Gender Differences in Cost Effectiveness of Coronary Angiography Following Hospital Admission

Gender Differences in Cost Effectiveness of Coronary Angiography Following Hospital Admission for an Acute Coronary Syndrome In Canada as in the United States Angiography rates have been increasing in step with CABG and PCI volumes1. Given the increase in CAD rates, optimal target volumes of angiographies have been set higher as well2 3. Previous studies, however, suggest selective as opposed to routine use of Angiography may offer the most cost-effective benefit to patients4 5 . In spite of the benefits of catheterization, women continue to receive this procedure at much lower rates than men. An important question to ask is whether this disparity is warranted. The literature presents a mixed picture with respect to the outcomes (i.e. death, recurrent ACS events) in women resulting from the routine use of angiographic procedures6-10. The objective of our study is to undertake a cost-effectiveness analysis, separately for men and women, using data provided by Statistics Canada, Canadian Institute for Health Information (CIHI) as well as peer reviewed literature to assess the incremental benefit of angiography. In our context, benefit refers to mortality as well as hospital readmission rates. DataInput data was derived from Statistics Canada hospital person oriented information database (HPOI) reporting angiography for men and women between 2002 and 2005. All remaining information viz. a viz. probability data was gathered from secondary sources primarily focusing on peer reviewed medical articles preferably published in Canada or the U.S.A.MethodsThe cost effectiveness study is based on discrete time Markov model design using a three state design. The cost methodology will be described separately in the next section. Markov ModelsIncreasingly Markov models have become the tool of choice when conducting cost- effectiveness or other decision analyses. However the basic premise or assumptions underlying these models have often been ignored. In general terms the future state of a system must depend only on the current state and not any prior state , for otherwise the conditions for employing a Markov model are violated. With that caveat in mind , the essential Markov constructs listed below are briefly reviewed.  Defined time period or cycleTo begin with a period or cycle is predetermined, usually based on calendar time such as a year or month. In our study data, including costs were available on a yearly basis.  Health or Disease statesA cohort of 100,000 men and women enter the model following an Acute Coronary Syndrome (ACS), and are assigned to a hospitalization state. From this state a patient can transit to either a wellness state (discharged from hospital alive), death state (removed from cohort model), or return to a hospital state. The hospital and well state are deemed recurrent states as returning to these states in subsequent cycles has a positive probability. The death state is an absorbing state as once entering this state , exiting is no longer possible (probability of 0). (See Figure 1) Probabilities of transition from one health state to anotherTransitions from one state to another involve various intermediate decision points prior to transiting to a new state. These decision pathways are associated with probabilities derived from previous study results and/or peer reviewed literature . In our analysis all patients initially transit from a hospital state to an intermediate state determined by whether they received an Angiography during the same episode of care. Regardless of which decision path a patient followed, their survival status is assessed. If the patient died, then the cohort is reduced in size , and no longer contributes cost or event data. In the event a patient survives , the patient follows one of three possible categories of treatment : 1) PTCA; 2) CABG; 3) non-surgical treatment (Medication only). To be sure patients undergoing a PTCA or CABG are assumed to be on medication as per guideline protocols. Following a treatment regime , the patient is assumed to transit to a well state (discharge from hospital) or a hospital state (readmitted to hospital) at the beginning of the next cycle. If a patient transits is readmitted to hospital , the same intermediate transitions as in the initial state are available. Otherwise, in a healthy or well state, a patient may transit to a hospital state (readmission to hospital within the cycle), die, or continue to be well to the end of the cycle (see Figure 2 for a complete illustration of the tree pathways). Table 1 lists all transition probabilities for both men and women along with their references.  Initial probabilities of starting in any given health stateIn our study all patients enter the model hospitalized following an ACS. Hence the probability of initially being in the hospitalization state is 1, and 0 for the remaining states. Termination ConditionsIn order for a Markov model to terminate in the sense that any computer model requires some stopping rule, the simulation was allowed to run for up to 5 cycles. In other words a follow-up of 5 years was allotted. Each cycle should correspond to the associated probability of any event. Outcomes from each cycle are comprised of cumulative costs , events, and transition probabilities.Hospital costs Hospitalization costs were calculated using the Canadian Institute for Health Information (CIHI) methodology which translates the Canadian inpatient data into CMGs, equivalents of DRGs. The standard costs, based on typical inpatient data, by diagnosis, were estimated using CMG + 2011 Discharge Abstract Database. The CMG + 2011 methodology was based on ICD-10CA and CCI classification systems. Inpatient data from Alberta, British Columbia and Ontario, of approximately 1 million cases, were used to calculate the case-cost data used for the RIW calculations. The CIHI data include all hospital care costs except physician fees. Inpatient and outpatient physician fees The unit costs of physician services for emergency, inpatient and outpatient care were estimated using reimbursement fees from Ontario and adjusted rates from Quebec, to calculate the average fees for those two provinces, since over 60% of health care costs are spent in these two provinces. Costs of outpatient diagnostic laboratory services Costs of outpatient diagnostic laboratory services, including the professional and technical components, were estimated using Ontario and adjusted Quebec reimbursement fees.Costs of outpatient prescription drugs Costs of outpatient prescription drugs, directly to the patient, including the dispensing fees and the pharmacy mark-ups, were estimated using data from IMS Health Canada. ResultsCostsTable 1 presents average hospitalization costs for patients presenting with an Acute Coronary Syndrome . Specifically hospital costs per stay are reported across age strata of18-59 , 60-79, and 80+ years along with intensity weights (RIW). Filling out the table are the primary CMG codes and average physician fees. All costs are assumed to apply equally for men and women. Outpatient costs are reported in Table 2, and categorized by physician, diagnostic, and drug costs. Once again we assume that once discharged, men and women incur the same costs. Hence the assumption is that once hospitalized there is no difference in cost between men and women. EventsThe discrete time Markov model was designed to run over a 5 year period starting with a patients initial hospitalization for an ACS. With each year corresponding to a cycle , Figure 2 presents all possible pathways that may occur over this period as elucidated by the probabilities laid out in tables 3 and 4. Beginning with a cohort of patients who are hospitalized with probability 1, patients travers the tree (Figure 2) according to prescribed probabilities derived from our study and peer reviewed literature. According to the data derived from the HPOI Statistics Canada database , more men than women had a likelihood of undergoing an Angiogram following an ACS during the same episode of care (43.5% vs. 31.5%). Similarly, the remaining probabilities are listed corresponding to the Markov model in Figure 2. Along with a description of the probabilities is a column listing the number corresponding to the cited reference in the bibliography.OutcomesFollowing hospitalizations for an ACS, first year costs were higher for men than for women ($1,163,781,630 vs. $1,020,880,422). However costs post 1 year were lower forMen than for women ($965,128,460 vs $926,556,442) or an average of $9.6 million a year . Corresponding to the increased cost incurred by women, between years 1 and 5 they sustained 90,803 readmissions as compared with 76,150 for men. The excess cost of$38,572,018 and 14,653 readmissions post year 1 resulted in an average of cost of $2632 per additional hospitalization in women. DiscussionAfter the first year of follow-up, the cost of treating women with ACS exceeds that of men by virtue of increased hospital admission over the subsequent 4 year period. The additional cost resulting from the excess readmissions in women amounted to over $2600 per stay. This study assumes that men and women benefit equally from the use of catheterization. Furthermore, no adjustment is made for disease severity. Likewise, the administrative data component of our database provided details of the diagnosis and subsequent procedures, but little information on the extent, severity of disease (i.e. single vs. multi vessel involvement), or classification of the ACS. Specifically, we could not deduce the category of disease be it ST-segment elevation myocardial infarction (STEMI) or non ST-segment elevation myocardial infarction (NSTEMI). That said, recent studies, Elbarasi et al.11 and Nguyen et al.12 have demonstrated that age and pre- existing comorbidities were, independent of all other factors, strong predictors of whether a patient underwent catheterization following an ACS. Finally, data on the admitting institution – such as the presence of a catheterization laboratory was not present although we were able to access data on the procedures performed in all hospitals treating the patient during the same episode of care. In addition, caution should be taken in assuming the course of treatment for women would be identical to men regardless of whether or not catheterization was performed. These differences may simply be attributed to accessibility to catheterization labs. From a policy perspective, targeting specific populations subgroups, including gender that are underserved in their health care needs , such as access to medical technology, may be a more cost-effective approach to spending health care dollars. ResultsTable 1: Inpatient costs by RIW, by age, in $2010 Avg physician Diagnosis Average LOS RIW Avg hospital$/stay CMG codes fees CABG with angiography 18–59 Years 10.5 3.38850 18,007 166-169 $3,887 60–79 Years 11.6 3.58623 19,057 $3,950 80+ Years 13.2 3.85736 20,498 $4,046 CABG no angiography 18–59 Years 5.8 2.57898 13,705 170-172 $3,086 60–79 Years 6.5 2.71760 14,441 $3,126 80+ Years 7.6 2.99210 15,900 $3,193 PTCA 18–59 Years 2.6 1.56912 8,338 175+176 $669 60–79 Years 2.7 1.59958 8,500 $672 80+ Years 3.6 1.79621 9,545 $728 ACS with angiography 18–59 Years 3.4 1.04858 5,572 193+203 $906 60–79 Years 3.9 1.13399 6,026 $938 80+ Years 4.9 1.29674 6,891 $995 ACS no angiography 18–59 Years 3.1 0.77653 4,126 194+204 $361 60–79 Years 3.7 0.85438 4,540 $397 80+ Years 4.7 1.00246 5,327 $453 Medical follow-up 18–59 Years 2.4 0.49806 2,647 196+204+208 $319 60–79 Years 3.8 0.70991 3,772 $403 80+ Years 5.2 0.91030 4,837 $482 Table 2 : Outpatient CostsItems Visits/year Unit cost Total costs/yearPhysician initial GP 1 $69.17 $69.17 repeat GP 3 $34.37 $103.10 cardiologist 2 $92.13 $184.26 Diagnostic tests $567 ECG Chest Xray Cardiac catheterization Radionuclide ventriculogram Echocardiography BiPap CT Thorax Ventilation perfusion scan Holter test Treadmill test Drugs $625Total outpatient costs $1,549 Table 3 : Males Description of Variable Name Probability Reference Probability Probability of PangioM 0.435 Study(HPOI, Angiography Statistics Canada) Probability of death PdtnoangM 0.046 13 (No Angiography) Probability of PCTA PangpciM 0.425 13 following Angiography Probability of CABG PAngcabgM 0.29 13 following Angiography Probability of PCTA PNoAngpciM 0.254 13 without Angiography Probabilty of CABG PNoAngcabgM 0.207 13 without Angiography Probability of death PdtangM 0.043 13 (Angiography) Probability of PreadpctaM 0.189 6 14-16 readmission following PCTA (No Angiography) Probability of PreadcabgM 0.078 6 14-16 readmission following CABG (No Angiography) Probabilty of preadmedsM 0.303 6 14-16 readmission following medical trx (No Angiography) Probability of PreadpctaAngM 0.127 6 14-16 readmission following PCTA (Angiography) Probability of PreadcabgangM 0.052 6 14-16 readmission following CABG (Angiography) Probability of PreadmedsAngM 0.291 6 14-16 readmission following Medical Trx (Angiography) Table 4: Females Description of Variable Name Probability Reference Probability Probability of PangioF 0.315 Study(HPOI, Angiography Statistics Canada) Probability of death PdtnoangF 0.039 13 (No Angiography) Probability of PCTA PangpciF 0.412 13 following Angiography Probability of CABG PAngcabgF 0.199 13 following Angiography Probability of PCTA PNoAngpciF 0.247 13 without Angiography Probabilty of CABG PNoAngcabgF 0.153 13 without Angiography Probability of death PdtangF 0.043 13 (Angiography) Probability of PreadpctaF 0.206 6 14-16 readmission following PCTA (No Angiography) Probability of PreadcabgF 0.105 6 14-16 readmission following CABG (No Angiography) Probabilty of PreadmedsF 0.346 6 14-16 readmission following medical trx (No Angiography) Probability of PreadpctaAngF 0.169 6 14-16 readmission following PCTA (Angiography) Probability of PreadcabgangF 0.086 6 14-16 readmission following CABG (Angiography) Probability of PreadmedsAngF 0.332 6 14-16 readmission following Medical Trx (Angiography) Table 5First Year Cost 1-5 Year Cost Hospitalizations Years 1 - 5 Females 16700 Without $591,540,663 $561,126,530 62442 Angiography With $429,339,759 $404,001,930 28361 Angiography Total $1,020,880,422 $965,128,460 90803 Males 17800 Without $525,663,427 $423,235,013 43217 Angiography With $638,118,203 $503,321,429 32933 Angiography Total $1,163,781,630 $926,556,442 76150 References1. Natarajan MK. , Gafni A. , Yusuf S. . Determining optimal population rates of cardiac catheterization: A phantom alternative ? CMAJ 2005;173(1):49-52. 2. CanadianCancerNetwork(CCN). Concensus Panel on Target Setting. 2004. 3. Graham MM., Ghali WA., Faris PD., Galbraith PD., Tu JV., Norris CM., et al. Population rates of cardiac catheterization and yield of high-risk coronary artery disease. CMAJ 2005;173(1):35-39. 4. Barnett PG. , Chen S. , Boden WE. , Al. e. Cost-Effectiveness of a Conservative , Ischemic-Guided management Strategy After Non-Q-Wave Myocardial Infarction: Results of a Randomized Trial. Circulation 2002;105:680-84. 5. Kuntz KM. , Tsevat J. , Goldman L. , Weinstan MC. . Cost-effectiveness of Routine Coronary angiography After Acute Myocardial Infarction. Circulation 1996;94:957-65. 6. Birkhead JS. , Weston CFM. , Chen R. . Determinants and outcomes of coronary angiography after non-ST-segment elevation myocardial infarction. A cohort study of the Myocardial Ischemic National audit project (MINAP). Heart 2009;95:1593-99. 7. Dey S., Flather MD. , Devlin G. , et Al. . Sex-related differences in the presentation, treatment andoutcomes among patients with acute coronary syndromes: the Global Registry of Acute Coronary Events. Heart 2009;95:20-26. 8. Anand S. XC, Mehta S. , et al. Differences in the Management and Prognosis of Women and Men Who Suffer From Acute Coronary Syndromes. JACC 2005;46(10):7. 9. Alfredsson J., Stenestrand U., Wallentin L., Swahn E. Gender differences in management and outcome in non-ST elevation acute coronary syndrome. Heart 2007;93:1357-62. 10. King KM. , Ghali WA. , Faris PD. , et al. . Sex Differences in Outcomes After cardiac Catheterization:Effect Modification by Treatment Strategy and Time. JAMA 2004;291(10):1220-25. 11. Elbarasi E, Goodman S, Yan R, Welsh R, Kornder J, Wong G, et al. Management patterns of non-ST segment elevation acute coronary syndromes in relation to prior coronary revascularization. Am Heart J 2010;159(1):40-46. 12. Nguyen H, Goldberg R, Gore J, Fox A, Eagle K, Gurfinkle E, et al. Age and sex differences, and changing in the use of evidence-based therapies in acute coronary syndromes: perspectives from a multinational registry. Coronary Artery Disease 2010;21:336-44. 13. O'Donoghue M, Boden WE, Braunwald E, Cannon CP, Clayton TC, de Winter RJ, et al. Early invasive vs conservative treatment strategies in women and men with unstable angina and non-ST-segment elevation myocardial infarction: a meta-analysis. JAMA : the journal of the American Medical Association 2008;300(1):71-80. 14. Guru V, Fremes S, Austin P, Blackstone E, Tu J. Gender Differences in Outcomes After Hospital Discharge From Coronary Artery Bypass Grafting. Circulation 2006;113:507-16. 15. Serruys PW, Unger F, Sousa JE, Jatene A, Bonnier HJ, Schonberger JP, et al. Comparison of coronary-artery bypass surgery and stenting for the treatment of multivessel disease. The New England journal of medicine 2001;344(15):1117-24. 16. Lee C, Tam MS, Yan A, Yan A, Fitchett D, Grima E, et al. Use of Cardiac Catheterization for Non-ST-Segment Elevation Acute Coronary Syndromes. Arch Intern Med 2008;168(3):291-96. Figure 1 Figure 2

Gender Differences in Cost Effectiveness of Coronary Angiography Following Hospital Admission

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