. However, the EQ-5D-3L is widely prevalent (even though cut-offs differ between 7 8 countries), and is preference-based, also making it suitable in cost-utility analysis.[26] The EQ-5D-3L 9 10 does not capture the specific reasons for limited mobility which could be from a variety of reasons— 11 12 for example, shortness of breath, which may be cardiac-related or limitations from musculoskeletal 13 14 disability, which may be related to non-cardiac causes. 15 16 17 18 For peer review only 19 Our study also examines differences by sex, but not by gender, which encourages us to seek and 20 21 develop instruments that measure this in an accurate manner. This is important as various aspects of 22 23 female gender traits can be associated with poorer outcomes and QoL after an ACS, including 24 25 26 restricted access to health care and increased anxiety.[14, 18] Our registry does not routinely 27 28 capture socio-economic data such as marital status and income; this would be useful as the 29 30 literature has identified that respondents of surveys tend to be female, older and more affluent,[4] 31 32 and that socio-economic factors independently influence QoL measurements.[16] Our study was 33 34 35 also limited by missing data as not all patients completed the 30-day EQ-5D-3L. However, its 36 37 strengths lie in the recruitment of consecutive patents undergoing PCI for ACS with all patients 38 39 approached for QoL assessment. 40 41 42 43 44 CONCLUSIONS 45 46 This is the first Australian study to show a significant sex-related difference in quality of life in a large 47 48 registry of patients treated with PCI for ACS. The lower health-related qualify of life in women 49 50 following an ACS is likely to result in loss of productive years with high economic impact. It highlights 51 52 53 the ongoing need to improve outcomes in our female cardiac patients. 54 55 56 57 58 59 60
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1 2 3 4 5 6 7 8 9 10 AUTHOR CONTRIBUTIONS 11 12 Youlin Koh: helped design the research questions, analysed the data, wrote, edited and refined the 13 14 manuscript. 15 16 17 Julia Stehli: designed and conceived the study, helped draft the results and manuscript, critically 18 For peer review only 19 revised the manuscript. 20 21 Catherine Martin: analysed the data, provided statistical analysis and expertise, critically revised the 22 23 manuscript. 24 25 26 Angela Brennan: managed the study, contributed to the study design, oversaw data analysis, 27 28 critically revised the manuscript 29 30 Diem T Dinh: analysed the data, provided statistical analysis and expertise, critically revised the 31 32 manuscript. 33 34 35 Jeffrey Lefkovits: designed and conceived the study, oversaw data analysis, critically revised the 36 37 manuscript. 38 39 Sarah Zaman: designed and conceived the study, oversaw data analysis, helped draft the results and 40 41 manuscript, critically revised the manuscript. 42 43 44 45 46 WORD COUNT: 2,872 words, excluding title page, abstract, references, figures and tables 47 48 49 50 51 52 53 54 55 56 57 58 59 60
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1 2 3 ACKNOWLEDGEMENTS, COMPETING INTERESTS AND FUNDING 4 5 6 The authors declare that they have no competing interests or external funding. 7 8 Dr Zaman has been supported by a fellowship [101993] from the National Heart Foundation of 9 10 Australia. 11 12 13 14 DATA STATEMENT 15 16 17 Data is not publicly available but is available upon application to the Victorian Cardiac Outcomes 18 For peer review only 19 Registry. 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
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1 2 3 Table 1. Baseline and procedural characteristics according to sex 4 5 Total Male (n=8315) Female (n=2497) P- 6 7 (n=10,182) value 8 Age (years), median and IQR 63.0 (54.0, 71.0) 68.0 (59.0, 78.0) <0.001 9 10 BMI (kg/m2), median and IQR 28.1 (25.2, 31.2) 27.9 (24.3, 32.4) 0.21 11 12 Diabetes, n (%) 1586 (19.1%) 551 (22.1%) <0.001 13 14 Previous CABG, n (%) 570 (6.9%) 129 (5.2%) 0.003 15 Previous PCI, n (%) 1915 (23.0%) 466 (18.7%) <0.001 16 17 CVD, n (%) 236 (2.8%) 100 (4.0%) 0.003 18 For peer review only 19 PVD, n (%) 263 (3.2%) 79 (3.2%) 1.00 20 21 LVEF estimate 22 Normal (>50%) 3468 (57.3%) 1127 (62.8%) <0.001 23 24 Mild dysfunction (45-50%) 1589 (26.3%) 403 (22.5%) 25 26 Moderate dysfunction (35- 746 (12.3%) 200 (11.1%) 27 44%) 28 29 Severe dysfunction (<35%) 250 (4.1%) 65 (3.6%) 30 31 eGFR, median (IQR) 95.6 (73.3, 120.6) 75.9 (54.5, 101.7) <0.001 32 33 ACS type 34 Unstable angina 1256 (15.1%) 459 (18.4%) <0.001 35 36 NSTEMI 3942 (47.4%) 1235 (49.5%) 37 38 STEMI 3117 (37.5%) 803 (32.2%) 39 Cardiac arrest and/or pre- 388 (4.7%) 83 (3.3%) 0.004 40 41 procedure intubation 42 43 Radial access, n (%) 4194 (50.5%) 1027 (41.2%) <0.001 44 45 Lesion locations 46 Other 305 (3.7%) 67 (2.7%) 0.018 47 48 LAD 3401 (40.9%) 1081 (43.3%) 0.034 49 50 RCA 2918 (35.1%) 935 (37.4%) 0.031 51 LCX 2083 (25.1%) 511 (20.5%) <0.001 52 53 Drug eluting stent 6366 (76.6%) 1913 (76.6%) 0.96 54 55 PCI result successful 7898 (95.0%) 2401 (96.2%) 0.016 56 57 ACS, acute coronary syndrome; BMI, body mass index; BMS, bare metal stent; CABG, coronary artery 58 bypass grafting; CVD, cerebrovascular disease; DES, drug eluting stent; IQR, interquartile range; LAD, 59 60 left anterior descending; LCX, left circumflex; LVEF, left ventricular ejection fraction; NSTEMI, non-ST
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1 2 3 elevation myocardial infarction; PCI, percutaneous coronary intervention; PVD, peripheral vascular 4 5 disease, RCA, right coronary artery; STEMI, ST-elevation myocardial infarction 6 7 8 9 10 11 12 13 14 15 16 17 18 For peer review only 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
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1 2 3 Table 2. Baseline characteristics for participants who completed 30-day QoL versus participants who 4 5 did not complete QoL assessment 6 7 Factor QoL completed (n = 10812) QoL not completed (n = 5705) p- 8 value 9 10 Age, median (IQR) 64.0 (55.0, 73.0) 63.0 (54.0, 73.0) <0.001 11 12 Male 8315 (76.9%) 4354 (76.3%) 0.40 13 14 15 Body mass index, 28.0 (25.1, 31.5) 27.8 (24.8, 31.2) 0.003 16 17 median (IQR) 18 For peer review only 19 Diabetes 2137 (19.8%) 1206 (21.1%) 0.037 20 medication 21 22 Previous CABG 699 (6.5%) 309 (5.4%) 0.007 23 24 Previous PCI 2381 (22.0%) 1060 (18.6%) <0.001 25 26 Cerebrovascular 336 (3.1%) 206 (3.6%) 0.084 27 disease history 28 29 Peripheral vascular 342 (3.2%) 187 (3.3%) 0.69 30 31 disease 32 LVEF estimate 33 34 Normal (>50%) 4595 (58.5%) 2551 (54.3%) <0.001 35 36 Mild dysfunction 1992 (25.4%) 1280 (27.3%) 37 (45-50%) 38 39 Moderate 946 (12.1%) 632 (13.5%) 40 41 dysfunction (35- 42 44%) 43 44 Severe 315 (4.0%) 233 (5.0%) 45 46 dysfunction 47 48 (<35%) 49 eGFR, median 91.2 (68.0, 117.0) 90.7 (67.2, 118.3) 0.45 50 51 (IQR) 52 53 54 Unstable angina 1715 (15.9%) 800 (14.0%) 55 56 NSTEMI 5177 (47.9%) 2445 (42.9%) 57 58 STEMI 3920 (36.3%) 2460 (43.1%) 59 60 Cardiogenic shock 218 (2.0%) 176 (3.1%) <0.001
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1 2 3 Out-of-hospital 275 (2.5%) 176 (3.1%) 0.042 4 5 cardiac arrest 6 7 Cardiac arrest or 471 (4.4%) 316 (5.5%) <0.001 8 pre-procedure 9 10 intubation 11 12 Radial access 5221 (48.4%) 2917 (51.2%) <0.001 13 14 PCI result 10299 (95.3%) 5410 (94.8%) 0.23 15 successful 16 17 In-hospital shock 115 (1.1%) 89 (1.6%) 0.008 18 For peer review only 19 In-hospital 80 (0.7%) 35 (0.6%) 0.38 20 myocardial 21 22 infarction 23 24 New renal 247 (2.8%) 162 (3.0%) 0.32 25 impairment 26 27 Stent thrombosis 36 (0.3%) 12 (0.2%) 0.17 28 29 (definite/probable) 30 In-hospital major 104 (1.0%) 65 (1.1%) 0.29 31 32 bleed 33 34 In-hospital stroke 28 (0.3%) 21 (0.4%) 0.23 35 36 37 Cardiac rehab 8272 (86.1%) 4503 (89.7%) <0.001 38 39 referral 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
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1 2 3 Table 3. 30-day Quality of Life (QoL) post ACS according to sex 4 5 Factor Total (n=10812) Male (n = 8315) Female (n = Odds Ratio p- 6 7 2497) (95% CI) value 8 QOL Mobility 9 10 No problem 9455 (87.7%) 7463 (89.9%) 1992 (80.1%) 2.38 (2.06- <0.001 11 12 2.75) 13 14 Some problem 1287 (11.9%) 808 (9.7%) 479 (19.3%) 15 Unable to walk around 44 (0.4%) 27 (0.3%) 17 (0.7%) 16 17 QOL Personal care 18 For peer review only 19 No problem 10245 (95.0%) 7956 (95.9%) 2289 (91.9%) 2.14 (1.73- <0.001 20 2.66) 21 22 Some problem 492 (4.6%) 309 (3.7%) 183 (7.3%) 23 24 Unable to wash/dress 52 (0.5%) 34 (0.4%) 18 (0.7%) 25 26 self 27 QOL Usual activity 28 29 No problem 8700 (80.7%) 6889 (83.0%) 1811 (72.8%) 1.84 (1.63- <0.001 30 31 2.08) 32 Some problem 1900 (17.6%) 1280 (15.4%) 620 (24.9%) 33 34 Unable to perform usual 187 (1.7%) 129 (1.6%) 58 (2.3%) 35 36 activities 37 38 QOL Pain/discomfort 39 No pain/discomfort 9305 (86.6%) 7242 (87.6%) 2063 (83.1%) 1.44 (1.24- <0.001 40 41 1.67) 42 43 Mod pain/discomfort 1395 (13.0%) 995 (12.0%) 400 (16.1%) 44 Extreme pain/discomfort 50 (0.5%) 31 (0.4%) 19 (0.8%) 45 46 QOL Anxiety/depression 47 48 No anxiety/depression 7827 (80.8%) 6132 (82.3%) 1695 (76.0%) 1.49 (1.30- <0.001 49 1.70) 50 51 Mod anxiety/depression 1704 (17.6%) 1226 (16.5%) 478 (21.4%) 52 53 Extreme 150 (1.5%) 94 (1.3%) 56 (2.5%) 54 55 anxiety/depression 7463 (89.9%) 56 57 58 QOL EQ-5D Index Score, 1.0 (0.8, 1.0) 1.0 (0.8, 1.0) 1.0 (0.8, 1.0) <0.001 59 60 median (IQR)
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1 2 3 QOL Own health state 80.0 (70.0, 90.0) 80.0 (70.0, 90.0) 80.0 (60.0, <0.001 4 5 today, median (IQR) 85.0) 6 7 *Index available in 58.7% of males and 57.8% of females. 8 9 10 11 12 13 14 15 16 17 18 For peer review only 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
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1 2 3 4 5 Table 4. Multivariate predictors of poor QoL at 30-days following ACS, based on EQ-5D index score 6 7 N=10812 8 OR 95% CI p-value 9 10 Age (for every ten year increase) 0.94 0.90 - 0.98 0.004 11 12 Female sex 0.57 0.50 - 0.64 <0.001 13 14 Diabetes 0.85 0.75 - 0.96 0.01 15 Previous CABG or PCI 0.88 0.78 - 0.99 0.03 16 17 CVD 0.58 0.43 - 0.78 <0.001 18 For peer review only 19 Out of hospital cardiac arrest 0.47 0.34 - 0.65 <0.001 20 LV EF > 44% 1.16 1.01 - 1.33 0.04 21 22 Renal failure 0.53 0.35 - 0.80 0.002 23 24 In-hospital cardiogenic shock 0.73 0.33 - 0.89 0.28 25 26 In-hospital major bleeding 0.54 0.41 - 1.29 0.02 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
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1 2 3 Table 5: Major adverse cardiovascular, cerebrovascular and bleeding events in patients who survived 4 5 to 30-days, according to sex 6 7 Characteristics All ACS STEMI 8 Male (n = Female (n p- Male (n = Female (n = p- 9 10 8315) =2497) value 3117) 803) value 11 12 Major 98 (1.2%) 36 (1.4%) 0.30 61 (2.0%) 22 (2.7%) 0.17 13 14 bleeding 15 Recurrent MI 104 (1.3%) 36 (1.4%) 0.48 47 (1.5%) 13 (1.6%) 0.87 16 17 Stroke 28 (0.3%) 10 (0.4%) 0.70 20 (0.6%) 7 (0.9%) 0.47 18 For peer review only 19 MACE 224 (2.7%) 74 (3.0%) 0.49 113 (3.6%) 31 (3.9%) 0.75 20 MACCE 251 (3.0%) 84 (3.4%) 0.39 132 (4.2%) 38 (4.7%) 0.56 21 22 Abbreviations: MI, myocardial infarction; CABG, coronary artery bypass grafting; MACE, major 23 24 adverse cardiovascular event; MACCE, major adverse cardiovascular or cerebrovascular event. 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
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1 2 3 References 4 5 6 1. Khan E, Brieger D, Amerena J, et al. Differences in management and outcomes for men and 7 women with ST-elevation myocardial infarction. Med J Aust. 2018;209(3):118-23.doi: 8 10.5694/mja17.01109 [Published Online First: 23 July 2018] 9 10 2. Chang WC, Kaul P, Westerhout CM, et al. Impact of sex on long-term mortality from acute 11 myocardial infarction vs unstable angina. Arch Intern Med. 2003;163(20):2476-84.doi: 12 10.1001/archinte.163.20.2476 [Published Online First: 12 November 2003] 13 14 3. Izadnegahdar M, Norris C, Kaul P, et al. Basis for Sex-Dependent Outcomes in Acute 15 16 Coronary Syndrome. Can J Cardiol. 2014;30(7):713-20.doi: 10.1016/j.cjca.2013.08.020 [Published 17 Online First: 24 January 2014] 18 For peer review only 19 4. Kwong E, Neuburger J, Petersen SE, et al. Using patient-reported outcome measures for 20 primary percutaneous coronary intervention. Open Heart. 2019;6(1):e000920.doi: 10.1136/openhrt- 21 2018-000920 [Published Online First: 16 February 2019] 22 23 5. Gencer B, Rodondi N, Auer R, et al. Health utility indexes in patients with acute coronary 24 syndromes. Open Heart. 2016;3(1):e000419.doi: 10.1136/openhrt-2016-000419 [Published Online 25 First: 3 June 2016] 26 27 6. Duenas M, Ramirez C, Arana R, et al. Gender differences and determinants of health related 28 29 quality of life in coronary patients: a follow-up study. BMC Cardiovasc Disord. 2011;11:24.doi: 30 10.1186/1471-2261-11-24 [Published Online First: 31 May 2011] 31 32 7. Pettersen KI, Reikvam A, Rollag A, et al. Understanding sex differences in health-related 33 quality of life following myocardial infarction. Int J Cardiol. 2008;130(3):449-56.doi: 34 10.1016/j.ijcard.2007.10.016 [Published Online First: 28 November 2008] 35 36 8. Norris CM, Spertus JA, Jensen L, et al. Sex and gender discrepancies in health-related quality 37 of life outcomes among patients with established coronary artery disease. Circ Cardiovasc Qual 38 Outcomes. 2008;1(2):123-30.doi: 10.1161/circoutcomes.108.793448 [Published Online First: 25 39 40 December 2009] 41 42 9. von Elm E, Altman DG, Egger M, et al. The Strengthening the Reporting of Observational 43 Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. Ann 44 Intern Med. 2007;147(8):573-7.doi: 10.7326/0003-4819-147-8-200710160-00010 [Published Online 45 First: 17 October 2007] 46 47 10. Stub D, Lefkovits J, Brennan AL, et al. The establishment of the Victorian Cardiac Outcomes 48 Registry (VCOR): monitoring and optimising outcomes for cardiac patients in Victoria. Heart, Lung 49 and Circulation. 2018;27(4):451-63.doi: 10.1016/j.hlc.2017.07.013 [Published Online First: 11 50 October 2017] 51 52 53 11. Lefkovits J, Brennan A, Dinh D, et al. The Victorian Cardiac Outcomes Registry Annual Report 54 2017. Monash University, SPHPM; 2018. Report No 5. 55 56 12. Herdman M, Gudex C, Lloyd A, et al. Development and preliminary testing of the new five- 57 level version of EQ-5D (EQ-5D-5L). Qual Life Res. 2011;20(10):1727-36.doi: 10.1007/s11136-011- 58 9903-x [Published Online First: 12 April 2011] 59 60
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1 2 3 13. Viney R, Norman R, King MT, et al. Time trade-off derived EQ-5D weights for Australia. Value 4 Health. 2011;14(6):928-36.doi: 10.1016/j.jval.2011.04.009 [Published Online First: 15 September 5 6 2011] 7 8 14. Leung Yinko SS, Pelletier R, Behlouli H, et al. Health-related quality of life in premature acute 9 coronary syndrome: does patient sex or gender really matter? J Am Heart Assoc. 2014;3(4).doi: 10 10.1161/jaha.114.000901 [Published Online First: 31 July 2014] 11 12 15. Schweikert B, Hunger M, Meisinger C, et al. Quality of life several years after myocardial 13 infarction: comparing the MONICA/KORA registry to the general population. Eur Heart J. 14 2009;30(4):436-43.doi: 10.1093/eurheartj/ehn509 [Published Online First: 21 November 2008] 15 16 16. Sullivan PW, Ghushchyan V. Preference-Based EQ-5D Index Scores for Chronic Conditions in 17 the United States. Med Decis Making. 2006;26(4):410-20.doi: 10.1177/0272989x06290495 18 For peer review only 19 [Published Online First: July 1 2006] 20 21 17. Vicent L, Martínez-Sellés M. Frailty and acute coronary syndrome: does gender matter? J 22 Geriatr Cardiol. 2019;16(2):138-44.doi: 10.11909/j.issn.1671-5411.2019.02.007 [Published Online 23 First: Feb 2019] 24 25 18. Pelletier R, Humphries KH, Shimony A, et al. Sex-related differences in access to care among 26 patients with premature acute coronary syndrome. Can Med Assoc J. 2014;186(7):497-504.doi: 27 10.1503/cmaj.131450 [Published Online First: 15 April 2014] 28 29 19. Jankowska-Polanska B, Uchmanowicz I, Dudek K, et al. Sex differences in the quality of life of 30 31 patients with acute coronary syndrome treated with percutaneous coronary intervention after a 3- 32 year follow-up. Patient Prefer Adherence. 2016;10:1279-87.doi: 10.2147/ppa.S106577 [Published 33 Online First: 9 August 2016] 34 35 20. Hess CN, Krucoff MW, Sheng S, et al. Comparison of quality-of-life measures after radial 36 versus femoral artery access for cardiac catheterization in women: Results of the Study of Access 37 Site for Enhancement of Percutaneous Coronary Intervention for Women quality-of-life substudy. 38 Am Heart J. 2015;170(2):371-9.doi: 10.1016/j.ahj.2015.04.024 [Published Online First: 25 August 39 2015] 40 41 21. Rocha JA, Allison TG, Santoalha JM, et al. Musculoskeletal complaints in cardiac 42 43 rehabilitation: Prevalence and impact on cardiovascular risk factor profile and functional and 44 psychosocial status. Rev Port Cardiol. 2015;34(2):117-23.doi: 10.1016/j.repc.2014.08.022 [Published 45 Online First: 2015/02/11] 46 47 22. McGrady A, McGinnis R, Badenhop D, et al. Effects of depression and anxiety on adherence 48 to cardiac rehabilitation. J Cardiopulm Rehabil Prev. 2009;29(6):358-64.doi: 49 10.1097/HCR.0b013e3181be7a8f [Published Online First: 2009/11/27] 50 51 23. Vedanthan R, Seligman B, Fuster V. Global perspective on acute coronary syndrome: a 52 burden on the young and poor. Circ Res. 2014;114(12):1959-75.doi: 10.1161/circresaha.114.302782 53 54 [Published Online First: 7 June 2014] 55 56 24. Sumner J, Harrison A, Doherty P. The effectiveness of modern cardiac rehabilitation: A 57 systematic review of recent observational studies in non-attenders versus attenders. PLoS One. 58 2017;12(5):e0177658-e.doi: 10.1371/journal.pone.0177658 [Published Online First: 12 May 2017] 59 60
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1 2 3 25. Valgimigli M, Frigoli E, Leonardi S, et al. Radial versus femoral access and bivalirudin versus 4 unfractionated heparin in invasively managed patients with acute coronary syndrome (MATRIX): 5 6 final 1-year results of a multicentre, randomised controlled trial. The Lancet. 2018;392(10150):835- 7 48.doi: 10.1016/S0140-6736(18)31714-8 [Published Online First: 25 August 2018] 8 9 26. Stevanović J, Pechlivanoglou P, Kampinga MA, et al. Multivariate meta-analysis of 10 preference-based quality of life values in coronary heart disease. PLoS One. 11 2016;11(3):e0152030.doi: 10.1371/journal.pone.0152030 [Published Online First: 24 March 2016] 12 13 14 15 16 17 18 For peer review only 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 For peer review only 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 Figure 1: Proportions of patients with moderate or severe impairment in each EQ-5D-3L domain, according 46 to sex 47 209x296mm (300 x 300 DPI) 48 49 50 51 52 53 54 55 56 57 58 59 60 For peer review only - http://bmjopen.bmj.com/site/about/guidelines.xhtml Page 27 of 28 BMJ Open
1 STROBE Statement—Checklist of items that should be included in reports of cohort studies 2 3 4 Item Page 5 No Recommendation No 6 Title and abstract 1 (a) Indicate the study’s design with a commonly used term in the title or the 2 7 abstract 8 2 9 (b) Provide in the abstract an informative and balanced summary of what was 10 done and what was found 11 Introduction 12 4 13 Background/rationale 2 Explain the scientific background and rationale for the investigation being 14 reported 15 Objectives 3 State specific objectives, including any prespecified hypotheses 4 16 17 Methods 18 Study design For4 Presentpeer key elements review of study design onlyearly in the paper 4 19 Setting 5 Describe the setting, locations, and relevant dates, including periods of 4-5 20 recruitment, exposure, follow-up, and data collection 21 22 Participants 6 (a) Give the eligibility criteria, and the sources and methods of selection of 5 23 participants. Describe methods of follow-up 24 (b) For matched studies, give matching criteria and number of exposed and N/A 25 26 unexposed 27 Variables 7 Clearly define all outcomes, exposures, predictors, potential confounders, and 6 28 effect modifiers. Give diagnostic criteria, if applicable 29 Data sources/ 8* For each variable of interest, give sources of data and details of methods of 6 30 31 measurement assessment (measurement). Describe comparability of assessment methods if 32 there is more than one group 33 Bias 9 Describe any efforts to address potential sources of bias 7 34 Study size 10 Explain how the study size was arrived at 6 35 36 Quantitative variables 11 Explain how quantitative variables were handled in the analyses. If applicable, 6 37 describe which groupings were chosen and why 38 Statistical methods 12 (a) Describe all statistical methods, including those used to control for 6 39 40 confounding 41 (b) Describe any methods used to examine subgroups and interactions 6 42 (c) Explain how missing data were addressed 7 43 (d) If applicable, explain how loss to follow-up was addressed 7 44 45 (e) Describe any sensitivity analyses 8-9 46 Results 47 7 48 Participants 13* (a) Report numbers of individuals at each stage of study—eg numbers potentially 49 eligible, examined for eligibility, confirmed eligible, included in the study, 50 completing follow-up, and analysed 51 (b) Give reasons for non-participation at each stage 7 52 N/A 53 (c) Consider use of a flow diagram 54 Descriptive data 14* (a) Give characteristics of study participants (eg demographic, clinical, social) 7 55 and information on exposures and potential confounders 56 (b) Indicate number of participants with missing data for each variable of interest 7 57 58 (c) Summarise follow-up time (eg, average and total amount) 6 59 Outcome data 15* Report numbers of outcome events or summary measures over time 8-9 60
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1 Main results 16 (a) Give unadjusted estimates and, if applicable, confounder-adjusted estimates and their 13 2 precision (eg, 95% confidence interval). Make clear which confounders were adjusted for 3 4 and why they were included 5 (b) Report category boundaries when continuous variables were categorized 13- 6 19 7 (c) If relevant, consider translating estimates of relative risk into absolute risk for a N/A 8 meaningful time period 9 Other analyses 17 Report other analyses done—eg analyses of subgroups and interactions, and sensitivity 8-9 10 11 analyses 12 Discussion 13 Key results 18 Summarise key results with reference to study objectives 9 14 15 Limitations 19 Discuss limitations of the study, taking into account sources of potential bias or imprecision. 11- 12 16 Discuss both direction and magnitude of any potential bias 17 Interpretation 20 Give a cautious overall interpretation of results considering objectives, limitations, 12 18 For peer review only 19 multiplicity of analyses, results from similar studies, and other relevant evidence 20 Generalisability 21 Discuss the generalisability (external validity) of the study results 11- 21 12 22 Other information 23 Funding 22 Give the source of funding and the role of the funders for the present study and, if 14 24 25 applicable, for the original study on which the present article is based 26 27 *Give information separately for exposed and unexposed groups. 28 29 30 Note: An Explanation and Elaboration article discusses each checklist item and gives methodological background and 31 published examples of transparent reporting. The STROBE checklist is best used in conjunction with this article (freely 32 available on the Web sites of PLoS Medicine at http://www.plosmedicine.org/, Annals of Internal Medicine at 33 http://www.annals.org/, and Epidemiology at http://www.epidem.com/). Information on the STROBE Initiative is 34 35 available at http://www.strobe-statement.org. 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
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Does Sex Predict Quality of Life after Acute Coronary Syndromes: a State-Wide, Multi-Centre Prospective Cohort Study ForJournal: peerBMJ Open review only Manuscript ID bmjopen-2019-034034.R1
Article Type: Original research
Date Submitted by the 04-Nov-2019 Author:
Complete List of Authors: Koh, Youlin; Alfred Hospital, Cardiology Stehli, Julia; Alfred Hospital, Cardiology; Monash University Faculty of Medicine Nursing and Health Sciences Martin, Catherine; Monash University, Department of Epidemiology and Preventative Medicine Brennan, Angela; Monash University, Monash Cardiovascular Research Centre Dinh, Diem; Monash University, Monash Cardiovascular Research Centre Lefkovits, Jeffrey; Royal Melbourne Hospital, Cardiology Department; Monash University, Monash Cardiovascular Research Centre Zaman, Sarah; Monash University, Monash Cardiovascular Research Centre; Monash Health, Monash Heart
Primary Subject Cardiovascular medicine Heading:
Secondary Subject Heading: Patient-centred medicine
Sex, Quality of life, Acute coronary syndrome, Mood disorders, Mobility Keywords: limitation
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1 2 3 4 1 Does Sex Predict Quality of Life after Acute Coronary Syndromes: a 5 6 7 2 State-Wide, Multi-Centre Prospective Cohort Study 8 9 10 3 Youlin Koh (MBBS) 1, Julia Stehli (MD) 1,2 Catherine Martin (PhD) 3, Angela Brennan (CCRN) 4, Diem 11 12 4 T. Dinh (PhD) 4, Jeffrey Lefkovits (MBBS) 4, 5, Sarah Zaman (MBBS, PhD) 4, 6 13 14 5 15 16 6 1 Cardiology Department, The Alfred Hospital, Melbourne, Australia 17 18 For peer review only 19 7 2 Department of Medicine, Nursing and Health Sciences, Monash University, Melbourne, Australia 20 21 8 3 Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, Australia 22 23 9 4 Monash Cardiovascular Research Centre, Monash University, Melbourne, Australia 24 25 10 5 Cardiology Department, Royal Melbourne Hospital, Melbourne, Australia 26 27 28 11 6 Monash Heart, Monash Medical Centre, Melbourne, Australia 29 30 12 31 32 13 Corresponding author details: 33 34 14 Dr Sarah Zaman 35 36 37 15 Monash Heart, Monash Medical Centre 38 39 16 246 Clayton Road, Clayton 40 41 17 Melbourne, Victoria, Australia, 3168 42 43 18 Email: [email protected] 44 45 46 19 Telephone: +613 9594 6666 47 48 20 Fax:+613 9594 6475 49 50 21 51 52 53 22 54 55 56 57 58 59 60
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1 2 3 1 ABSTRACT 4 5 2 6 Objective: Women have reported higher mortality and major adverse cardiovascular events (MACE) 7 8 3 following acute coronary syndromes (ACS) compared to men. With this in mind, we aimed to identify 9 10 4 predictors of poor quality of life (QoL) post ACS as our primary outcome. We also examined 11 12 5 predictors of MACE, major cerebrovascular events and major bleeding as our secondary outcome. 13 14 6 15 16 17 7 Methods: Patients treated with percutaneous coronary intervention (PCI) for ACS were 18 For peer review only 19 8 prospectively recruited from 30 centres over 4 years as part of the Victorian Cardiac Outcomes 20 21 9 Registry (VCOR) in this prospective cohort study. QoL was assessed using the EuroQol-5D (EQ-5D-3L) 22 23 10 instrument by telephone at 30 days. Univariate and multivariate logistic regression analyses were 24 25 26 11 performed to identify independent predictors of QoL. 27 28 12 29 30 13 Results: 16,517 patients underwent PCI for ACS (22.9% females). Women were significantly older 31 32 14 (median 68.7 years, IQR 59.0-78.0 vs 62.0 years, IQR 54.0-71.0) with more diabetes, cerebrovascular 33 34 35 15 disease and renal failure. At 30-days 2,497 (64.7% females) completed the QoL EQ-5D-3L instrument. 36 37 16 Regarding the primary outcome, female sex was independently associated with moderate/severe 38 39 17 impairment in all EQ-5D-3L domains including mobility (OR 2.38, 95% CI: 2.06-2.75, p<0.001), 40 41 18 personal care (OR 2.14, 95% CI 1.73-2.66, p<0.001), activities of daily living (OR 1.84, 95% CI 1.63- 42 43 44 19 2.08, p<0.001) , pain/discomfort (OR 1.44, 95% CI 1.24-1.67, p<0.001), and anxiety/depression (OR 45 46 20 1.49, 95% CI 1.30-1.70, p<0.001). Women had significantly lower self-rated Visual Analogue Scale 47 48 21 (VAS) scores (75.0 vs 80.0, p <0.001). There was no significant difference between the sexes in 49 50 22 secondary outcomes. 51 52 23 53 54 55 24 Conclusions: Female sex was a predictor of poorer QoL following PCI for ACS including significantly 56 57 25 higher pain, anxiety and depression. This was independent of age, comorbidities and ACS 58 59 60
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1 2 3 1 presentation. There is a clinical need for a tailored approach in female ACS management, for 4 5 2 6 example, emphasis on management of depressive and anxiety symptoms. 7 8 3 9 10 4 ARTICLE SUMMARY 11 12 5 Strengths and limitations of this study 13 14 6 15 Consecutive patients were approached for recruitment with large numbers obtained 16 17 7 First of its kind in the Australian literature to examine the impact of sex on quality of life 18 For peer review only 19 8 following an acute coronary event. 20 21 9 Highlights the need for early screening of women for impaired mobility and reduced mood 22 23 24 10 following an acute coronary event. 25 26 11 Sex measured rather than gender role (wider social construct), which encourages us to 27 28 12 develop more accurate measures for gender for future studies. 29 30 13 Socio-economic status not measured in this study. 31 32 33 14 34 35 15 KEYWORDS 36 37 16 Quality of life; sex; acute coronary syndrome; mood disorders; mobility limitation 38 39 17 40 18 41 42 43 19 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
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1 2 3 1 INTRODUCTION 4 5 2 6 Numerous sex differences have been detected in the outcomes of patients with coronary artery 7 8 3 disease. Perhaps the most striking is that women who have suffered an acute coronary syndrome 9 10 4 (ACS) have higher mortality rates than men.[1-3] In the same vein, women receive significantly less 11 12 5 invasive angiography and timely revascularisation, compared to men.[1] Women with ACS have 13 14 6 more comorbidities, such as diabetes and cerebrovascular disease, and are on average 5-7 years 15 16 17 7 older, than men. However, these differences cannot fully account for the significant sex 18 For peer review only 19 8 discrepancies observed in mortality and morbidity following ACS. 20 21 9 22 23 10 This has prompted investigation into social factors such as sex differences in access to care and 24 25 26 11 patient-centred outcomes including symptoms, functional status or health-related quality of life 27 28 12 (QoL).[3-5] Among these patient-centred outcome data, women have been observed to report lower 29 30 13 health-related QoL, compared to men, [6-9] and also experience greater angina frequency. [10] This 31 32 14 may be partly explained by biological factors such as coronary anatomy and ejection fraction, but 33 34 35 15 social factors, such as lack of social support [11], play a key role. The current literature also identifies 36 37 16 young women (age <=55) at particular risk of increased anginal symptoms and lower QoL post ACS, 38 39 17 [12] a subgroup that may benefit from targeted intervention for depressive or anxiety symptoms. 40 41 18 42 43 44 19 Our prospective large cohort study is the first of its kind to examine differences in QoL post ACS 45 46 20 between the sexes in the Oceania region and aims to fill this gap in the global literature (as most 47 48 21 studies have been conducted in North America and Europe). Utilising data from the Australian 49 50 22 Victorian Cardiac Outcomes Registry (VCOR), we aimed to examine patient centred (primary) 51 52 23 53 outcomes and clinical (secondary) outcomes at 30 days post PCI, with sex as a predictor of these 54 55 24 outcomes in mind. 56 57 25 58 59 26 METHODS 60
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1 2 3 1 This study was carried out according to the principles of the STROBE (Strengthening the Reporting of 4 5 2 6 Observational Studies in Epidemiology) checklist for cohort studies.[13] 7 8 3 9 10 4 Data Source and patient population 11 12 5 VCOR is an Australian, state-based clinical quality registry designed to monitor the performance and 13 14 6 outcome of percutaneous coronary interventions (PCI) in Victoria. VCOR was established in 2012 and 15 16 17 7 is engaged at all 30 Victorian hospitals (13 public and 17 private).[14] VCOR prospectively collects 18 For peer review only 19 8 baseline demographic, procedural characteristics, in-hospital outcome and 30-day outcome data on 20 21 9 all patients who undergo PCI at a given facility through a secure web-based data collection system. 22 23 10 All data entry personnel are registered with VCOR and data is entered by trained hospital staff. Data 24 25 26 11 integrity is ensured with regular audit activities conducted by the central registry.[15] VCOR is 27 28 12 currently funded by the Victorian Department of Health and Human Services. Quality of life (QoL) 29 30 13 data is collected at 30 day by means of the EQ-5D-3L questionnaire. [16] Patients are contacted over 31 32 14 the phone at the time of routine post-PCI 30 day follow up and are invited to complete the QoL 33 34 35 15 questionnaire. This study was approved by the Monash Health Human Research Ethics Committee 36 37 16 with an opt-out consent model. 38 39 17 40 41 18 Patient and public involvement 42 43 44 19 No patient involvement was required or sought for this study. 45 46 20 47 48 21 Inclusion and exclusion criteria 49 50 22 The current study included consecutive patients who received successful or attempted PCI for an 51 52 23 53 ACS from 2013-2016 and who were alive at 30-days post PCI. Those with all EQ-5D-3L questions 54 55 24 missing and those who had died before 30 days were excluded from the quality of life analysis. ACS 56 57 25 included ST elevation MI (STEMI), non-ST elevation MI (NSTEMI) and unstable angina (UA). STEMI 58 59 26 was defined as elevated biomarkers and new or presumed new ST-segment elevation in two or more 60
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1 2 3 1 contiguous leads. NSTEMI was defined as presence of elevated biomarkers and at least one of either, 4 5 2 6 ECG changes (ST segment depression or T wave abnormalities), or ischaemic symptoms. UA was 7 8 3 defined as at least one of either, ECG changes (ST segment depression or T wave abnormalities), or 9 10 4 ischaemic symptoms in the absence of elevated biomarkers. Left ventricular ejection fraction (LVEF) 11 12 5 was collected during the index admission and four weeks post-MI for STEMI patients and within six 13 14 6 months prior to the index admission and four weeks post-discharge for NSTEMI patients. LVEF could 15 16 17 7 be assessed by echocardiogram, LV angiography, cardiac magnetic resonance imaging, or nuclear 18 For peer review only 19 8 imaging. Normal LVEF was defined as a LVEF>50%, mild dysfunction as a LVEF 45–50%, moderate 20 21 9 dysfunction as a LVEF 35-44% and severe dysfunction as LVEF<35%. 22 23 10 24 25 26 11 Primary and secondary outcomes 27 28 12 The primary endpoint was 30-day quality of life assessment using the EQ-5D-3L, an instrument for 29 30 13 describing and valuing health states. [16] The EQ-5D-3L is composed of five dimensions: mobility, 31 32 14 personal care, usual activities, pain/discomfort and anxiety/depression for which the patients can 33 34 35 15 answer with no, some or extreme problems (e.g. unable to walk, extreme depression/anxiety 36 37 16 symptoms). Patients are also asked to rank on a scale of 1-100, their overall health state at the time 38 39 17 of completing the questionnaire.[17] Patient responses are used to derive a composite EQ-5D-3L 40 41 18 index score, which was adjusted for in our analysis as per Australian values.[18] The index score was 42 43 44 19 categorized into 1, 0.5-0.9 and <0.5. An index score of 1 is consistent with perfect health. Scores of 45 46 20 0.5-0.9 reflect a lower health utility, and scores below 0.5 reflect lowest health utility. In addition to 47 48 21 the above, we asked patients to rate their own health state on the EQ-VAS (Visual Analogue Scale) 49 50 22 on a scale of 0-100, with higher scores reflecting better self-perception of current health state. 51 52 23 53 54 55 24 Secondary outcomes were 30-day major adverse cardiac events (MACE), major adverse cardiac and 56 57 25 cerebrovascular events (MACCE), and major bleeding. 30-day MACE was defined as new or recurrent 58 59 26 MI, stent thrombosis and target vessel revascularisation. 30-day MACCE was defined as MACE with 60
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1 2 3 1 the inclusion of stroke. Major bleeding was defined according to the international Bleeding 4 5 2 6 Academic Research Consortium (BARC) [19] and encompassed categories three and five which 7 8 3 included bleeding that required blood transfusion, cardiac tamponade, intracranial haemorrhage 9 10 4 and/or any fatal bleeding. 11 12 5 13 14 6 Statistical analysis 15 16 17 7 Patients in the PCI registry who did not complete the EQ-5L-3D questionnaire at routine 30-day 18 For peer review only 19 8 follow-up were excluded. Due to the large number of missing we chose to explore the missing to 20 21 9 determine the possible effect of the missing on the outcome. Excluded patients (missing data, n = 22 23 10 5705, 34.5% of eligible participants) were analysed against those who provided a complete response 24 25 26 11 (n = 10,812). The missing were also examined by sex to determine if the characteristics of the 27 28 12 missing were similar by sex. 29 30 13 31 32 14 For the included patients, categorical variables between men and women were analysed using Chi- 33 34 35 15 square tests or Fisher’s exact tests and expressed as numbers and percentages. Normally distributed 36 37 16 continuous variables were compared using an independent t-test and expressed as mean and 38 39 17 standard deviation, whereas skewed data was compared using Wilcoxon rank-sum and expressed as 40 41 18 median and interquartile range (IQR). Univariate ordered logistic regression was used to analyse the 42 43 44 19 EQ-3D-3L questionnaire, whereas univariate and multivariate ordered logistic regression was used to 45 46 20 analyse the EQ-3D-3L index category values. For the QOL questions an OR > 1 refers to a relative 47 48 21 change from a good QOL response towards a poorer QOL response, whereas an OR > 1 for the index 49 50 22 category refers to a relative change from a low (poor) index response towards a high (good) index 51 52 23 53 response. An OR < 1 for the QOL questions refers to a relative change from a poor QOL response 54 55 24 towards a good QOL response, whereas an OR < 1 for the index category refers to a relative change 56 57 25 from a high (good) index response towards a low (poor) index response. VAS scores were compared 58 59 60
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1 2 3 1 between the sexes as a numerical variable. A P value <0.05 was considered statistically significant for 4 5 2 6 all analyses. 7 8 3 9 10 4 To determine if QOL differs between male and females multivariate modelling was undertaken 11 12 5 where the association between QOL and sex was adjusted for age, diabetes medication, previous 13 14 6 CABG (coronary artery bypass grafting) and/or PCI, CVD (cerebrovascular disease), out of hospital 15 16 17 7 cardiac arrest, eGFR category, in-hospital cardiogenic shock, major bleed and ejection fraction 18 For peer review only 19 8 (categorised into a binary variable with cut-off at 44), with these potential confounders determined 20 21 9 a priori. Statistical analyses were performed using Stata version 14. 22 23 10 24 25 26 11 RESULTS 27 28 12 From a total of 17,132 consecutive ACS patients treated with PCI, 615 patients died by 30 days and 29 30 13 were excluded. A total of 16,517 patients were followed at 30-days post-PCI with exclusion of 5705 31 32 14 patients (1351 women, 4354 men) who did not complete the QoL assessment. As per above 33 34 35 15 methods, the characteristics of included versus excluded patients are shown in Table 1. From the 36 37 16 patients who completed the 30-day EQ-5D-3L questionnaire, 7,440 (58.7%) males and 2,223 (57.8%) 38 39 17 females fully completed it and another 875 (6.8%) males and 274 (6.9%) females partially it. 40 41 18 42 43 44 19 Baseline and procedural characteristics 45 46 20 The baseline and procedural characteristics and discharge medications are shown in Table 2. Women 47 48 21 were significantly older than men (median 68.7 years, interquartile range [IQR] 59.0-78.0 vs 62.0 49 50 22 years [IQR 54.0-71.0]) with more diabetes, history of cerebrovascular disease and renal impairment. 51 52 23 53 Men had a higher rate of previous coronary artery bypass grafting (CABG), previous PCI, impaired 54 55 24 LVEF and pre-hospital cardiac arrest. Women had higher rates of UA and NSTEMI, whereas men 56 57 25 presented with higher rates of STEMI. Radial vascular access was more commonly used in men than 58 59 26 women (p<0.001). The majority of coronary lesions were successfully treated, with most patients 60
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1 2 3 1 received drug-eluting stents. Women had a higher incidence of right coronary artery (RCA) and left 4 5 2 6 anterior descending (LAD) lesions, whereas men had more left main and circumflex (LCx) lesions. 7 8 3 9 10 4 Analysis of missing data 11 12 5 Patients with missing QOL data were found to present more acutely with slightly higher rates of 13 14 6 STEMI, cardiogenic shock, out-of-hospital cardiac arrest and intubation compared to included 15 16 17 7 patients. They also had higher rates of documented cerebrovascular disease suggesting more 18 For peer review only 19 8 widespread vasculopathy at baseline. However, excluded patients were discharged home at the 20 21 9 same rate as non-missing data patients, suggesting similar recovery to baseline. They also had higher 22 23 10 rates of cardiac rehabilitation referrals, perhaps reflecting recognition of their sicker baseline status. 24 25 26 11 More unwell patients may have had persistent low mood affecting their motivation to participate in 27 28 12 follow-up. There were similar proportions of excluded men and women. These results are 29 30 13 summarised in Table 1. 31 32 14 33 34 35 15 The missing data was then examined by sex. These results are summarised in Supplementary Table 36 37 16 1. Female patients were older, with a higher incidence of comorbidities including diabetes, previous 38 39 17 CABG/PCI, cerebrovascular disease and renal impairment. However, they had higher rates of 40 41 18 preserved left ventricular function. They presented with higher rates of NSTEMI and unstable angina 42 43 44 19 and were less likely to have severe presentation with cardiac arrest or require pre-procedural 45 46 20 intubation. However, they had slightly higher rates of in-hospital shock and were less likely to be 47 48 21 referred to cardiac rehabilitation. As the missing females were older and had increased levels of 49 50 22 chronic disease compared with the missing males, it is possible that the estimated difference in QOL 51 52 23 53 between males and females may be underestimated. 54 55 24 56 57 25 30-day Quality of Life Outcomes 58 59 60
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1 2 3 1 Thirty-day QoL results are presented in Table 3 and summarised graphically in Figure 1. The EQ-5D- 4 5 2 6 3L is divided into no, some or extreme problems. Responses of “some” and “extreme” problems 7 8 3 were both categorised as “presence of a problem” when reporting the following summary statistics. 9 10 4 To determine if the difference in QOL index score between male and females was consistent at 11 12 5 different ages an interaction term of age and sex was included in the multivariate model. There 13 14 6 appeared to be a narrowing in the difference in the QOL index score between male and females as 15 16 17 7 age increased, however this was not significant at the 5% level, p=0.08. 18 For peer review only 19 8 20 21 9 In each assessed domain, women were significantly more likely than men to report the presence of a 22 23 10 problem. Women reported significantly higher mobility problems compared to men (20.0% vs 9.4%). 24 25 26 11 Radial access was associated with fewer mobility problems (10.6% vs 13.6%, p<0.001), though this 27 28 12 effect was not significant in females. On the EQ-VAS, women’s ratings of their own health state were 29 30 13 significantly lower than that of men (75.0 vs 80.0, p <0.001), despite similar EQ-5D index scores. 31 32 14 33 34 35 15 Women reported significantly higher personal care issues compared to men (7.8% vs 3.8%,) and a 36 37 16 higher inability to perform usual activities (27.9% vs 17.2%). Women were significantly more likely to 38 39 17 report moderate pain or discomfort compared to men (16.6% vs 12.2%). They were also more likely 40 41 18 to experience moderate or severe symptoms of anxiety and depression, compared to men (24.1% vs 42 43 44 19 17.6%). 45 46 20 47 48 21 Women had significantly poorer mobility (OR 2.38, 95% CI 2.06-2.75, p<0.001) and higher personal 49 50 22 care issues (OR 2.14, 95% CI 2.06-2.75, p<0.001) compared to men, on univariate analysis. Usual 51 52 23 53 activities were affected to a lesser extent but still poorer in women compared to men (OR 1.84, 95% 54 55 24 CI 1.63-2.08, p<0.001), as were perceptions of bodily pain (OR 1.44, 95% CI 1.24-1.67, p<0.001) and 56 57 25 depression or anxiety (OR 1.49, 95% CI 1.30-1.70, p<0.001), respectively. 58 59 26 60
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1 2 3 1 The multivariate predictors of quality of life outcomes as assessed by the overall EQ-5D index score 4 5 2 6 are shown in Table 4. Female sex was a significant independent predictor of poorer QoL at 30 days 7 8 3 (OR 0.57,95% CI 0.50-0.64, p<0.001), along with increasing age, presence of diabetes, 9 10 4 cerebrovascular disease, left ventricular dysfunction, renal failure, previous CABG or PCI, and in- 11 12 5 hospital major bleeding. 13 14 6 15 16 17 7 Secondary endpoints 18 For peer review only 19 8 All secondary endpoints are shown in Table 5, after exclusion of the 615 patients who died before 30 20 21 9 days. Female sex was not associated with increased rates of major bleeding, recurrent myocardial 22 23 10 infarction, stroke, major adverse cardiovascular or combined cardiovascular and cerebrovascular 24 25 26 11 events in patients who survived to 30-days. 27 28 12 29 30 13 DISCUSSION 31 32 14 This study showed that female sex was an independent predictor of poorer QoL at thirty days 33 34 35 15 following an acute coronary event. Women were significantly more likely than men to rate their QoL 36 37 16 poorly in all domains of the EQ-5D-3L, including mobility, personal care, usual activities, pain, 38 39 17 discomfort, anxiety and depression, and had a poorer self-perception of their health state. 40 41 18 42 43 44 19 Comorbid conditions 45 46 20 The reasons for poorer overall QoL in women is likely multifactorial - influenced by both biological 47 48 21 sex and gender as a social construct. Our results add to previous research demonstrating a reduced 49 50 22 QoL in women following ACS in addition to their higher premorbid conditions and older age.[3, 8, 20, 51 52 23 53 21] The presence of a chronic condition such as CAD, cerebrovascular disease or renal failure is 54 55 24 known to predict poorer EQ-5D-3L scores following ACS.[20, 22] It is possible that women with ACS, 56 57 25 due to their older age and medical comorbidities are also more frail. We know that frailty in turn is 58 59 26 associated with poorer outcomes after ACS, but is difficult to measure and correct for.[23] Further, 60
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1 2 3 1 women had numerically, but not significantly higher rates of major bleeding, recurrent myocardial 4 5 2 6 infarction, stroke, or major cardiovascular and cerebrovascular events, compared to men. However, 7 8 3 this was after the exclusion of 30-day deaths, limiting conclusions drawn from these findings. 9 10 4 11 12 5 Physical health factors 13 14 6 We identified greater issues with mobility, personal care and usual activities in women compared to 15 16 17 7 men at 30 days based on EQ-5D-3L scores. These findings are consistent with previous research and 18 For peer review only 19 8 may be explained by an increased prevalence of angina amongst women [8, 20]. They are also likely 20 21 9 influenced by other factors, including the observed increased prevalence of diabetes and older age 22 23 10 in women with ACS.[24, 25] We found that radial access was associated with improved mobility QoL 24 25 26 11 scores, although this effect was not statistically significant in women, potentially due to smaller 27 28 12 numbers. In support of this, the literature has shown that women undergoing radial access preferred 29 30 13 it for future interventions over those who underwent femoral access.[26] 31 32 14 33 34 35 15 Our study demonstrated that women reported higher levels of pain and discomfort following ACS. 36 37 16 Ongoing pain is an issue as this tends to occur in older women, is associated with higher 38 39 17 cardiometabolic risk, and negatively impacts participation in cardiac rehabilitation. [27] 40 41 18 Further, women showed increased symptoms of anxiety and depression following ACS. Higher 42 43 44 19 baseline rates of depression and anxiety are also associated with lower rates of cardiac rehabilitation 45 46 20 attendance.[28] Mental health diagnoses disproportionately affect younger women post ACS and 47 48 21 are associated with poorer health related QoL.[21] Our results add to the literature in stable CAD, 49 50 22 where women have been found to report more depressive symptoms, with similar adverse QoL 51 52 23 53 effects.[8] 54 55 24 56 57 25 How can we improve QoL in women? 58 59 60
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1 2 3 1 The lower health-related quality of life in women following an ACS invariably leads to loss of 4 5 2 6 productive years and subsequent increased cost to our economy.[29] It highlights the need for 7 8 3 strategies to improve female patients’ QoL. Increasing uptake of cardiac rehabilitation has been 9 10 4 shown to have a positive impact on QoL.[30] We know that women are significantly less likely to be 11 12 5 referred to, and complete cardiac rehab, compared to men.[1] Development of female-specific 13 14 6 cardiac rehab programs may be one way to address this, along with approaches that target 15 16 17 7 symptoms of depression and anxiety by providing psychological counselling and support. Women 18 For peer review only 19 8 continue to receive less radial access use compared to males, an important finding given that 20 21 9 femoral access has been associated with increased complications, particularly major bleeding.[31] 22 23 10 Ensuring a radial-first approach in women may be another way to combat poorer QoL. As 24 25 26 11 demonstrated by our older study cohort, frailty also needs to be considered in the presentation of 27 28 12 female ACS, with an emphasis on a multidisciplinary approach involving not only cardiology, but also 29 30 13 endocrinology, geriatrics and allied health. 31 32 14 33 34 35 15 LIMITATIONS 36 37 16 Our study is limited by the high proportion of missing data. Because of the large number of missing 38 39 17 30-day QOL we chose to explore the effect of missing rather than employing imputation methods, 40 41 18 which all have associated biases. The strength of the data is that important characteristics were 42 43 44 19 available for comparison by missing/non missing status and the missing status characteristics by sex. 45 46 20 The missing analysis suggested that there may be a difference in missing by sex, however as the 47 48 21 missing females were older and had more chronic disease than the missing males, we would expect 49 50 22 that the conclusion (females have worse QOL than males) would not change. 51 52 23 53 54 55 24 Measures of QoL are heterogenous, both within and between instruments with consequent 56 57 25 limitations. However, the EQ-5D-3L is widely prevalent (even though cut-offs differ between 58 59 26 countries), and is preference-based, also making it suitable in cost-utility analysis.[32] The EQ-5D-3L 60
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1 2 3 1 does not capture the specific reasons for limited mobility which could be from a variety of reasons— 4 5 2 6 for example, shortness of breath, which may be cardiac-related or limitations from musculoskeletal 7 8 3 disability, which may be related to non-cardiac causes. 9 10 4 11 12 5 Our study examines outcomes by patient sex, but does not explore gender in detail, which is part of 13 14 6 the wider social construct comprising norms, roles and relationships in society [33]. This encourages 15 16 17 7 us to seek and develop instruments that measure this in an accurate manner. This is important as 18 For peer review only 19 8 various aspects of female gender can be associated with poorer outcomes and QoL after an ACS, 20 21 9 including restricted access to health care and increased anxiety.[20, 24] Our registry does not 22 23 10 routinely capture socio-economic data such as marital status and income; this would be useful as the 24 25 26 11 literature has identified that respondents of surveys tend to be female, older and more affluent,[4] 27 28 12 and that socio-economic factors independently influence QoL measurements.[22]However, its 29 30 13 strengths lie in the recruitment of consecutive patents undergoing PCI for ACS with all patients 31 32 14 approached for QoL assessment. 33 34 35 15 36 37 16 CONCLUSIONS 38 39 17 This is the first Australian study to show a significant sex-related difference in quality of life in a large 40 41 18 registry of patients treated with PCI for ACS. The lower health-related qualify of life in women 42 43 44 19 following an ACS is likely to result in loss of productive years with high economic impact. It highlights 45 46 20 the ongoing need to improve outcomes in our female cardiac patients. 47 48 21 49 50 22 51 52 23 53 54 55 24 56 57 25 58 59 26 60
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1 2 3 1 AUTHOR CONTRIBUTIONS 4 5 2 Youlin Koh: helped design the research questions, analysed the data, wrote, edited and refined the 6 7 3 manuscript. 8 9 10 4 Julia Stehli: designed and conceived the study, helped draft the results and manuscript, critically 11 12 5 revised the manuscript. 13 14 6 Catherine Martin: analysed the data, provided statistical analysis and expertise, critically revised the 15 16 7 manuscript. 17 18 For peer review only 19 8 Angela Brennan: managed the study, contributed to the study design, oversaw data analysis, 20 21 9 critically revised the manuscript 22 23 10 Diem T Dinh: analysed the data, provided statistical analysis and expertise, critically revised the 24 25 11 manuscript. 26 27 12 28 Jeffrey Lefkovits: designed and conceived the study, oversaw data analysis, critically revised the 29 30 13 manuscript. 31 32 14 Sarah Zaman: designed and conceived the study, oversaw data analysis, helped draft the results and 33 34 15 manuscript, critically revised the manuscript. 35 36 16 37 38 39 17 WORD COUNT: 3,301 words, excluding title page, abstract, references, figures and tables 40 41 18 42 43 19 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
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1 2 3 1 ACKNOWLEDGEMENTS 4 5 2 6 The authors declare that they have no competing interests.. 7 8 3 9 10 4 COMPETING INTERESTS 11 12 5 Dr Zaman has been supported by a fellowship [101993] from the National Heart Foundation of 13 14 6 Australia. 15 16 17 7 18 For peer review only 19 8 FUNDING 20 21 9 The authors declare that they have no external funding. 22 23 10 24 25 26 11 DATA STATEMENT 27 28 12 Data is not publicly available but is available upon application to the Victorian Cardiac Outcomes 29 30 13 Registry. 31 32 14 33 34 15 35 36 16 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
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1 2 3 1 Table 1. Baseline characteristics for participants who completed 30-day QoL versus participants who 4 5 2 did not complete QoL assessment 6 7 Factor QoL completed (n = 10812) QoL not completed (n = 5705) p- 8 value 9 10 Age, median (IQR) 64.0 (55.0, 73.0) 63.0 (54.0, 73.0) <0.001 11 12 Male 8315 (76.9%) 4354 (76.3%) 0.40 13 14 15 Body mass index, 28.0 (25.1, 31.5) 27.8 (24.8, 31.2) 0.003 16 17 median (IQR) 18 For peer review only 19 Diabetes medication 2137 (19.8%) 1206 (21.1%) 0.037 20 Previous CABG 699 (6.5%) 309 (5.4%) 0.007 21 22 Previous PCI 2381 (22.0%) 1060 (18.6%) <0.001 23 24 Cerebrovascular 336 (3.1%) 206 (3.6%) 0.084 25 26 disease history 27 Peripheral vascular 342 (3.2%) 187 (3.3%) 0.69 28 29 disease 30 31 LVEF estimate 32 Normal (>50%) 4595 (58.5%) 2551 (54.3%) <0.001 33 34 Mild dysfunction 1992 (25.4%) 1280 (27.3%) 35 36 (45-50%) 37 Moderate 946 (12.1%) 632 (13.5%) 38 39 dysfunction (35-44%) 40 41 Severe dysfunction 315 (4.0%) 233 (5.0%) 42 43 (<35%) 44 eGFR, median (IQR) 91.2 (68.0, 117.0) 90.7 (67.2, 118.3) 0.45 45 46 Acute coronary syndrome type 47 48 Unstable angina 1715 (15.9%) 800 (14.0%) 49 NSTEMI 5177 (47.9%) 2445 (42.9%) 50 51 STEMI 3920 (36.3%) 2460 (43.1%) 52 53 Cardiogenic shock 218 (2.0%) 176 (3.1%) <0.001 54 55 Out-of-hospital 275 (2.5%) 176 (3.1%) 0.042 56 cardiac arrest 57 58 59 60
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1 2 3 Cardiac arrest or pre- 471 (4.4%) 316 (5.5%) <0.001 4 5 procedure 6 7 intubation 8 Radial access 5221 (48.4%) 2917 (51.2%) <0.001 9 10 PCI result successful 10299 (95.3%) 5410 (94.8%) 0.23 11 12 In-hospital shock 115 (1.1%) 89 (1.6%) 0.008 13 14 In-hospital 80 (0.7%) 35 (0.6%) 0.38 15 myocardial infarction 16 17 New renal 247 (2.8%) 162 (3.0%) 0.32 18 For peer review only 19 impairment 20 Stent thrombosis 36 (0.3%) 12 (0.2%) 0.17 21 22 (definite/probable) 23 24 In-hospital major 104 (1.0%) 65 (1.1%) 0.29 25 bleed 26 27 In-hospital stroke 28 (0.3%) 21 (0.4%) 0.23 28 29 Cardiac rehab 8272 (86.1%) 4503 (89.7%) <0.001 30 31 referral 32 1 33 34 2 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
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1 2 3 1 Table 2. Baseline and procedural characteristics according to sex 4 5 Total Men (n=8315) Women (n=2497) P- 6 7 (n=10,812) value 8 Age (years), median and IQR 63.0 (54.0, 71.0) 68.0 (59.0, 78.0) <0.001 9 10 BMI (kg/m2), median and IQR 28.1 (25.2, 31.2) 27.9 (24.3, 32.4) 0.21 11 12 Diabetes, n (%) 1586 (19.1%) 551 (22.1%) <0.001 13 14 Previous CABG, n (%) 570 (6.9%) 129 (5.2%) 0.003 15 Previous PCI, n (%) 1915 (23.0%) 466 (18.7%) <0.001 16 17 CVD, n (%) 236 (2.8%) 100 (4.0%) 0.003 18 For peer review only 19 PVD, n (%) 263 (3.2%) 79 (3.2%) 1.00 20 21 LVEF estimate 22 Normal (>50%) 3468 (57.3%) 1127 (62.8%) <0.001 23 24 Mild dysfunction (45-50%) 1589 (26.3%) 403 (22.5%) 25 26 Moderate dysfunction (35- 746 (12.3%) 200 (11.1%) 27 44%) 28 29 Severe dysfunction (<35%) 250 (4.1%) 65 (3.6%) 30 31 eGFR, median (IQR) 95.6 (73.3, 120.6) 75.9 (54.5, 101.7) <0.001 32 33 ACS type 34 Unstable angina 1256 (15.1%) 459 (18.4%) <0.001 35 36 NSTEMI 3942 (47.4%) 1235 (49.5%) 37 38 STEMI 3117 (37.5%) 803 (32.2%) 39 Cardiac arrest and/or pre- 388 (4.7%) 83 (3.3%) 0.004 40 41 procedure intubation 42 43 Radial access, n (%) 4194 (50.5%) 1027 (41.2%) <0.001 44 45 Lesion locations 46 Other 305 (3.7%) 67 (2.7%) 0.018 47 48 LAD 3401 (40.9%) 1081 (43.3%) 0.034 49 50 RCA 2918 (35.1%) 935 (37.4%) 0.031 51 LCX 2083 (25.1%) 511 (20.5%) <0.001 52 53 Drug eluting stent 6366 (76.6%) 1913 (76.6%) 0.96 54 55 PCI result successful 7898 (95.0%) 2401 (96.2%) 0.016 56 57 2 ACS, acute coronary syndrome; BMI, body mass index; BMS, bare metal stent; CABG, coronary artery 58 3 bypass grafting; CVD, cerebrovascular disease; DES, drug eluting stent; IQR, interquartile range; LAD, 59 60 4 left anterior descending; LCX, left circumflex; LVEF, left ventricular ejection fraction; NSTEMI, non-ST
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1 2 3 1 elevation myocardial infarction; PCI, percutaneous coronary intervention; PVD, peripheral vascular 4 5 2 disease, RCA, right coronary artery; STEMI, ST-elevation myocardial infarction 6 7 3 8 9 10 11 12 13 14 15 16 17 18 For peer review only 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
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1 2 3 1 Table 3. 30-day Quality of Life (QoL) post ACS according to sex 4 5 Factor Total (n=10812) Men (n = 8315) Women (n = Odds Ratio p- 6 7 2497) (95% CI) value 8 QOL Mobility 9 10 No problem 9455 (87.7%) 7463 (89.9%) 1992 (80.1%) 2.38 (2.06- <0.001 11 12 2.75) 13 14 Some problem 1287 (11.9%) 808 (9.7%) 479 (19.3%) 15 Unable to walk around 44 (0.4%) 27 (0.3%) 17 (0.7%) 16 17 QOL Personal care 18 For peer review only 19 No problem 10245 (95.0%) 7956 (95.9%) 2289 (91.9%) 2.14 (1.73- <0.001 20 2.66) 21 22 Some problem 492 (4.6%) 309 (3.7%) 183 (7.3%) 23 24 Unable to wash/dress 52 (0.5%) 34 (0.4%) 18 (0.7%) 25 26 self 27 QOL Usual activity 28 29 No problem 8700 (80.7%) 6889 (83.0%) 1811 (72.8%) 1.84 (1.63- <0.001 30 31 2.08) 32 Some problem 1900 (17.6%) 1280 (15.4%) 620 (24.9%) 33 34 Unable to perform usual 187 (1.7%) 129 (1.6%) 58 (2.3%) 35 36 activities 37 38 QOL Pain/discomfort 39 No pain/discomfort 9305 (86.6%) 7242 (87.6%) 2063 (83.1%) 1.44 (1.24- <0.001 40 41 1.67) 42 43 Mod pain/discomfort 1395 (13.0%) 995 (12.0%) 400 (16.1%) 44 Extreme pain/discomfort 50 (0.5%) 31 (0.4%) 19 (0.8%) 45 46 QOL Anxiety/depression 47 48 No anxiety/depression 7827 (80.8%) 6132 (82.3%) 1695 (76.0%) 1.49 (1.30- <0.001 49 1.70) 50 51 Mod anxiety/depression 1704 (17.6%) 1226 (16.5%) 478 (21.4%) 52 53 Extreme 150 (1.5%) 94 (1.3%) 56 (2.5%) 54 55 anxiety/depression 7463 (89.9%) 56 57 58 QOL EQ-5D Index Score, 1.0 (0.8, 1.0) 1.0 (0.8, 1.0) 1.0 (0.8, 1.0) <0.001 59 60 median (IQR)
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1 2 3 QOL Own health state 80.0 (70.0, 90.0) 80.0 (70.0, 90.0) 80.0 (60.0, <0.001 4 5 today, median (IQR) 85.0) 6 7 1 *Index available in 58.7% of men and 57.8% of women. 8 2 9 10 11 12 13 14 15 16 17 18 For peer review only 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
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1 2 3 1 4 5 2 Table 4. Multivariate predictors of poor QoL at 30-days following ACS, based on EQ-5D index score 6 7 N=10812 8 OR 95% CI p-value 9 10 Age (for every ten year increase) 0.94 0.90 - 0.98 0.004 11 12 Female sex 0.57 0.50 - 0.64 <0.001 13 14 Diabetes 0.85 0.75 - 0.96 0.01 15 Previous CABG or PCI 0.88 0.78 - 0.99 0.03 16 17 CVD 0.58 0.43 - 0.78 <0.001 18 For peer review only 19 Out of hospital cardiac arrest 0.47 0.34 - 0.65 <0.001 20 LV EF > 44% 1.16 1.01 - 1.33 0.04 21 22 Renal failure 0.53 0.35 - 0.80 0.002 23 24 In-hospital cardiogenic shock 0.73 0.33 - 0.89 0.28 25 26 In-hospital major bleeding 0.54 0.41 - 1.29 0.02 27 3 28 29 4 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
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1 2 3 1 Table 5: Major adverse cardiovascular, cerebrovascular and bleeding events in patients who survived 4 5 2 to 30-days, according to sex 6 7 Characteristics All ACS STEMI 8 Men (n = Women (n p- Men (n = Women (n = p- 9 10 8315) =2497) value 3117) 803) value 11 12 Major 98 (1.2%) 36 (1.4%) 0.30 61 (2.0%) 22 (2.7%) 0.17 13 14 bleeding 15 Recurrent MI 104 (1.3%) 36 (1.4%) 0.48 47 (1.5%) 13 (1.6%) 0.87 16 17 Stroke 28 (0.3%) 10 (0.4%) 0.70 20 (0.6%) 7 (0.9%) 0.47 18 For peer review only 19 MACE 224 (2.7%) 74 (3.0%) 0.49 113 (3.6%) 31 (3.9%) 0.75 20 MACCE 251 (3.0%) 84 (3.4%) 0.39 132 (4.2%) 38 (4.7%) 0.56 21 22 3 Abbreviations: MI, myocardial infarction; CABG, coronary artery bypass grafting; MACE, major 23 24 4 adverse cardiovascular event; MACCE, major adverse cardiovascular or cerebrovascular event. 25 26 5 27 28 6 29 30 7 31 8 32 33 9 34 35 10 36 37 11 38 39 40 12 41 42 13 43 44 14 45 15 46 47 16 48 49 17 50 51 18 52 53 19 54 55 20 56 57 21 58 59 22 60
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1 2 3 1 Figure 1: Proportions of patients with moderate or severe impairment in each EQ-5D-3L domain, 4 5 2 6 according to sex 7 8 3 9 10 11 12 13 14 15 16 17 18 For peer review only 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
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1 2 3 1 References 4 2 5 6 3 1. Khan E, Brieger D, Amerena J, et al. Differences in management and outcomes for men and 7 8 4 women with ST-elevation myocardial infarction. Med J Aust. 2018;209(3):118-23.doi: 9 10 5 10.5694/mja17.01109 [Published Online First: 23 July 2018] 11 12 13 14 6 2. Chang WC, Kaul P, Westerhout CM, et al. Impact of sex on long-term mortality from acute 15 16 17 7 myocardial infarction vs unstable angina. Arch Intern Med. 2003;163(20):2476-84.doi: 18 For peer review only 19 8 10.1001/archinte.163.20.2476 [Published Online First: 12 November 2003] 20 21 22 23 9 3. Izadnegahdar M, Norris C, Kaul P, et al. Basis for Sex-Dependent Outcomes in Acute 24 25 10 Coronary Syndrome. Can J Cardiol. 2014;30(7):713-20.doi: 10.1016/j.cjca.2013.08.020 [Published 26 27 28 11 Online First: 24 January 2014] 29 30 31 32 12 4. Kwong E, Neuburger J, Petersen SE, et al. Using patient-reported outcome measures for 33 34 13 primary percutaneous coronary intervention. Open Heart. 2019;6(1):e000920.doi: 10.1136/openhrt- 35 36 14 2018-000920 [Published Online First: 16 February 2019] 37 38 39 40 41 15 5. Gencer B, Rodondi N, Auer R, et al. Health utility indexes in patients with acute coronary 42 43 16 syndromes. Open Heart. 2016;3(1):e000419.doi: 10.1136/openhrt-2016-000419 [Published Online 44 45 17 First: 3 June 2016] 46 47 48 49 18 6. Duenas M, Ramirez C, Arana R, et al. Gender differences and determinants of health related 50 51 19 quality of life in coronary patients: a follow-up study. BMC Cardiovasc Disord. 2011;11:24.doi: 52 53 54 20 10.1186/1471-2261-11-24 [Published Online First: 31 May 2011] 55 56 57 58 59 60
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1 2 3 1 7. Pettersen KI, Reikvam A, Rollag A, et al. Understanding sex differences in health-related 4 5 2 6 quality of life following myocardial infarction. Int J Cardiol. 2008;130(3):449-56.doi: 7 8 3 10.1016/j.ijcard.2007.10.016 [Published Online First: 28 November 2008] 9 10 11 12 4 8. Norris CM, Spertus JA, Jensen L, et al. Sex and gender discrepancies in health-related quality 13 14 5 of life outcomes among patients with established coronary artery disease. Circ Cardiovasc Qual 15 16 6 Outcomes. 2008;1(2):123-30.doi: 10.1161/circoutcomes.108.793448 [Published Online First: 25 17 18 For peer review only 19 7 December 2009] 20 21 22 23 8 9. Agewall S, Berglund M, Henareh L. Reduced quality of life after myocardial infarction in 24 25 9 women compared with men. Clin Cardiol. 2004;27(5):271-4.doi: 10.1002/clc.4960270506 [Published 26 27 10 Online First: 2004/06/11] 28 29 30 31 32 11 10. Garavalia LS, Decker C, Reid KJ, et al. Does health status differ between men and women in 33 34 12 early recovery after myocardial infarction? Journal of women's health (2002). 2007;16(1):93-101.doi: 35 36 13 10.1089/jwh.2006.M073 [Published Online First: 2007/02/28] 37 38 39 40 14 11. Emery CF, Frid DJ, Engebretson TO, et al. Gender differences in quality of life among cardiac 41 42 43 15 patients. Psychosom Med. 2004;66(2):190-7.doi: 10.1097/01.psy.0000116775.98593.f4 [Published 44 45 16 Online First: 2004/03/25] 46 47 48 49 17 12. Dreyer RP, Wang Y, Strait KM, et al. Gender differences in the trajectory of recovery in 50 51 18 health status among young patients with acute myocardial infarction: results from the variation in 52 53 54 19 recovery: role of gender on outcomes of young AMI patients (VIRGO) study. Circulation. 55 56 20 2015;131(22):1971-80.doi: 10.1161/circulationaha.114.014503 [Published Online First: 2015/04/12] 57 58 59 60
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1 2 3 1 13. von Elm E, Altman DG, Egger M, et al. The Strengthening the Reporting of Observational 4 5 2 6 Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. Ann 7 8 3 Intern Med. 2007;147(8):573-7.doi: 10.7326/0003-4819-147-8-200710160-00010 [Published Online 9 10 4 First: 17 October 2007] 11 12 13 14 5 14. Stub D, Lefkovits J, Brennan AL, et al. The establishment of the Victorian Cardiac Outcomes 15 16 6 Registry (VCOR): monitoring and optimising outcomes for cardiac patients in Victoria. Heart, Lung 17 18 For peer review only 19 7 and Circulation. 2018;27(4):451-63.doi: 10.1016/j.hlc.2017.07.013 [Published Online First: 11 20 21 8 October 2017] 22 23 24 25 9 15. Lefkovits J, Brennan A, Dinh D, et al. The Victorian Cardiac Outcomes Registry Annual Report 26 27 10 2017. Monash University, SPHPM; 2018. Report No 5. 28 29 30 31 32 11 16. Brooks R. EuroQol: the current state of play. Health Policy. 1996;37(1):53-72.doi: [Published 33 34 12 Online First: 1996/06/06] 35 36 37 38 13 17. Herdman M, Gudex C, Lloyd A, et al. Development and preliminary testing of the new five- 39 40 14 level version of EQ-5D (EQ-5D-5L). Qual Life Res. 2011;20(10):1727-36.doi: 10.1007/s11136-011- 41 42 43 15 9903-x [Published Online First: 12 April 2011] 44 45 46 47 16 18. Viney R, Norman R, King MT, et al. Time trade-off derived EQ-5D weights for Australia. Value 48 49 17 Health. 2011;14(6):928-36.doi: 10.1016/j.jval.2011.04.009 [Published Online First: 15 September 50 51 18 2011] 52 53 54 55 56 19 19. Mehran R, Rao SV, Bhatt DL, et al. Standardized Bleeding Definitions for Cardiovascular 57 58 20 Clinical Trials. Circulation. 2011;123(23):2736-47.doi: doi:10.1161/CIRCULATIONAHA.110.009449 59 60 21 [Published Online First:
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1 2 3 1 20. Leung Yinko SS, Pelletier R, Behlouli H, et al. Health-related quality of life in premature acute 4 5 2 6 coronary syndrome: does patient sex or gender really matter? J Am Heart Assoc. 2014;3(4).doi: 7 8 3 10.1161/jaha.114.000901 [Published Online First: 31 July 2014] 9 10 11 12 4 21. Schweikert B, Hunger M, Meisinger C, et al. Quality of life several years after myocardial 13 14 5 infarction: comparing the MONICA/KORA registry to the general population. Eur Heart J. 15 16 6 2009;30(4):436-43.doi: 10.1093/eurheartj/ehn509 [Published Online First: 21 November 2008] 17 18 For peer review only 19 20 21 7 22. Sullivan PW, Ghushchyan V. Preference-Based EQ-5D Index Scores for Chronic Conditions in 22 23 8 the United States. Med Decis Making. 2006;26(4):410-20.doi: 10.1177/0272989x06290495 24 25 9 [Published Online First: July 1 2006] 26 27 28 29 10 23. Vicent L, Martínez-Sellés M. Frailty and acute coronary syndrome: does gender matter? J 30 31 32 11 Geriatr Cardiol. 2019;16(2):138-44.doi: 10.11909/j.issn.1671-5411.2019.02.007 [Published Online 33 34 12 First: Feb 2019] 35 36 37 38 13 24. Pelletier R, Humphries KH, Shimony A, et al. Sex-related differences in access to care among 39 40 14 patients with premature acute coronary syndrome. Can Med Assoc J. 2014;186(7):497-504.doi: 41 42 43 15 10.1503/cmaj.131450 [Published Online First: 15 April 2014] 44 45 46 47 16 25. Jankowska-Polanska B, Uchmanowicz I, Dudek K, et al. Sex differences in the quality of life of 48 49 17 patients with acute coronary syndrome treated with percutaneous coronary intervention after a 3- 50 51 18 year follow-up. Patient Prefer Adherence. 2016;10:1279-87.doi: 10.2147/ppa.S106577 [Published 52 53 54 19 Online First: 9 August 2016] 55 56 57 58 20 26. Hess CN, Krucoff MW, Sheng S, et al. Comparison of quality-of-life measures after radial 59 60 21 versus femoral artery access for cardiac catheterization in women: Results of the Study of Access
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1 2 3 1 Site for Enhancement of Percutaneous Coronary Intervention for Women quality-of-life substudy. 4 5 2 6 Am Heart J. 2015;170(2):371-9.doi: 10.1016/j.ahj.2015.04.024 [Published Online First: 25 August 7 8 3 2015] 9 10 11 12 4 27. Rocha JA, Allison TG, Santoalha JM, et al. Musculoskeletal complaints in cardiac 13 14 5 rehabilitation: Prevalence and impact on cardiovascular risk factor profile and functional and 15 16 6 psychosocial status. Rev Port Cardiol. 2015;34(2):117-23.doi: 10.1016/j.repc.2014.08.022 [Published 17 18 For peer review only 19 7 Online First: 2015/02/11] 20 21 22 23 8 28. McGrady A, McGinnis R, Badenhop D, et al. Effects of depression and anxiety on adherence 24 25 9 to cardiac rehabilitation. J Cardiopulm Rehabil Prev. 2009;29(6):358-64.doi: 26 27 10 10.1097/HCR.0b013e3181be7a8f [Published Online First: 2009/11/27] 28 29 30 31 32 11 29. Vedanthan R, Seligman B, Fuster V. Global perspective on acute coronary syndrome: a 33 34 12 burden on the young and poor. Circ Res. 2014;114(12):1959-75.doi: 10.1161/circresaha.114.302782 35 36 13 [Published Online First: 7 June 2014] 37 38 39 40 14 30. Sumner J, Harrison A, Doherty P. The effectiveness of modern cardiac rehabilitation: A 41 42 43 15 systematic review of recent observational studies in non-attenders versus attenders. PLoS One. 44 45 16 2017;12(5):e0177658-e.doi: 10.1371/journal.pone.0177658 [Published Online First: 12 May 2017] 46 47 48 49 17 31. Valgimigli M, Frigoli E, Leonardi S, et al. Radial versus femoral access and bivalirudin versus 50 51 18 unfractionated heparin in invasively managed patients with acute coronary syndrome (MATRIX): 52 53 54 19 final 1-year results of a multicentre, randomised controlled trial. The Lancet. 2018;392(10150):835- 55 56 20 48.doi: 10.1016/S0140-6736(18)31714-8 [Published Online First: 25 August 2018] 57 58 59 60
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1 2 3 1 32. Stevanović J, Pechlivanoglou P, Kampinga MA, et al. Multivariate meta-analysis of 4 5 2 6 preference-based quality of life values in coronary heart disease. PLoS One. 7 8 3 2016;11(3):e0152030.doi: 10.1371/journal.pone.0152030 [Published Online First: 24 March 2016] 9 10 11 12 4 33. World Health Organization. Glossary of terms and tools Geneva, Switzerland: World Health 13 14 5 Organization; 2019 [Available from: https://www.who.int/gender-equity- 15 16 6 rights/knowledge/glossary/en/. 17 18 For peer review only 19 7 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 For peer review only 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 Figure 1: Proportions of patients with moderate or severe impairment in each EQ-5D-3L domain, according 46 to sex 47 209x296mm (300 x 300 DPI) 48 49 50 51 52 53 54 55 56 57 58 59 60 For peer review only - http://bmjopen.bmj.com/site/about/guidelines.xhtml Page 33 of 36 BMJ Open
1 2 3 Supplementary Table 1. Baseline characteristics for patients with missing QoL data by sex 4 Factor Male (n = 4354) Female (n = 1351) p-value 5 6 Age, median (IQR) 7 8 Male 61.0 (53.0, 71.0) 68.0 (58.0, 77.0) <0.001 9 10 Body mass index, median 27.8 (25.0, 31.0) 27.7 (24.0, 32.4) 0.89 11 (IQR) 12 13 Diabetes medication 877 (20.1%) 329 (24.4%) <0.001 14 15 Previous CABG 254 (5.8%) 55 (4.1%) 0.01 16 17 Previous PCI 831 (19.1%) 229 (17.0%) 0.08 18 Cerebrovascular diseaseFor peer144 (3.3%) review only62 (4.6%) 0.03 19 20 history 21 22 Peripheral vascular disease 139 (3.2%) 48 (3.6%) 0.52 23 LVEF estimate 0.03 24 25 Normal (>50%) 1923 (53.2%) 628 (58.1%) 26 27 Mild dysfunction (45-50%) 1017 (28.1%) 263 (24.4%) 28 29 Moderate dysfunction (35- 493 (13.6%) 139 (12.9%) 30 44%) 31 32 Severe dysfunction (<35%) 183 (5.1%) 50 (4.6%) 33 34 eGFR, median (IQR) 94.7 (72.6, 121.12) 75.5 (53.8, 103.5) <0.001 35 Acute coronary syndrome type <0.001 36 37 Unstable angina 574 (13.2%) 226 (16.7%) 38 39 NSTEMI 1826 (41.9%) 619 (45.8%) 40 41 STEMI 1954 (44.9%) 506 (37.5%) 42 Cardiogenic shock 130 (3.0%) 46 (3.4%) 0.44 43 44 Out-of-hospital cardiac arrest 158 (3.6^) 18 (1.3%) <0.001 45 46 Cardiac arrest or pre- 264 (6.1%) 52 (3.9%) 0.002 47 procedure intubation 48 49 Radial access 2308 (53.1%) 609 (45.1%) <0.001 50 51 PCI result successful 4144 (95.2%) 1266 (93.7%) 0.033 52 53 In-hospital shock 60 (1.4%) 29 (2.1%) 0.06 54 In-hospital myocardial 22 (0.5%) 13 (1.0%) 0.072 55 56 infarction 57 58 New renal impairment 111 (2.7%) 51 (4.0%) 0.02 59 60
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1 2 3 Stent thrombosis 6 (0.1%) 6 (0.4%) 0.04 4 5 (definite/probable) 6 7 In-hospital major bleed 40 (0.9%) 25 (1.9%) 0.008 8 In-hospital stroke 18 (0.4%) 3 (0.2%) 0.44 9 10 Cardiac rehab referral 3478 (90.4%) 1025 (87.5%) 0.006 11 12 13 14 15 16 17 18 For peer review only 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
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1 STROBE Statement—Checklist of items that should be included in reports of cohort studies 2 3 4 Item Page No 5 No Recommendation 6 Title and abstract 1 (a) Indicate the study’s design with a commonly used term in the title Pg 1 (title) 7 or the abstract 8 Pg 2 to pg 3 line 2 9 (b) Provide in the abstract an informative and balanced summary of 10 what was done and what was found 11 Introduction 12 Pg 4, lines 2-24 13 Background/rationale 2 Explain the scientific background and rationale for the investigation 14 being reported 15 Objectives 3 State specific objectives, including any prespecified hypotheses Pg 4, lines 21-24 16 17 Methods 18 Study design For4 Present peer key elements review of study design early only in the paper Pg 5, lines 5-16 19 Setting 5 Describe the setting, locations, and relevant dates, including periods of Pg 5, lines 5-24 20 recruitment, exposure, follow-up, and data collection 21 22 Participants 6 (a) Give the eligibility criteria, and the sources and methods of Eligibility, Pg 5, line 21 to pg 6, 23 selection of participants. Describe methods of follow-up line 9. Follow-up 24 pg 5, lines 12-16 25 N/A 26 (b) For matched studies, give matching criteria and number of exposed 27 and unexposed 28 Variables 7 Clearly define all outcomes, exposures, predictors, potential Outcomes pg 6 29 line 11-pg 7 line 4 confounders, and effect modifiers. Give diagnostic criteria, if 30 Predictors—pg 8 31 applicable line 20-pg 9 line 32 2, Table 1 pg 17- 33 18, 34 Supplementary 35 Table 1 36 Exposures and 37 diagnostic 38 criteria—pg 5 line 39 24-pg 6 line 9 40 Data sources/ 8* For each variable of interest, give sources of data and details of Sources of data pg 5 lines 4-16 41 measurement methods of assessment (measurement). Describe comparability of Comparability pg 42 assessment methods if there is more than one group 6 line 11 to pg 7 43 line 4 44 Bias 9 Describe any efforts to address potential sources of bias Missing vs no 45 missing data 46 comparison pg 7 47 lines 7-12 and pg 48 9 line 5-23 49 Study size 10 Explain how the study size was arrived at Pg 5, lines 22-23 50 Pg 7, line 6-pg 8, 51 Quantitative 11 Explain how quantitative variables were handled in the analyses. If line 17; pg 9 lines 52 variables applicable, describe which groupings were chosen and why 4-23 53 Pg 7, line 6-pg 8, 54 Statistical methods 12 (a) Describe all statistical methods, including those used to control for line 9 55 confounding 56 (b) Describe any methods used to examine subgroups and interactions Pg 7, line 14 to 57 pg8, line 9 58 (c) Explain how missing data were addressed Pg 7 lines 7-12 59 and pg 9 line 5-23 60 (d) If applicable, explain how loss to follow-up was addressed Pg 7 lines 7-12 and pg 9 line 5-23 (e) Describe any sensitivity analyses Pg 11, lines 1-11 1 For peer review only - http://bmjopen.bmj.com/site/about/guidelines.xhtml BMJ Open Page 36 of 36
1 Results 2 Pg 7, lines 7-9 3 Participants 13* (a) Report numbers of individuals at each stage of study—eg numbers 4 potentially eligible, examined for eligibility, confirmed eligible, 5 included in the study, completing follow-up, and analysed 6 (b) Give reasons for non-participation at each stage Pg 7, lines 7-9 7 N/A 8 (c) Consider use of a flow diagram 9 Descriptive data 14* (a) Give characteristics of study participants (eg demographic, clinical, Pg 8 line 20 to pg 9 line 2, Table 1 10 social) and information on exposures and potential confounders 11 pg 17-18 12 (b) Indicate number of participants with missing data for each variable Pg9, lines 5-23, Table 2, Pg 19-20, 13 of interest 14 Supplementary 15 Table 1 16 (c) Summarise follow-up time (eg, average and total amount) Pg 5, lines 12-16 17 Outcome data 15* Report numbers of outcome events or summary measures over time Pg 9, line 1 to Pg 18 For peer review only 11, line 11 19 20 *Give information separately for exposed and unexposed groups. 21 22 23 Note: An Explanation and Elaboration article discusses each checklist item and gives methodological background and 24 published examples of transparent reporting. The STROBE checklist is best used in conjunction with this article (freely 25 available on the Web sites of PLoS Medicine at http://www.plosmedicine.org/, Annals of Internal Medicine at 26 27 http://www.annals.org/, and Epidemiology at http://www.epidem.com/). Information on the STROBE Initiative is 28 available at http://www.strobe-statement.org. 29 30 31 32 33 Main results 16 (a) Give unadjusted estimates and, if applicable, confounder-adjusted estimates Pg 10, line 1-pg 11, line 11 34 and their precision (eg, 95% confidence interval). Make clear which 35 confounders were adjusted for and why they were included 36 37 (b) Report category boundaries when continuous variables were categorized Pg 6, line 18-22, pg 8, lines 7-8, 38 Table 1 (eGFR 39 and LVEF 40 category) 41 N/A 42 (c) If relevant, consider translating estimates of relative risk into absolute risk 43 for a meaningful time period 44 Other analyses 17 Report other analyses done—eg analyses of subgroups and interactions, and Pg 10, lines 5-7, 45 pg 11, lines 1-11 sensitivity analyses 46 47 Discussion 48 Key results 18 Summarise key results with reference to study objectives Pg 11, lines 13-17 49 Limitations 19 Discuss limitations of the study, taking into account sources of potential bias or Pg 13, line 15 to 50 pg 14, line 14 51 imprecision. Discuss both direction and magnitude of any potential bias 52 Interpretation 20 Give a cautious overall interpretation of results considering objectives, Pg 12 line 25 to pg 13 line 13 53 limitations, multiplicity of analyses, results from similar studies, and other 54 55 relevant evidence 56 Generalisability 21 Discuss the generalisability (external validity) of the study results Pg 12, lines 15-23 57 Other information 58 Pg 16, line 2 59 Funding 22 Give the source of funding and the role of the funders for the present study and, 60 if applicable, for the original study on which the present article is based
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Does Sex Predict Quality of Life after Acute Coronary Syndromes: an Australian, State-Wide, Multi-Centre Prospective Cohort Study ForJournal: peerBMJ Open review only Manuscript ID bmjopen-2019-034034.R2
Article Type: Original research
Date Submitted by the 28-Nov-2019 Author:
Complete List of Authors: Koh, Youlin; Alfred Hospital, Cardiology Stehli, Julia; Alfred Hospital, Cardiology; Monash University Faculty of Medicine Nursing and Health Sciences Martin, Catherine; Monash University, Department of Epidemiology and Preventative Medicine Brennan, Angela; Monash University, Monash Cardiovascular Research Centre Dinh, Diem; Monash University, Monash Cardiovascular Research Centre Lefkovits, Jeffrey; Royal Melbourne Hospital, Cardiology Department; Monash University, Monash Cardiovascular Research Centre Zaman, Sarah; Monash University, Monash Cardiovascular Research Centre; Monash Health, Monash Heart
Primary Subject Cardiovascular medicine Heading:
Secondary Subject Heading: Patient-centred medicine
Sex, Quality of life, Acute coronary syndrome, Mood disorders, Mobility Keywords: limitation
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1 2 3 4 1 Does Sex Predict Quality of Life after Acute Coronary Syndromes: an 5 6 7 2 Australian, State-Wide, Multi-Centre Prospective Cohort Study 8 9 10 3 Youlin Koh (MBBS) 1, Julia Stehli (MD) 1,2 Catherine Martin (PhD) 3, Angela Brennan (CCRN) 4, Diem 11 12 4 T. Dinh (PhD) 4, Jeffrey Lefkovits (MBBS) 4, 5, Sarah Zaman (MBBS, PhD) 4, 6 13 14 5 15 16 6 1 Cardiology Department, The Alfred Hospital, Melbourne, Australia 17 18 For peer review only 19 7 2 Department of Medicine, Nursing and Health Sciences, Monash University, Melbourne, Australia 20 21 8 3 Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, Australia 22 23 9 4 Monash Cardiovascular Research Centre, Monash University, Melbourne, Australia 24 25 10 5 Cardiology Department, Royal Melbourne Hospital, Melbourne, Australia 26 27 28 11 6 Monash Heart, Monash Medical Centre, Melbourne, Australia 29 30 12 31 32 13 Corresponding author details: 33 34 14 Dr Sarah Zaman 35 36 37 15 Monash Heart, Monash Medical Centre 38 39 16 246 Clayton Road, Clayton 40 41 17 Melbourne, Victoria, Australia, 3168 42 43 18 Email: [email protected] 44 45 46 19 Telephone: +613 9594 6666 47 48 20 Fax:+613 9594 6475 49 50 21 51 52 53 22 54 55 56 57 58 59 60
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1 2 3 1 ABSTRACT 4 5 2 6 Objective: Women have reported higher mortality and major adverse cardiovascular events (MACE) 7 8 3 following acute coronary syndromes (ACS) compared to men. With this in mind, we aimed to identify 9 10 4 predictors of poor quality of life (QoL) post ACS as our primary outcome. We examined predictors of 11 12 5 MACE, major cerebrovascular events and major bleeding as our secondary outcome. 13 14 6 15 16 17 7 Design: Prospective cohort study. 18 For peer review only 19 8 20 21 9 Setting: 30 metropolitan centres across the Victorian Cardiac Outcomes Registry (VCOR) network. 22 23 10 24 25 26 11 Participants: 16,517 patients treated with percutaneous coronary intervention (PCI) for ACS (22.9% 27 28 12 females). Selection/inclusion criteria: consecutive patients with successful or attempted PCI for ACS 29 30 13 from 2013-2016, alive at 30-days post PCI. Exclusion criteria: patients not fulfilling ACS criteria. At 31 32 14 30-days 2,497 (64.7% females) completed the QoL EQ-5D-3L instrument. 33 34 35 15 36 37 16 Primary and secondary outcome measures: QoL, assessed using the EuroQol-5D (EQ-5D-3L) 38 39 17 instrument by telephone at 30 days. Independent predictors of QoL were identified by univariate 40 41 18 and multivariate logistic regression analyses. 42 43 44 19 45 46 20 Results: Women were significantly older with more diabetes, cerebrovascular disease and renal 47 48 21 failure. Regarding the primary outcome, female sex was independently associated with 49 50 22 moderate/severe impairment in all EQ-5D-3L domains including mobility (OR 2.38, 95% CI: 2.06-2.75, 51 52 23 53 p<0.001), personal care (OR 2.14, 95% CI 1.73-2.66, p<0.001), activities of daily living (OR 1.84, 95% 54 55 24 CI 1.63-2.08, p<0.001) , pain/discomfort (OR 1.44, 95% CI 1.24-1.67, p<0.001), and 56 57 25 anxiety/depression (OR 1.49, 95% CI 1.30-1.70, p<0.001). Women had significantly lower self-rated 58 59 60
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1 2 3 1 Visual Analogue Scale (VAS) scores (75.0 vs 80.0, p <0.001). There was no significant difference 4 5 2 6 between the sexes in secondary outcomes. 7 8 3 9 10 4 Conclusions: Female sex was a predictor of poorer QoL following PCI for ACS including significantly 11 12 5 higher pain, anxiety and depression. This was independent of age, comorbidities and ACS 13 14 6 presentation. There is a clinical need for a tailored approach in female ACS management, for 15 16 17 7 example, emphasis on management of depressive and anxiety symptoms. 18 For peer review only 19 8 20 21 9 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
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1 2 3 1 ARTICLE SUMMARY 4 5 2 6 Strengths and limitations of this study 7 8 3 Consecutive patients were approached for recruitment with large numbers obtained 9 10 4 First of its kind in the Australian literature to examine the impact of sex on quality of life 11 12 5 following an acute coronary event. 13 14 15 6 Highlights the need for early screening of women for impaired mobility and reduced mood 16 17 7 following an acute coronary event. 18 For peer review only 19 8 Sex measured rather than gender role (wider social construct), which encourages us to 20 21 9 develop more accurate measures for gender for future studies. 22 23 24 10 Socio-economic status not measured in this study. 25 26 11 27 28 12 KEYWORDS 29 30 13 Quality of life; sex; acute coronary syndrome; mood disorders; mobility limitation 31 32 33 14 34 15 35 36 16 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
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1 2 3 1 INTRODUCTION 4 5 2 6 Numerous sex differences have been detected in the outcomes of patients with coronary artery 7 8 3 disease. Perhaps the most striking is that women who have suffered an acute coronary syndrome 9 10 4 (ACS) have higher mortality rates than men.[1-3] In the same vein, women receive significantly less 11 12 5 invasive angiography and timely revascularisation, compared to men.[1] Women with ACS have 13 14 6 more comorbidities, such as diabetes and cerebrovascular disease, and are on average 5-7 years 15 16 17 7 older, than men. However, these differences cannot fully account for the significant sex 18 For peer review only 19 8 discrepancies observed in mortality and morbidity following ACS. 20 21 9 22 23 10 This has prompted investigation into social factors such as sex differences in access to care and 24 25 26 11 patient-centred outcomes including symptoms, functional status or health-related quality of life 27 28 12 (QoL).[3-5] Among these patient-centred outcome data, women have been observed to report lower 29 30 13 health-related QoL, compared to men, [6-9] and also experience greater angina frequency. [10] This 31 32 14 may be partly explained by biological factors such as coronary anatomy and ejection fraction, but 33 34 35 15 social factors, such as lack of social support [11], play a key role. The current literature also identifies 36 37 16 young women (age <=55) at particular risk of increased anginal symptoms and lower QoL post ACS, 38 39 17 [12] a subgroup that may benefit from targeted intervention for depressive or anxiety symptoms. 40 41 18 42 43 44 19 Our prospective large cohort study is the first of its kind to examine differences in QoL post ACS 45 46 20 between the sexes in the Oceania region and aims to fill this gap in the global literature (as most 47 48 21 studies have been conducted in North America and Europe). Utilising data from the Australian 49 50 22 Victorian Cardiac Outcomes Registry (VCOR), we aimed to examine patient centred (primary) 51 52 23 53 outcomes and clinical (secondary) outcomes at 30 days post PCI, with sex as a predictor of these 54 55 24 outcomes in mind. 56 57 25 58 59 26 METHODS 60
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1 2 3 1 This study was carried out according to the principles of the STROBE (Strengthening the Reporting of 4 5 2 6 Observational Studies in Epidemiology) checklist for cohort studies.[13] 7 8 3 9 10 4 Data Source and patient population 11 12 5 VCOR is an Australian, state-based clinical quality registry designed to monitor the performance and 13 14 6 outcome of percutaneous coronary interventions (PCI) in Victoria. VCOR was established in 2012 and 15 16 17 7 is engaged at all 30 Victorian hospitals (13 public and 17 private).[14] VCOR prospectively collects 18 For peer review only 19 8 baseline demographic, procedural characteristics, in-hospital outcome and 30-day outcome data on 20 21 9 all patients who undergo PCI at a given facility through a secure web-based data collection system. 22 23 10 All data entry personnel are registered with VCOR and data is entered by trained hospital staff. Data 24 25 26 11 integrity is ensured with regular audit activities conducted by the central registry.[15] VCOR is 27 28 12 currently funded by the Victorian Department of Health and Human Services. Quality of life (QoL) 29 30 13 data is collected at 30 day by means of the EQ-5D-3L questionnaire. [16] Patients are contacted over 31 32 14 the phone at the time of routine post-PCI 30 day follow up and are invited to complete the QoL 33 34 35 15 questionnaire. This study was approved by the Monash Health Human Research Ethics Committee 36 37 16 with an opt-out consent model. 38 39 17 40 41 18 Patient and public involvement 42 43 44 19 This research was performed without patient involvement. Patients were not invited to comment on 45 46 20 the study design and were not consulted to develop patient relevant outcomes or interpret the 47 48 21 results. Patients were not invited to contribute to the writing or editing of this document for 49 50 22 readability or accuracy. 51 52 23 53 Inclusion and exclusion criteria 54 55 24 The current study included consecutive patients who received successful or attempted PCI for an 56 57 25 ACS from 2013-2016 and who were alive at 30-days post PCI. Those with all EQ-5D-3L questions 58 59 26 missing and those who had died before 30 days were excluded from the quality of life analysis. ACS 60
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1 2 3 1 included ST elevation MI (STEMI), non-ST elevation MI (NSTEMI) and unstable angina (UA). STEMI 4 5 2 6 was defined as elevated biomarkers and new or presumed new ST-segment elevation in two or more 7 8 3 contiguous leads. NSTEMI was defined as presence of elevated biomarkers and at least one of either, 9 10 4 ECG changes (ST segment depression or T wave abnormalities), or ischaemic symptoms. UA was 11 12 5 defined as at least one of either, ECG changes (ST segment depression or T wave abnormalities), or 13 14 6 ischaemic symptoms in the absence of elevated biomarkers. Left ventricular ejection fraction (LVEF) 15 16 17 7 was collected during the index admission and four weeks post-MI for STEMI patients and within six 18 For peer review only 19 8 months prior to the index admission and four weeks post-discharge for NSTEMI patients. LVEF could 20 21 9 be assessed by echocardiogram, LV angiography, cardiac magnetic resonance imaging, or nuclear 22 23 10 imaging. Normal LVEF was defined as a LVEF>50%, mild dysfunction as a LVEF 45–50%, moderate 24 25 26 11 dysfunction as a LVEF 35-44% and severe dysfunction as LVEF<35%. 27 28 12 29 30 13 Primary and secondary outcomes 31 32 14 The primary endpoint was 30-day quality of life assessment using the EQ-5D-3L, an instrument for 33 34 35 15 describing and valuing health states. [16] The EQ-5D-3L is composed of five dimensions: mobility, 36 37 16 personal care, usual activities, pain/discomfort and anxiety/depression for which the patients can 38 39 17 answer with no, some or extreme problems (e.g. unable to walk, extreme depression/anxiety 40 41 18 symptoms). Patients are also asked to rank on a scale of 1-100, their overall health state at the time 42 43 44 19 of completing the questionnaire.[17] Patient responses are used to derive a composite EQ-5D-3L 45 46 20 index score, which was adjusted for in our analysis as per Australian values.[18] The index score was 47 48 21 categorized into 1, 0.5-0.9 and <0.5. An index score of 1 is consistent with perfect health. Scores of 49 50 22 0.5-0.9 reflect a lower health utility, and scores below 0.5 reflect lowest health utility. In addition to 51 52 23 53 the above, we asked patients to rate their own health state on the EQ-VAS (Visual Analogue Scale) 54 55 24 on a scale of 0-100, with higher scores reflecting better self-perception of current health state. 56 57 25 58 59 60
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1 2 3 1 Secondary outcomes were 30-day major adverse cardiac events (MACE), major adverse cardiac and 4 5 2 6 cerebrovascular events (MACCE), and major bleeding. 30-day MACE was defined as new or recurrent 7 8 3 MI, stent thrombosis and target vessel revascularisation. 30-day MACCE was defined as MACE with 9 10 4 the inclusion of stroke. Major bleeding was defined according to the international Bleeding 11 12 5 Academic Research Consortium (BARC) [19] and encompassed categories three and five which 13 14 6 included bleeding that required blood transfusion, cardiac tamponade, intracranial haemorrhage 15 16 17 7 and/or any fatal bleeding. 18 For peer review only 19 8 20 21 9 Statistical analysis 22 23 10 Patients in the PCI registry who did not complete the EQ-5L-3D questionnaire at routine 30-day 24 25 26 11 follow-up were excluded. Due to the large number of patients in the missing data group, we chose 27 28 12 to explore the missing data group’s key patient characteristics to determine the possible effect of 29 30 13 the distribution of these characteristics on the outcome. Excluded patients (missing data, n = 5705, 31 32 14 34.5% of eligible participants) were analysed against those who provided a complete response (n = 33 34 35 15 10,812). The missing data group were also examined by sex to determine if distribution of key 36 37 16 characteristics differed by sex. 38 39 17 40 41 18 For the included patients, categorical variables between men and women were analysed using Chi- 42 43 44 19 square tests or Fisher’s exact tests and expressed as numbers and percentages. Normally distributed 45 46 20 continuous variables were compared using an independent t-test and expressed as mean and 47 48 21 standard deviation, whereas skewed data was compared using Wilcoxon rank-sum and expressed as 49 50 22 median and interquartile range (IQR). Univariate ordered logistic regression was used to analyse the 51 52 23 53 EQ-3D-3L questionnaire, whereas univariate and multivariate ordered logistic regression was used to 54 55 24 analyse the EQ-3D-3L index category values. For the QOL questions an OR > 1 refers to a relative 56 57 25 change from a good QOL response towards a poorer QOL response, whereas an OR > 1 for the index 58 59 26 category refers to a relative change from a low (poor) index response towards a high (good) index 60
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1 2 3 1 response. An OR < 1 for the QOL questions refers to a relative change from a poor QOL response 4 5 2 6 towards a good QOL response, whereas an OR < 1 for the index category refers to a relative change 7 8 3 from a high (good) index response towards a low (poor) index response. VAS scores were compared 9 10 4 between the sexes as a numerical variable. A P value <0.05 was considered statistically significant for 11 12 5 all analyses. 13 14 6 15 16 17 7 To determine if QOL differs between male and females multivariate modelling was undertaken 18 For peer review only 19 8 where the association between QOL and sex was adjusted for age, diabetes medication, previous 20 21 9 CABG (coronary artery bypass grafting) and/or PCI, CVD (cerebrovascular disease), out of hospital 22 23 10 cardiac arrest, eGFR category, in-hospital cardiogenic shock, major bleed and ejection fraction 24 25 26 11 (categorised into a binary variable with cut-off at 44), with these potential confounders determined 27 28 12 a priori. Statistical analyses were performed using Stata version 14. 29 30 13 31 32 14 RESULTS 33 34 35 15 From a total of 17,132 consecutive ACS patients treated with PCI, 615 patients died by 30 days and 36 37 16 were excluded. A total of 16,517 patients were followed at 30-days post-PCI with exclusion of 5705 38 39 17 patients (1351 women, 4354 men) who did not complete the QoL assessment. As per above 40 41 18 methods, the characteristics of included versus excluded patients are shown in Table 1. From the 42 43 44 19 patients who completed the 30-day EQ-5D-3L questionnaire, 7,440 (58.7%) males and 2,223 (57.8%) 45 46 20 females fully completed it and another 875 (6.8%) males and 274 (6.9%) females partially it. 47 48 21 49 50 22 Baseline and procedural characteristics 51 52 23 53 The baseline and procedural characteristics and discharge medications are shown in Table 2. Women 54 55 24 were significantly older than men (median 68.7 years, interquartile range [IQR] 59.0-78.0 vs 62.0 56 57 25 years [IQR 54.0-71.0]) with more diabetes, history of cerebrovascular disease and renal impairment. 58 59 26 Men had a higher rate of previous coronary artery bypass grafting (CABG), previous PCI, impaired 60
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1 2 3 1 LVEF and pre-hospital cardiac arrest. Women had higher rates of UA and NSTEMI, whereas men 4 5 2 6 presented with higher rates of STEMI. Radial vascular access was more commonly used in men than 7 8 3 women (p<0.001). The majority of coronary lesions were successfully treated, with most patients 9 10 4 received drug-eluting stents. Women had a higher incidence of right coronary artery (RCA) and left 11 12 5 anterior descending (LAD) lesions, whereas men had more left main and circumflex (LCx) lesions. 13 14 6 15 16 17 7 Analysis of missing data 18 For peer review only 19 8 Patients with missing QOL data were found to present more acutely with slightly higher rates of 20 21 9 STEMI, cardiogenic shock, out-of-hospital cardiac arrest and intubation compared to included 22 23 10 patients. They also had higher rates of documented cerebrovascular disease suggesting more 24 25 26 11 widespread vasculopathy at baseline. However, excluded patients were discharged home at the 27 28 12 same rate as non-missing data patients, suggesting similar recovery to baseline. They also had higher 29 30 13 rates of cardiac rehabilitation referrals, perhaps reflecting recognition of their sicker baseline status. 31 32 14 More unwell patients may have had persistent low mood affecting their motivation to participate in 33 34 35 15 follow-up. There were similar proportions of excluded men and women. These results are 36 37 16 summarised in Table 1. 38 39 17 40 41 18 The missing data was then examined by sex. These results are summarised in Supplementary Table 42 43 44 19 1. Female patients were older, with a higher incidence of comorbidities including diabetes, previous 45 46 20 CABG/PCI, cerebrovascular disease and renal impairment. However, they had higher rates of 47 48 21 preserved left ventricular function. They presented with higher rates of NSTEMI and unstable angina 49 50 22 and were less likely to have severe presentation with cardiac arrest or require pre-procedural 51 52 23 53 intubation. However, they had slightly higher rates of in-hospital shock and were less likely to be 54 55 24 referred to cardiac rehabilitation. As the missing females were older and had increased levels of 56 57 25 chronic disease compared with the missing males, it is possible that the estimated difference in QOL 58 59 26 between males and females may be underestimated. 60
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1 2 3 1 4 5 2 6 30-day Quality of Life Outcomes 7 8 3 Thirty-day QoL results are presented in Table 3 and summarised graphically in Figure 1. The EQ-5D- 9 10 4 3L is divided into no, some or extreme problems. Responses of “some” and “extreme” problems 11 12 5 were both categorised as “presence of a problem” when reporting the following summary statistics. 13 14 6 To determine if the difference in QOL index score between male and females was consistent at 15 16 17 7 different ages an interaction term of age and sex was included in the multivariate model. There 18 For peer review only 19 8 appeared to be a narrowing in the difference in the QOL index score between male and females as 20 21 9 age increased, however this was not significant at the 5% level, p=0.08. 22 23 10 24 25 26 11 In each assessed domain, women were significantly more likely than men to report the presence of a 27 28 12 problem. Women reported significantly higher mobility problems compared to men (20.0% vs 9.4%). 29 30 13 Radial access was associated with fewer mobility problems (10.6% vs 13.6%, p<0.001), though this 31 32 14 effect was not significant in females. On the EQ-VAS, women’s ratings of their own health state were 33 34 35 15 significantly lower than that of men (75.0 vs 80.0, p <0.001), despite similar EQ-5D index scores. 36 37 16 38 39 17 Women reported significantly higher personal care issues compared to men (7.8% vs 3.8%,) and a 40 41 18 higher inability to perform usual activities (27.9% vs 17.2%). Women were significantly more likely to 42 43 44 19 report moderate pain or discomfort compared to men (16.6% vs 12.2%). They were also more likely 45 46 20 to experience moderate or severe symptoms of anxiety and depression, compared to men (24.1% vs 47 48 21 17.6%). 49 50 22 51 52 23 53 Women had significantly poorer mobility (OR 2.38, 95% CI 2.06-2.75, p<0.001) and higher personal 54 55 24 care issues (OR 2.14, 95% CI 2.06-2.75, p<0.001) compared to men, on univariate analysis. Usual 56 57 25 activities were affected to a lesser extent but still poorer in women compared to men (OR 1.84, 95% 58 59 60
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1 2 3 1 CI 1.63-2.08, p<0.001), as were perceptions of bodily pain (OR 1.44, 95% CI 1.24-1.67, p<0.001) and 4 5 2 6 depression or anxiety (OR 1.49, 95% CI 1.30-1.70, p<0.001), respectively. 7 8 3 9 10 4 The multivariate predictors of quality of life outcomes as assessed by the overall EQ-5D index score 11 12 5 are shown in Table 4. Female sex was a significant independent predictor of poorer QoL at 30 days 13 14 6 (OR 0.57,95% CI 0.50-0.64, p<0.001), along with increasing age, presence of diabetes, 15 16 17 7 cerebrovascular disease, left ventricular dysfunction, renal failure, previous CABG or PCI, and in- 18 For peer review only 19 8 hospital major bleeding. 20 21 9 22 23 10 Secondary endpoints 24 25 26 11 All secondary endpoints are shown in Table 5, after exclusion of the 615 patients who died before 30 27 28 12 days. Female sex was not associated with increased rates of major bleeding, recurrent myocardial 29 30 13 infarction, stroke, major adverse cardiovascular or combined cardiovascular and cerebrovascular 31 32 14 events in patients who survived to 30-days. 33 34 35 15 36 37 16 DISCUSSION 38 39 17 This study showed that female sex was an independent predictor of poorer QoL at thirty days 40 41 18 following an acute coronary event. Women were significantly more likely than men to rate their QoL 42 43 44 19 poorly in all domains of the EQ-5D-3L, including mobility, personal care, usual activities, pain, 45 46 20 discomfort, anxiety and depression, and had a poorer self-perception of their health state. 47 48 21 49 50 22 Comorbid conditions 51 52 23 53 The reasons for poorer overall QoL in women is likely multifactorial - influenced by both biological 54 55 24 sex and gender as a social construct. Our results add to previous research demonstrating a reduced 56 57 25 QoL in women following ACS in addition to their higher premorbid conditions and older age.[3, 8, 20, 58 59 26 21] The presence of a chronic condition such as CAD, cerebrovascular disease or renal failure is 60
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1 2 3 1 known to predict poorer EQ-5D-3L scores following ACS.[20, 22] It is possible that women with ACS, 4 5 2 6 due to their older age and medical comorbidities are also more frail. We know that frailty in turn is 7 8 3 associated with poorer outcomes after ACS, but is difficult to measure and correct for.[23] Further, 9 10 4 women had numerically, but not significantly higher rates of major bleeding, recurrent myocardial 11 12 5 infarction, stroke, or major cardiovascular and cerebrovascular events, compared to men. However, 13 14 6 this was after the exclusion of 30-day deaths, limiting conclusions drawn from these findings. 15 16 17 7 18 For peer review only 19 8 Physical health factors 20 21 9 We identified greater issues with mobility, personal care and usual activities in women compared to 22 23 10 men at 30 days based on EQ-5D-3L scores. These findings are consistent with previous research and 24 25 26 11 may be explained by an increased prevalence of angina amongst women [8, 20]. They are also likely 27 28 12 influenced by other factors, including the observed increased prevalence of diabetes and older age 29 30 13 in women with ACS.[24, 25] We found that radial access was associated with improved mobility QoL 31 32 14 scores, although this effect was not statistically significant in women, potentially due to smaller 33 34 35 15 numbers. In support of this, the literature has shown that women undergoing radial access preferred 36 37 16 it for future interventions over those who underwent femoral access.[26] 38 39 17 40 41 18 Our study demonstrated that women reported higher levels of pain and discomfort following ACS. 42 43 44 19 Ongoing pain is an issue as this tends to occur in older women, is associated with higher 45 46 20 cardiometabolic risk, and negatively impacts participation in cardiac rehabilitation. [27] 47 48 21 Further, women showed increased symptoms of anxiety and depression following ACS. Higher 49 50 22 baseline rates of depression and anxiety are also associated with lower rates of cardiac rehabilitation 51 52 23 53 attendance.[28] Mental health diagnoses disproportionately affect younger women post ACS and 54 55 24 are associated with poorer health related QoL.[21] Our results add to the literature in stable CAD, 56 57 25 where women have been found to report more depressive symptoms, with similar adverse QoL 58 59 26 effects.[8] 60
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1 2 3 1 4 5 2 6 How can we improve QoL in women? 7 8 3 The lower health-related quality of life in women following an ACS invariably leads to loss of 9 10 4 productive years and subsequent increased cost to our economy.[29] It highlights the need for 11 12 5 strategies to improve female patients’ QoL. Increasing uptake of cardiac rehabilitation has been 13 14 6 shown to have a positive impact on QoL.[30] We know that women are significantly less likely to be 15 16 17 7 referred to, and complete cardiac rehab, compared to men.[1] Development of female-specific 18 For peer review only 19 8 cardiac rehab programs may be one way to address this, along with approaches that target 20 21 9 symptoms of depression and anxiety by providing psychological counselling and support. Women 22 23 10 continue to receive less radial access use compared to males, an important finding given that 24 25 26 11 femoral access has been associated with increased complications, particularly major bleeding.[31] 27 28 12 Ensuring a radial-first approach in women may be another way to combat poorer QoL. As 29 30 13 demonstrated by our older study cohort, frailty also needs to be considered in the presentation of 31 32 14 female ACS, with an emphasis on a multidisciplinary approach involving not only cardiology, but also 33 34 35 15 endocrinology, geriatrics and allied health. 36 37 16 38 39 17 LIMITATIONS 40 41 18 Our study is limited by the high proportion of missing data. Because of the large number of missing 42 43 44 19 30-day QOL we chose to explore the effect of missing rather than employing imputation methods, 45 46 20 which all have associated biases. The strength of the data is that important characteristics were 47 48 21 available for comparison by missing/non missing status and the missing status characteristics by sex. 49 50 22 The missing analysis suggested that there may be a difference in missing by sex, however as the 51 52 23 53 missing females were older and had more chronic disease than the missing males, we would expect 54 55 24 that the conclusion (females have worse QOL than males) would not change. 56 57 25 58 59 60
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1 2 3 1 Measures of QoL are heterogenous, both within and between instruments with consequent 4 5 2 6 limitations. However, the EQ-5D-3L is widely prevalent (even though cut-offs differ between 7 8 3 countries), and is preference-based, also making it suitable in cost-utility analysis.[32] The EQ-5D-3L 9 10 4 does not capture the specific reasons for limited mobility which could be from a variety of reasons— 11 12 5 for example, shortness of breath, which may be cardiac-related or limitations from musculoskeletal 13 14 6 disability, which may be related to non-cardiac causes. 15 16 17 7 18 For peer review only 19 8 Our study examines outcomes by patient sex, but does not explore gender in detail, which is part of 20 21 9 the wider social construct comprising norms, roles and relationships in society [33]. This encourages 22 23 10 us to seek and develop instruments that measure this in an accurate manner. This is important as 24 25 26 11 various aspects of female gender can be associated with poorer outcomes and QoL after an ACS, 27 28 12 including restricted access to health care and increased anxiety.[20, 24] Our registry does not 29 30 13 routinely capture socio-economic data such as marital status and income; this would be useful as the 31 32 14 literature has identified that respondents of surveys tend to be female, older and more affluent,[4] 33 34 35 15 and that socio-economic factors independently influence QoL measurements.[22]However, its 36 37 16 strengths lie in the recruitment of consecutive patents undergoing PCI for ACS with all patients 38 39 17 approached for QoL assessment. 40 41 18 42 43 44 19 CONCLUSIONS 45 46 20 This is the first Australian study to show a significant sex-related difference in quality of life in a large 47 48 21 registry of patients treated with PCI for ACS. The lower health-related qualify of life in women 49 50 22 following an ACS is likely to result in loss of productive years with high economic impact. It highlights 51 52 23 53 the ongoing need to improve outcomes in our female cardiac patients. 54 55 24 56 57 25 58 59 26 60
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1 2 3 1 AUTHOR CONTRIBUTIONS 4 5 2 Youlin Koh: helped design the research questions, analysed the data, wrote, edited and refined the 6 7 3 manuscript. 8 9 10 4 Julia Stehli: designed and conceived the study, helped draft the results and manuscript, critically 11 12 5 revised the manuscript. 13 14 6 Catherine Martin: analysed the data, provided statistical analysis and expertise, critically revised the 15 16 7 manuscript. 17 18 For peer review only 19 8 Angela Brennan: managed the study, contributed to the study design, oversaw data analysis, 20 21 9 critically revised the manuscript 22 23 10 Diem T Dinh: analysed the data, provided statistical analysis and expertise, critically revised the 24 25 11 manuscript. 26 27 12 28 Jeffrey Lefkovits: designed and conceived the study, oversaw data analysis, critically revised the 29 30 13 manuscript. 31 32 14 Sarah Zaman: designed and conceived the study, oversaw data analysis, helped draft the results and 33 34 15 manuscript, critically revised the manuscript. 35 36 16 37 38 39 17 WORD COUNT: 3,417 words, excluding title page, abstract, references, figures and tables 40 41 18 42 43 19 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
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1 2 3 1 ACKNOWLEDGEMENTS 4 5 2 6 There are no further acknowledgements. 7 8 3 9 10 4 COMPETING INTERESTS 11 12 5 The authors declare no competing interests. 13 14 6 15 16 17 7 FUNDING 18 For peer review only 19 8 Dr Zaman has been supported by a fellowship [101993] from the National Heart Foundation of 20 21 9 Australia. 22 23 10 24 25 26 11 DATA STATEMENT 27 28 12 Data is not publicly available but is available upon application to the Victorian Cardiac Outcomes 29 30 13 Registry (http://www.vcor.org.au/Data-Access). 31 32 14 33 34 15 35 36 16 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
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1 2 3 1 Table 1. Baseline characteristics for participants who completed 30-day QoL versus participants who 4 5 2 did not complete QoL assessment 6 7 Factor QoL completed (n = 10812) QoL not completed (n = 5705) p- 8 value 9 10 Age, median (IQR) 64.0 (55.0, 73.0) 63.0 (54.0, 73.0) <0.001 11 12 Male 8315 (76.9%) 4354 (76.3%) 0.40 13 14 15 Body mass index, 28.0 (25.1, 31.5) 27.8 (24.8, 31.2) 0.003 16 17 median (IQR) 18 For peer review only 19 Diabetes medication 2137 (19.8%) 1206 (21.1%) 0.037 20 Previous CABG 699 (6.5%) 309 (5.4%) 0.007 21 22 Previous PCI 2381 (22.0%) 1060 (18.6%) <0.001 23 24 Cerebrovascular 336 (3.1%) 206 (3.6%) 0.084 25 26 disease history 27 Peripheral vascular 342 (3.2%) 187 (3.3%) 0.69 28 29 disease 30 31 LVEF estimate 32 Normal (>50%) 4595 (58.5%) 2551 (54.3%) <0.001 33 34 Mild dysfunction 1992 (25.4%) 1280 (27.3%) 35 36 (45-50%) 37 Moderate 946 (12.1%) 632 (13.5%) 38 39 dysfunction (35-44%) 40 41 Severe dysfunction 315 (4.0%) 233 (5.0%) 42 43 (<35%) 44 eGFR, median (IQR) 91.2 (68.0, 117.0) 90.7 (67.2, 118.3) 0.45 45 46 Acute coronary syndrome type 47 48 Unstable angina 1715 (15.9%) 800 (14.0%) 49 NSTEMI 5177 (47.9%) 2445 (42.9%) 50 51 STEMI 3920 (36.3%) 2460 (43.1%) 52 53 Cardiogenic shock 218 (2.0%) 176 (3.1%) <0.001 54 55 Out-of-hospital 275 (2.5%) 176 (3.1%) 0.042 56 cardiac arrest 57 58 59 60
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1 2 3 Cardiac arrest or pre- 471 (4.4%) 316 (5.5%) <0.001 4 5 procedure 6 7 intubation 8 Radial access 5221 (48.4%) 2917 (51.2%) <0.001 9 10 PCI result successful 10299 (95.3%) 5410 (94.8%) 0.23 11 12 In-hospital shock 115 (1.1%) 89 (1.6%) 0.008 13 14 In-hospital 80 (0.7%) 35 (0.6%) 0.38 15 myocardial infarction 16 17 New renal 247 (2.8%) 162 (3.0%) 0.32 18 For peer review only 19 impairment 20 Stent thrombosis 36 (0.3%) 12 (0.2%) 0.17 21 22 (definite/probable) 23 24 In-hospital major 104 (1.0%) 65 (1.1%) 0.29 25 bleed 26 27 In-hospital stroke 28 (0.3%) 21 (0.4%) 0.23 28 29 Cardiac rehab 8272 (86.1%) 4503 (89.7%) <0.001 30 31 referral 32 1 33 34 2 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
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1 2 3 1 Table 2. Baseline and procedural characteristics according to sex 4 5 Total Men (n=8315) Women (n=2497) P- 6 7 (n=10,812) value 8 Age (years), median and IQR 63.0 (54.0, 71.0) 68.0 (59.0, 78.0) <0.001 9 10 BMI (kg/m2), median and IQR 28.1 (25.2, 31.2) 27.9 (24.3, 32.4) 0.21 11 12 Diabetes, n (%) 1586 (19.1%) 551 (22.1%) <0.001 13 14 Previous CABG, n (%) 570 (6.9%) 129 (5.2%) 0.003 15 Previous PCI, n (%) 1915 (23.0%) 466 (18.7%) <0.001 16 17 CVD, n (%) 236 (2.8%) 100 (4.0%) 0.003 18 For peer review only 19 PVD, n (%) 263 (3.2%) 79 (3.2%) 1.00 20 21 LVEF estimate 22 Normal (>50%) 3468 (57.3%) 1127 (62.8%) <0.001 23 24 Mild dysfunction (45-50%) 1589 (26.3%) 403 (22.5%) 25 26 Moderate dysfunction (35- 746 (12.3%) 200 (11.1%) 27 44%) 28 29 Severe dysfunction (<35%) 250 (4.1%) 65 (3.6%) 30 31 eGFR, median (IQR) 95.6 (73.3, 120.6) 75.9 (54.5, 101.7) <0.001 32 33 ACS type 34 Unstable angina 1256 (15.1%) 459 (18.4%) <0.001 35 36 NSTEMI 3942 (47.4%) 1235 (49.5%) 37 38 STEMI 3117 (37.5%) 803 (32.2%) 39 Cardiac arrest and/or pre- 388 (4.7%) 83 (3.3%) 0.004 40 41 procedure intubation 42 43 Radial access, n (%) 4194 (50.5%) 1027 (41.2%) <0.001 44 45 Lesion locations 46 Other 305 (3.7%) 67 (2.7%) 0.018 47 48 LAD 3401 (40.9%) 1081 (43.3%) 0.034 49 50 RCA 2918 (35.1%) 935 (37.4%) 0.031 51 LCX 2083 (25.1%) 511 (20.5%) <0.001 52 53 Drug eluting stent 6366 (76.6%) 1913 (76.6%) 0.96 54 55 PCI result successful 7898 (95.0%) 2401 (96.2%) 0.016 56 57 2 ACS, acute coronary syndrome; BMI, body mass index; BMS, bare metal stent; CABG, coronary artery 58 3 bypass grafting; CVD, cerebrovascular disease; DES, drug eluting stent; IQR, interquartile range; LAD, 59 60 4 left anterior descending; LCX, left circumflex; LVEF, left ventricular ejection fraction; NSTEMI, non-ST
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1 2 3 1 elevation myocardial infarction; PCI, percutaneous coronary intervention; PVD, peripheral vascular 4 5 2 disease, RCA, right coronary artery; STEMI, ST-elevation myocardial infarction 6 7 3 8 9 10 11 12 13 14 15 16 17 18 For peer review only 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
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1 2 3 1 Table 3. 30-day Quality of Life (QoL) post ACS according to sex 4 5 Factor Total (n=10812) Men (n = 8315) Women (n = Odds Ratio p- 6 7 2497) (95% CI) value 8 QOL Mobility 9 10 No problem 9455 (87.7%) 7463 (89.9%) 1992 (80.1%) 2.38 (2.06- <0.001 11 12 2.75) 13 14 Some problem 1287 (11.9%) 808 (9.7%) 479 (19.3%) 15 Unable to walk around 44 (0.4%) 27 (0.3%) 17 (0.7%) 16 17 QOL Personal care 18 For peer review only 19 No problem 10245 (95.0%) 7956 (95.9%) 2289 (91.9%) 2.14 (1.73- <0.001 20 2.66) 21 22 Some problem 492 (4.6%) 309 (3.7%) 183 (7.3%) 23 24 Unable to wash/dress 52 (0.5%) 34 (0.4%) 18 (0.7%) 25 26 self 27 QOL Usual activity 28 29 No problem 8700 (80.7%) 6889 (83.0%) 1811 (72.8%) 1.84 (1.63- <0.001 30 31 2.08) 32 Some problem 1900 (17.6%) 1280 (15.4%) 620 (24.9%) 33 34 Unable to perform usual 187 (1.7%) 129 (1.6%) 58 (2.3%) 35 36 activities 37 38 QOL Pain/discomfort 39 No pain/discomfort 9305 (86.6%) 7242 (87.6%) 2063 (83.1%) 1.44 (1.24- <0.001 40 41 1.67) 42 43 Mod pain/discomfort 1395 (13.0%) 995 (12.0%) 400 (16.1%) 44 Extreme pain/discomfort 50 (0.5%) 31 (0.4%) 19 (0.8%) 45 46 QOL Anxiety/depression 47 48 No anxiety/depression 7827 (80.8%) 6132 (82.3%) 1695 (76.0%) 1.49 (1.30- <0.001 49 1.70) 50 51 Mod anxiety/depression 1704 (17.6%) 1226 (16.5%) 478 (21.4%) 52 53 Extreme 150 (1.5%) 94 (1.3%) 56 (2.5%) 54 55 anxiety/depression 7463 (89.9%) 56 57 58 QOL EQ-5D Index Score, 1.0 (0.8, 1.0) 1.0 (0.8, 1.0) 1.0 (0.8, 1.0) <0.001 59 60 median (IQR)
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1 2 3 QOL Own health state 80.0 (70.0, 90.0) 80.0 (70.0, 90.0) 80.0 (60.0, <0.001 4 5 today, median (IQR) 85.0) 6 7 1 *Index available in 58.7% of men and 57.8% of women. 8 2 9 10 11 12 13 14 15 16 17 18 For peer review only 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
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1 2 3 1 4 5 2 Table 4. Multivariate predictors of poor QoL at 30-days following ACS, based on EQ-5D index score 6 7 N=10812 8 OR 95% CI p-value 9 10 Age (for every ten year increase) 0.94 0.90 - 0.98 0.004 11 12 Female sex 0.57 0.50 - 0.64 <0.001 13 14 Diabetes 0.85 0.75 - 0.96 0.01 15 Previous CABG or PCI 0.88 0.78 - 0.99 0.03 16 17 CVD 0.58 0.43 - 0.78 <0.001 18 For peer review only 19 Out of hospital cardiac arrest 0.47 0.34 - 0.65 <0.001 20 LV EF > 44% 1.16 1.01 - 1.33 0.04 21 22 Renal failure 0.53 0.35 - 0.80 0.002 23 24 In-hospital cardiogenic shock 0.73 0.33 - 0.89 0.28 25 26 In-hospital major bleeding 0.54 0.41 - 1.29 0.02 27 3 28 29 4 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
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1 2 3 1 Table 5: Major adverse cardiovascular, cerebrovascular and bleeding events in patients who survived 4 5 2 to 30-days, according to sex 6 7 Characteristics All ACS STEMI 8 Men (n = Women (n p- Men (n = Women (n = p- 9 10 8315) =2497) value 3117) 803) value 11 12 Major 98 (1.2%) 36 (1.4%) 0.30 61 (2.0%) 22 (2.7%) 0.17 13 14 bleeding 15 Recurrent MI 104 (1.3%) 36 (1.4%) 0.48 47 (1.5%) 13 (1.6%) 0.87 16 17 Stroke 28 (0.3%) 10 (0.4%) 0.70 20 (0.6%) 7 (0.9%) 0.47 18 For peer review only 19 MACE 224 (2.7%) 74 (3.0%) 0.49 113 (3.6%) 31 (3.9%) 0.75 20 MACCE 251 (3.0%) 84 (3.4%) 0.39 132 (4.2%) 38 (4.7%) 0.56 21 22 3 Abbreviations: MI, myocardial infarction; CABG, coronary artery bypass grafting; MACE, major 23 24 4 adverse cardiovascular event; MACCE, major adverse cardiovascular or cerebrovascular event. 25 26 5 27 28 6 29 30 7 31 8 32 33 9 34 35 10 36 37 11 38 39 40 12 41 42 13 43 44 14 45 15 46 47 16 48 49 17 50 51 18 52 53 19 54 55 20 56 57 21 58 59 22 60
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1 2 3 1 Figure 1: Proportions of patients with moderate or severe impairment in each EQ-5D-3L domain, 4 5 2 6 according to sex 7 8 3 9 10 11 12 13 14 15 16 17 18 For peer review only 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
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1 2 3 1 References 4 2 5 6 3 1. Khan E, Brieger D, Amerena J, et al. Differences in management and outcomes for men and 7 8 4 women with ST-elevation myocardial infarction. Med J Aust. 2018;209(3):118-23.doi: 9 10 5 10.5694/mja17.01109 [Published Online First: 23 July 2018] 11 12 13 14 6 2. Chang WC, Kaul P, Westerhout CM, et al. Impact of sex on long-term mortality from acute 15 16 17 7 myocardial infarction vs unstable angina. Arch Intern Med. 2003;163(20):2476-84.doi: 18 For peer review only 19 8 10.1001/archinte.163.20.2476 [Published Online First: 12 November 2003] 20 21 22 23 9 3. Izadnegahdar M, Norris C, Kaul P, et al. Basis for Sex-Dependent Outcomes in Acute 24 25 10 Coronary Syndrome. Can J Cardiol. 2014;30(7):713-20.doi: 10.1016/j.cjca.2013.08.020 [Published 26 27 28 11 Online First: 24 January 2014] 29 30 31 32 12 4. Kwong E, Neuburger J, Petersen SE, et al. Using patient-reported outcome measures for 33 34 13 primary percutaneous coronary intervention. Open Heart. 2019;6(1):e000920.doi: 10.1136/openhrt- 35 36 14 2018-000920 [Published Online First: 16 February 2019] 37 38 39 40 41 15 5. Gencer B, Rodondi N, Auer R, et al. Health utility indexes in patients with acute coronary 42 43 16 syndromes. Open Heart. 2016;3(1):e000419.doi: 10.1136/openhrt-2016-000419 [Published Online 44 45 17 First: 3 June 2016] 46 47 48 49 18 6. Duenas M, Ramirez C, Arana R, et al. Gender differences and determinants of health related 50 51 19 quality of life in coronary patients: a follow-up study. BMC Cardiovasc Disord. 2011;11:24.doi: 52 53 54 20 10.1186/1471-2261-11-24 [Published Online First: 31 May 2011] 55 56 57 58 59 60
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1 2 3 1 7. Pettersen KI, Reikvam A, Rollag A, et al. Understanding sex differences in health-related 4 5 2 6 quality of life following myocardial infarction. Int J Cardiol. 2008;130(3):449-56.doi: 7 8 3 10.1016/j.ijcard.2007.10.016 [Published Online First: 28 November 2008] 9 10 11 12 4 8. Norris CM, Spertus JA, Jensen L, et al. Sex and gender discrepancies in health-related quality 13 14 5 of life outcomes among patients with established coronary artery disease. Circ Cardiovasc Qual 15 16 6 Outcomes. 2008;1(2):123-30.doi: 10.1161/circoutcomes.108.793448 [Published Online First: 25 17 18 For peer review only 19 7 December 2009] 20 21 22 23 8 9. Agewall S, Berglund M, Henareh L. Reduced quality of life after myocardial infarction in 24 25 9 women compared with men. Clin Cardiol. 2004;27(5):271-4.doi: 10.1002/clc.4960270506 [Published 26 27 10 Online First: 2004/06/11] 28 29 30 31 32 11 10. Garavalia LS, Decker C, Reid KJ, et al. Does health status differ between men and women in 33 34 12 early recovery after myocardial infarction? Journal of women's health (2002). 2007;16(1):93-101.doi: 35 36 13 10.1089/jwh.2006.M073 [Published Online First: 2007/02/28] 37 38 39 40 14 11. Emery CF, Frid DJ, Engebretson TO, et al. Gender differences in quality of life among cardiac 41 42 43 15 patients. Psychosom Med. 2004;66(2):190-7.doi: 10.1097/01.psy.0000116775.98593.f4 [Published 44 45 16 Online First: 2004/03/25] 46 47 48 49 17 12. Dreyer RP, Wang Y, Strait KM, et al. Gender differences in the trajectory of recovery in 50 51 18 health status among young patients with acute myocardial infarction: results from the variation in 52 53 54 19 recovery: role of gender on outcomes of young AMI patients (VIRGO) study. Circulation. 55 56 20 2015;131(22):1971-80.doi: 10.1161/circulationaha.114.014503 [Published Online First: 2015/04/12] 57 58 59 60
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1 2 3 1 13. von Elm E, Altman DG, Egger M, et al. The Strengthening the Reporting of Observational 4 5 2 6 Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. Ann 7 8 3 Intern Med. 2007;147(8):573-7.doi: 10.7326/0003-4819-147-8-200710160-00010 [Published Online 9 10 4 First: 17 October 2007] 11 12 13 14 5 14. Stub D, Lefkovits J, Brennan AL, et al. The establishment of the Victorian Cardiac Outcomes 15 16 6 Registry (VCOR): monitoring and optimising outcomes for cardiac patients in Victoria. Heart, Lung 17 18 For peer review only 19 7 and Circulation. 2018;27(4):451-63.doi: 10.1016/j.hlc.2017.07.013 [Published Online First: 11 20 21 8 October 2017] 22 23 24 25 9 15. Lefkovits J, Brennan A, Dinh D, et al. The Victorian Cardiac Outcomes Registry Annual Report 26 27 10 2017. Monash University, SPHPM; 2018. Report No 5. 28 29 30 31 32 11 16. Brooks R. EuroQol: the current state of play. Health Policy. 1996;37(1):53-72.doi: [Published 33 34 12 Online First: 1996/06/06] 35 36 37 38 13 17. Herdman M, Gudex C, Lloyd A, et al. Development and preliminary testing of the new five- 39 40 14 level version of EQ-5D (EQ-5D-5L). Qual Life Res. 2011;20(10):1727-36.doi: 10.1007/s11136-011- 41 42 43 15 9903-x [Published Online First: 12 April 2011] 44 45 46 47 16 18. Viney R, Norman R, King MT, et al. Time trade-off derived EQ-5D weights for Australia. Value 48 49 17 Health. 2011;14(6):928-36.doi: 10.1016/j.jval.2011.04.009 [Published Online First: 15 September 50 51 18 2011] 52 53 54 55 56 19 19. Mehran R, Rao SV, Bhatt DL, et al. Standardized Bleeding Definitions for Cardiovascular 57 58 20 Clinical Trials. Circulation. 2011;123(23):2736-47.doi: doi:10.1161/CIRCULATIONAHA.110.009449 59 60 21 [Published Online First:
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1 2 3 1 20. Leung Yinko SS, Pelletier R, Behlouli H, et al. Health-related quality of life in premature acute 4 5 2 6 coronary syndrome: does patient sex or gender really matter? J Am Heart Assoc. 2014;3(4).doi: 7 8 3 10.1161/jaha.114.000901 [Published Online First: 31 July 2014] 9 10 11 12 4 21. Schweikert B, Hunger M, Meisinger C, et al. Quality of life several years after myocardial 13 14 5 infarction: comparing the MONICA/KORA registry to the general population. Eur Heart J. 15 16 6 2009;30(4):436-43.doi: 10.1093/eurheartj/ehn509 [Published Online First: 21 November 2008] 17 18 For peer review only 19 20 21 7 22. Sullivan PW, Ghushchyan V. Preference-Based EQ-5D Index Scores for Chronic Conditions in 22 23 8 the United States. Med Decis Making. 2006;26(4):410-20.doi: 10.1177/0272989x06290495 24 25 9 [Published Online First: July 1 2006] 26 27 28 29 10 23. Vicent L, Martínez-Sellés M. Frailty and acute coronary syndrome: does gender matter? J 30 31 32 11 Geriatr Cardiol. 2019;16(2):138-44.doi: 10.11909/j.issn.1671-5411.2019.02.007 [Published Online 33 34 12 First: Feb 2019] 35 36 37 38 13 24. Pelletier R, Humphries KH, Shimony A, et al. Sex-related differences in access to care among 39 40 14 patients with premature acute coronary syndrome. Can Med Assoc J. 2014;186(7):497-504.doi: 41 42 43 15 10.1503/cmaj.131450 [Published Online First: 15 April 2014] 44 45 46 47 16 25. Jankowska-Polanska B, Uchmanowicz I, Dudek K, et al. Sex differences in the quality of life of 48 49 17 patients with acute coronary syndrome treated with percutaneous coronary intervention after a 3- 50 51 18 year follow-up. Patient Prefer Adherence. 2016;10:1279-87.doi: 10.2147/ppa.S106577 [Published 52 53 54 19 Online First: 9 August 2016] 55 56 57 58 20 26. Hess CN, Krucoff MW, Sheng S, et al. Comparison of quality-of-life measures after radial 59 60 21 versus femoral artery access for cardiac catheterization in women: Results of the Study of Access
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1 2 3 1 Site for Enhancement of Percutaneous Coronary Intervention for Women quality-of-life substudy. 4 5 2 6 Am Heart J. 2015;170(2):371-9.doi: 10.1016/j.ahj.2015.04.024 [Published Online First: 25 August 7 8 3 2015] 9 10 11 12 4 27. Rocha JA, Allison TG, Santoalha JM, et al. Musculoskeletal complaints in cardiac 13 14 5 rehabilitation: Prevalence and impact on cardiovascular risk factor profile and functional and 15 16 6 psychosocial status. Rev Port Cardiol. 2015;34(2):117-23.doi: 10.1016/j.repc.2014.08.022 [Published 17 18 For peer review only 19 7 Online First: 2015/02/11] 20 21 22 23 8 28. McGrady A, McGinnis R, Badenhop D, et al. Effects of depression and anxiety on adherence 24 25 9 to cardiac rehabilitation. J Cardiopulm Rehabil Prev. 2009;29(6):358-64.doi: 26 27 10 10.1097/HCR.0b013e3181be7a8f [Published Online First: 2009/11/27] 28 29 30 31 32 11 29. Vedanthan R, Seligman B, Fuster V. Global perspective on acute coronary syndrome: a 33 34 12 burden on the young and poor. Circ Res. 2014;114(12):1959-75.doi: 10.1161/circresaha.114.302782 35 36 13 [Published Online First: 7 June 2014] 37 38 39 40 14 30. Sumner J, Harrison A, Doherty P. The effectiveness of modern cardiac rehabilitation: A 41 42 43 15 systematic review of recent observational studies in non-attenders versus attenders. PLoS One. 44 45 16 2017;12(5):e0177658-e.doi: 10.1371/journal.pone.0177658 [Published Online First: 12 May 2017] 46 47 48 49 17 31. Valgimigli M, Frigoli E, Leonardi S, et al. Radial versus femoral access and bivalirudin versus 50 51 18 unfractionated heparin in invasively managed patients with acute coronary syndrome (MATRIX): 52 53 54 19 final 1-year results of a multicentre, randomised controlled trial. The Lancet. 2018;392(10150):835- 55 56 20 48.doi: 10.1016/S0140-6736(18)31714-8 [Published Online First: 25 August 2018] 57 58 59 60
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1 2 3 1 32. Stevanović J, Pechlivanoglou P, Kampinga MA, et al. Multivariate meta-analysis of 4 5 2 6 preference-based quality of life values in coronary heart disease. PLoS One. 7 8 3 2016;11(3):e0152030.doi: 10.1371/journal.pone.0152030 [Published Online First: 24 March 2016] 9 10 11 12 4 33. World Health Organization. Glossary of terms and tools Geneva, Switzerland: World Health 13 14 5 Organization; 2019 [Available from: https://www.who.int/gender-equity- 15 16 6 rights/knowledge/glossary/en/. 17 18 For peer review only 19 7 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 For peer review only 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 Figure 1: Proportions of patients with moderate or severe impairment in each EQ-5D-3L domain, according 46 to sex 47 209x296mm (300 x 300 DPI) 48 49 50 51 52 53 54 55 56 57 58 59 60 For peer review only - http://bmjopen.bmj.com/site/about/guidelines.xhtml BMJ Open Page 34 of 37
1 2 3 Supplementary Table 1. Baseline characteristics for patients with missing QoL data by sex 4 Factor Male (n = 4354) Female (n = 1351) p-value 5 6 Age, median (IQR) 7 Male 61.0 (53.0, 71.0) 68.0 (58.0, 77.0) <0.001 8 9 Body mass index, median 27.8 (25.0, 31.0) 27.7 (24.0, 32.4) 0.89 10 (IQR) 11 12 Diabetes medication 877 (20.1%) 329 (24.4%) <0.001 13 14 Previous CABG 254 (5.8%) 55 (4.1%) 0.01 15 Previous PCI 831 (19.1%) 229 (17.0%) 0.08 16 For peer review only 17 Cerebrovascular disease 144 (3.3%) 62 (4.6%) 0.03 18 history 19 20 Peripheral vascular disease 139 (3.2%) 48 (3.6%) 0.52 21 22 LVEF estimate 0.03 23 Normal (>50%) 1923 (53.2%) 628 (58.1%) 24 25 Mild dysfunction (45-50%) 1017 (28.1%) 263 (24.4%) 26 27 Moderate dysfunction (35- 493 (13.6%) 139 (12.9%) 28 44%) 29 30 Severe dysfunction (<35%) 183 (5.1%) 50 (4.6%) 31 eGFR, median (IQR) 94.7 (72.6, 121.12) 75.5 (53.8, 103.5) <0.001 32 33 Acute coronary syndrome type <0.001 34 35 Unstable angina 574 (13.2%) 226 (16.7%) 36 NSTEMI 1826 (41.9%) 619 (45.8%) 37 38 STEMI 1954 (44.9%) 506 (37.5%) 39 Cardiogenic shock 130 (3.0%) 46 (3.4%) 0.44 40 41 Out-of-hospital cardiac arrest 158 (3.6^) 18 (1.3%) <0.001 42 43 Cardiac arrest or pre- 264 (6.1%) 52 (3.9%) 0.002 44 procedure intubation 45 46 Radial access 2308 (53.1%) 609 (45.1%) <0.001 47 48 PCI result successful 4144 (95.2%) 1266 (93.7%) 0.033 49 In-hospital shock 60 (1.4%) 29 (2.1%) 0.06 50 51 In-hospital myocardial 22 (0.5%) 13 (1.0%) 0.072 52 infarction 53 54 New renal impairment 111 (2.7%) 51 (4.0%) 0.02 55 56 57 58 59 60 For peer review only - http://bmjopen.bmj.com/site/about/guidelines.xhtml Page 35 of 37 BMJ Open
1 2 3 Stent thrombosis 6 (0.1%) 6 (0.4%) 0.04 4 (definite/probable) 5 6 In-hospital major bleed 40 (0.9%) 25 (1.9%) 0.008 7 8 In-hospital stroke 18 (0.4%) 3 (0.2%) 0.44 9 Cardiac rehab referral 3478 (90.4%) 1025 (87.5%) 0.006 10 11 12 13 14 15 16 For peer review only 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 For peer review only - http://bmjopen.bmj.com/site/about/guidelines.xhtml BMJ Open Page 36 of 37
1 STROBE Statement—Checklist of items that should be included in reports of cohort studies 2 3 4 Item Page No 5 No Recommendation 6 Title and abstract 1 (a) Indicate the study’s design with a commonly used term in the title Pg 1 (title) 7 or the abstract 8 Pg 2 to pg 3 line 2 9 (b) Provide in the abstract an informative and balanced summary of 10 what was done and what was found 11 Introduction 12 Pg 4, lines 2-24 13 Background/rationale 2 Explain the scientific background and rationale for the investigation 14 being reported 15 Objectives 3 State specific objectives, including any prespecified hypotheses Pg 4, lines 21-24 16 17 Methods 18 Study design For4 Present peer key elements review of study design early only in the paper Pg 5, lines 5-16 19 Setting 5 Describe the setting, locations, and relevant dates, including periods of Pg 5, lines 5-24 20 recruitment, exposure, follow-up, and data collection 21 22 Participants 6 (a) Give the eligibility criteria, and the sources and methods of Eligibility, Pg 5, line 21 to pg 6, 23 selection of participants. Describe methods of follow-up line 9. Follow-up 24 pg 5, lines 12-16 25 N/A 26 (b) For matched studies, give matching criteria and number of exposed 27 and unexposed 28 Variables 7 Clearly define all outcomes, exposures, predictors, potential Outcomes pg 6 29 line 11-pg 7 line 4 confounders, and effect modifiers. Give diagnostic criteria, if 30 Predictors—pg 8 31 applicable line 20-pg 9 line 32 2, Table 1 pg 17- 33 18, 34 Supplementary 35 Table 1 36 Exposures and 37 diagnostic 38 criteria—pg 5 line 39 24-pg 6 line 9 40 Data sources/ 8* For each variable of interest, give sources of data and details of Sources of data pg 5 lines 4-16 41 measurement methods of assessment (measurement). Describe comparability of Comparability pg 42 assessment methods if there is more than one group 6 line 11 to pg 7 43 line 4 44 Bias 9 Describe any efforts to address potential sources of bias Missing vs no 45 missing data 46 comparison pg 7 47 lines 7-12 and pg 48 9 line 5-23 49 Study size 10 Explain how the study size was arrived at Pg 5, lines 22-23 50 Pg 7, line 6-pg 8, 51 Quantitative 11 Explain how quantitative variables were handled in the analyses. If line 17; pg 9 lines 52 variables applicable, describe which groupings were chosen and why 4-23 53 Pg 7, line 6-pg 8, 54 Statistical methods 12 (a) Describe all statistical methods, including those used to control for line 9 55 confounding 56 (b) Describe any methods used to examine subgroups and interactions Pg 7, line 14 to 57 pg8, line 9 58 (c) Explain how missing data were addressed Pg 7 lines 7-12 59 and pg 9 line 5-23 60 (d) If applicable, explain how loss to follow-up was addressed Pg 7 lines 7-12 and pg 9 line 5-23 (e) Describe any sensitivity analyses Pg 11, lines 1-11 1 For peer review only - http://bmjopen.bmj.com/site/about/guidelines.xhtml Page 37 of 37 BMJ Open
1 Results 2 Pg 7, lines 7-9 3 Participants 13* (a) Report numbers of individuals at each stage of study—eg numbers 4 potentially eligible, examined for eligibility, confirmed eligible, 5 included in the study, completing follow-up, and analysed 6 (b) Give reasons for non-participation at each stage Pg 7, lines 7-9 7 N/A 8 (c) Consider use of a flow diagram 9 Descriptive data 14* (a) Give characteristics of study participants (eg demographic, clinical, Pg 8 line 20 to pg 9 line 2, Table 1 10 social) and information on exposures and potential confounders 11 pg 17-18 12 (b) Indicate number of participants with missing data for each variable Pg9, lines 5-23, Table 2, Pg 19-20, 13 of interest 14 Supplementary 15 Table 1 16 (c) Summarise follow-up time (eg, average and total amount) Pg 5, lines 12-16 17 Outcome data 15* Report numbers of outcome events or summary measures over time Pg 9, line 1 to Pg 18 For peer review only 11, line 11 19 20 *Give information separately for exposed and unexposed groups. 21 22 23 Note: An Explanation and Elaboration article discusses each checklist item and gives methodological background and 24 published examples of transparent reporting. The STROBE checklist is best used in conjunction with this article (freely 25 available on the Web sites of PLoS Medicine at http://www.plosmedicine.org/, Annals of Internal Medicine at 26 27 http://www.annals.org/, and Epidemiology at http://www.epidem.com/). Information on the STROBE Initiative is 28 available at http://www.strobe-statement.org. 29 30 31 32 33 Main results 16 (a) Give unadjusted estimates and, if applicable, confounder-adjusted estimates Pg 10, line 1-pg 11, line 11 34 and their precision (eg, 95% confidence interval). Make clear which 35 confounders were adjusted for and why they were included 36 37 (b) Report category boundaries when continuous variables were categorized Pg 6, line 18-22, pg 8, lines 7-8, 38 Table 1 (eGFR 39 and LVEF 40 category) 41 N/A 42 (c) If relevant, consider translating estimates of relative risk into absolute risk 43 for a meaningful time period 44 Other analyses 17 Report other analyses done—eg analyses of subgroups and interactions, and Pg 10, lines 5-7, 45 pg 11, lines 1-11 sensitivity analyses 46 47 Discussion 48 Key results 18 Summarise key results with reference to study objectives Pg 11, lines 13-17 49 Limitations 19 Discuss limitations of the study, taking into account sources of potential bias or Pg 13, line 15 to 50 pg 14, line 14 51 imprecision. Discuss both direction and magnitude of any potential bias 52 Interpretation 20 Give a cautious overall interpretation of results considering objectives, Pg 12 line 25 to pg 13 line 13 53 limitations, multiplicity of analyses, results from similar studies, and other 54 55 relevant evidence 56 Generalisability 21 Discuss the generalisability (external validity) of the study results Pg 12, lines 15-23 57 Other information 58 Pg 16, line 2 59 Funding 22 Give the source of funding and the role of the funders for the present study and, 60 if applicable, for the original study on which the present article is based
2 For peer review only - http://bmjopen.bmj.com/site/about/guidelines.xhtml