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Prediction of Median Survival Time in Sepsis Patients by the SOFA Score

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Prediction of Median Survival Time in Sepsis Patients by the SOFA Score Burns & Trauma, 2020, 8, tkz006 doi: 10.1093/burnst/tkz006 Research Article Research Article Prediction of median survival time in sepsis patients by the SOFA score combined with different predictors Wen Li1, Meiping Wang1,2, Bo Zhu1, Yibing Zhu1,3,* and Xiuming Xi1,* 1Department of Critical Care Medicine, Fuxing Hospital, Capital Medical University. 20A Fuxingmenwai Street, Xicheng District, Beijing 100038, China, 2Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University. 10 Xitoutiao, Youanmenwai, Fengtai District, Beijing 100069, China and 3Department of Statistics, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100037, China *Correspondence. Xiuming Xi, Email: [email protected]; Yibing Zhu, Email: [email protected] Received 30 July 2019; Revised 21 August 2019; Editorial decision 27 November 2019 Abstract Background: Sepsis is the leading cause of intensive care unit (ICU) admission. The purpose of this study was to explore the prognostic value of the Sequential Organ Failure Assessment (SOFA) score, the Acute Physiological and Chronic Health Evaluation II (APACHE II) score, and procalcitonin (PCT), albumin (ALB), and lactate (LAC) levels in patients with sepsis. Methods: Consecutive adult patients with suspected or documented sepsis at ICU admission were recruited. Their basic vital signs and related auxiliary examinations to determine their PCT and ALB levels and APACHE II score were recorded at ICU admission, and their LAC levels and SOFA scores were recorded for one week after admission. The influence of these variables on hospital mortality was evaluated. Logistic regression was used to derive the Sepsis Hospital Mortality Score (SHMS), a prediction equation describing the relationship between predictors and hospital mortality. The median survival time was calculated by the Kaplan–Meier method. In the validation group, the kappa value was calculated to evaluate the stability of the derived formula. Results: This study included 894 sepsis patients admitted to 18 ICUs in 16 tertiary hospitals. Patients were randomly assigned to an experimental group (626 cases) and validation group (258 cases). In addition, a nonsurvival group (248 patients) of the experimental group was established according to the outcome at the time of discharge. The hospital mortality rate in the experimental group was 39.6% (248/626). Univariate and multivariate regression analyses revealed that the APACHE II score (odds ratio [OR] = 1.178), SOFA (OR = 1.186), LAC (OR = 1.157), and SOFA mean score (OR = 1.086) were independently associated with hospital mortality. The SHMS was calculated as logit(p) = 4.715 – (0.164 × APACHE II) – (0.171 ×SOFA) – (0.145 ×LAC) – (0.082 × SOFA mean). A receiver operating characteristic curve was constructed to further investigate the accuracy of the SHMS, with an area under the curve of 0.851 (95% confidence interval [CI] 0.821–0.882; p < 0.001) for hospital mortality. In the low-risk group and high-risk groups, the corresponding median survival times were 15 days and 11 days, respectively. Conclusion: The APACHE II score, SOFA, LAC and SOFA mean score were independently associated with hospital mortality in sepsis patients and accurately predicted the hospital mortality © The Author(s) 2020. Published by Oxford University Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License 1 (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact [email protected] 2 Burns & Trauma, 2020, Vol. 8, tkz006 rate and median survival time. Data on the median survival time in sepsis patients could be provided to clinicians to assist in the rational use of limited medical resources by facilitating prudent resource allocation. Trial registration: ChiCTR-ECH-13003934, retrospectively registered on August 03, 2013. Key words: Sepsis, SOFA, APACHE II, Lactate, Mortality, Sequential Organ Failure Assessment, Acute Physiological and Chronic Health Evaluation II Background Methods Sepsis is the leading cause of intensive care unit (ICU) admission Patient selection [1]. In addition, sepsis is associated with concurrent multiple organ This prospective observational study was performed from January dysfunction syndrome, which is the main cause of death in ICU 2014 to August 2015 in 18 ICUs in 16 tertiary hospitals in China. patients [2]. The social and economic impact of sepsis consumes The sepsis 1.0 standard was used to establish the database initially a considerable proportion of healthcare resources [3, 4]. Despite [12]. We conducted a retrospective study with information from this the decline in mortality in the past decade due to increased sep- prospective cohort database, so database patients were diagnosed sis awareness and management, the short-term mortality rate has according to the new definitions proposed by the Third International remained at 20% or higher as the population ages, the number of Consensus Definitions for Sepsis and Septic Shock [8]. invasive medical procedures increases, and the incidence of cancer Consecutive adult patients with suspected or documented sepsis in the elderly population increases [5, 6]. Early stratification and at ICU admission were recruited. We excluded patients who met any identification of patients with a high risk of death is essential [7]. one of the following conditions: (1) withdrawal of life-sustaining The early warning score is an alternative tool for risk stratification. treatment; (2) postoperative cardiopulmonary resuscitation (CPR); This will help guide clinicians in developing different treatment plans or (3) incomplete data. in a timely manner for the individual patient with different risk The protocol used in this study was approved by the local levels. ethics committee (Fuxing Hospital, Capital Medical University, The latest definition of sepsis emphasizes organ failure [8], refer- 2013FXHEC-KY018). Eight hundred and ninety-four patients were ring to two or more changes in the Sequential Organ Failure (SOFA) recruited. García-Gallo et al. reported the grouping ratio as 7:3 [13], score. One of the scoring systems used for predicting mortality in approximately 70% of the patients were randomly assigned to the septic patients is the initial SOFA score. Harm-Jan de Grooth and experimental group (626 sepsis cases). It is generally believed that the colleagues [9] pointed out that the effects of treatment on the change sample size should be at least 10–20 times higher than the number in the SOFA score appear to be reliably and consistently associated of independent variables [14]; the relatively stricter requirement is with mortality in random controlled trials (RCTs). Fixed-day SOFA that the number of samples in each category in the two-classification was the most frequently reported outcome among the reviewed RCTs, results is at least 10 times higher than the number of independent but it was not significantly associated with mortality. variables. One of the characteristics of a clinical early warning scoring Basic vital signs and patient characteristics, such as age, sex, system is that it needs to apply commonly used and easily available admission category, auxiliary examinations, comorbidities, primary predictive indicators. The Acute Physiological and Chronic Health sites of infection, PCT level, albumin (ALB) level, and APACHE II Evaluation II (APACHE II) score, SOFA score, lactate (LAC) and score, were collected at ICU presentation, while the LAC levels and procalcitonin (PCT) levels are all meet this requirement. However, SOFA scores were recorded for one week after admission. The values the establishment of the diagnostic criteria was based on a retrospec- immediately preceding missing values were used when data values tive analysis of the database, and significant data loss inevitably were missing [15]. affected the final results. Their limited performance prevents these markers from being applied to individual risk stratifications Definition of predictive indicators [10, 11]. To create a predictive model, biomarker levels were presented in Combining several predictors into a single classification rule absolute values and measured at different time points; the relative should help to improve their accuracy and practicability. We inves- change in biomarker values was analyzed according to each indictor, tigated the dynamic changes in the SOFA score and LAC levels which was described as follows: in patients with sepsis during the first week after admission. The purpose of the present study was to derive a prediction equation 1. SOFA max = maximum SOFA score over 7 consecutive days, using a combination of the APACHE II score, SOFA score, and SOFA min = minimum SOFA score over 7 consecutive days, LAC and PCT levels to predict hospital mortality in sepsis patients; SOFA initial = SOFA score on the first day after admission, this combined score is called the sepsis hospital mortality score SOFA mean = average SOFA score for 7 consecutive days. (SHMS). Furthermore, according to the formula, the severity of 2. SOFA1 = SOFA max – SOFA min, SOFA2 = SOFA max – the conditions of patients was evaluated, and stratification was SOFA initial. performed to calculate the median survival time corresponding to 3. LAC max
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