www.nature.com/scientificreports OPEN The association between four scoring systems and 30‑day mortality among intensive care patients with sepsis: a cohort study Tianyang Hu 1,4, Huajie Lv2,4 & Youfan Jiang3* Several commonly used scoring systems (SOFA, SAPS II, LODS, and SIRS) are currently lacking large sample data to confrm the predictive value of 30‑day mortality from sepsis, and their clinical net benefts of predicting mortality are still inconclusive. The baseline data, LODS score, SAPS II score, SIRS score, SOFA score, and 30‑day prognosis of patients who met the diagnostic criteria of sepsis were retrieved from the Medical Information Mart for Intensive Care III (MIMIC‑III) intensive care unit (ICU) database. Receiver operating characteristic (ROC) curves and comparisons between the areas under the ROC curves (AUC) were conducted. Decision curve analysis (DCA) was performed to determine the net benefts between the four scoring systems and 30‑day mortality of sepsis. For all cases in the cohort study, the AUC of LODS, SAPS II, SIRS, SOFA were 0.733, 0.787, 0.597, and 0.688, respectively. The diferences between the scoring systems were statistically signifcant (all P‑values < 0.0001), and stratifed analyses (the elderly and non‑elderly) also showed the superiority of SAPS II among the four systems. According to the DCA, the net beneft ranges in descending order were SAPS II, LODS, SOFA, and SIRS. For stratifed analyses of the elderly or non‑elderly groups, the results also showed that SAPS II had the most net beneft. Among the four commonly used scoring systems, the SAPS II score has the highest predictive value for 30‑day mortality from sepsis, which is better than LODS, SIRS, and SOFA. The results of the DCA curves show that using the SAPS II score to predict the 30‑day mortality of intensive care patients with sepsis to guide clinical applications may obtain the highest net beneft. Te third international consensus defnitions for sepsis and septic shock (Sepsis-3) were released in 2016 1. Sep- sis is defned as life-threatening organ dysfunction caused by a dysregulated host response to infection. Septic shock is defned as a subset of sepsis and can be identifed with a clinical construct of sepsis with persisting hypotension requiring vasopressors to maintain mean arterial pressure (MAP) ≥ 65 mmHg and having a serum lactate level > 2 mmol/L despite adequate volume resuscitation. Sepsis has become an important public health issue worldwide, and its overall mortality rate is about 30%, especially in the intensive care unit (ICU)1,2. Sep- sis-3 emphasized the strong association between infection and organ failure1, thus, a scoring system for organ dysfunction may be of great beneft to early-recognition of sepsis, especially for patients admitted to the ICU. As the most commonly used severe disease scoring system in clinical practice, Sequential Organ Failure Assessment (SOFA) score is listed as the diagnostic criteria for sepsis (Sepsis-3)1, and has been proven to be efective in evaluating the prognosis of patients with sepsis3. Simplifed Acute Physiology Score (SAPS II) was proposed in 19934, and a study showed that patients with an admission diagnosis of sepsis/septic shock had the highest values of SAPS II5. Te Logistic Organ Dysfunction System (LODS) provides an objective tool for assessing severity levels for organ dysfunction in the ICU 6, each variable included in the LODS score is screened and weighted by Logistic regression. However, a previous study showed that the diference between LODS score and SOFA score in predicting the prognosis of sepsis was not statistically signifcant 7. Te task force of Sepsis-3 replaced the systemic infammatory response syndrome (SIRS) criteria with SOFA score due to lacking 1Department of Cardiology, The Second Afliated Hospital, Chongqing Medical University, Chongqing, China. 2Department of Infectious Diseases, Southwest Hospital, Third Military Medical University (Army Medical University), Chongqing, China. 3Department of Respiration, The Second Afliated Hospital, Chongqing Medical University, 74 Linjiang Road, Yuzhong District, Chongqing 400010, China. 4These authors contributed equally: Tianyang Hu and Huajie Lv. *email: [email protected] Scientifc Reports | (2021) 11:11214 | https://doi.org/10.1038/s41598-021-90806-2 1 Vol.:(0123456789) www.nature.com/scientificreports/ discriminant validity and convergent validity 1. Relative simplifcation is the advantage of SIRS criteria, and its net beneft of predicting mortality is still inconclusive. Tis study intends to explore the association between the four scoring systems (SOFA, SAPS II, LODS, and SIRS) and 30-day mortality of sepsis based on the MIMIC- III (Medical Information Mart for Intensive Care III) ICU database, to determine which scoring system could better predict 30-day mortality of sepsis and septic shock from the beginning of ICU admission. Considering that elderly patients with sepsis ofen present with atypical, nonspecifc symptoms, and have greater mortality risks due to delay in time to diagnosis 8,9, we will conduct a stratifed analysis of elderly and non-elderly patients to determine whether age afects the efcacy of the scoring systems. In particular, we expect to discuss the net benefts between the scoring systems and 30-day mortality of sepsis through the decision curve analysis (DCA), a suitable method for evaluating alternative diagnostic and prognostic strategies10. Methods Database. MIMIC-III is a large, freely-available database comprised of over forty thousand patients admit- ted to the Beth Israel Deaconess Medical Center (BIDMC) between 2001 and 2012 11. Any researcher who com- plies with the data use requirements is permitted to use the database. Afer passing the “Protecting Human Research Participants” exam on the website of the National Institutes of Health (NIH), an author (Tianyang Hu) was approved to extract data from this database (Record ID: 37474354). All patient-related information in the MIMIC-III database is anonymous and no informed consent is required. Study population. We followed the method of Johnson et al.12 to screen patients in the MIMIC-III ICU database from years 2008 to 2012 (the reason was that the group of admissions between 2008 and 2012 were easily identifable in the database) that met the Sepsis-3 criteria, the core criteria for sepsis were extracted as sus- pected infection with associated organ dysfunction (SOFA greater than or equal to 2). All patients were required to have at least 24 h of ICU data. Finally, 5784 patients were identifed as meeting the criteria, which was consist- ent with the results of Johnson et al.12. Meanwhile, we conducted a stratifed analysis of the elderly (more than 65 years old) and non-elderly. Data extraction. Data were acquired from the MIMIC-III database (v1.4). PostgreSQL 10.7 and Navicat Premium 15.0 sofware were used to extract the basic characteristics (subject id, ICU stay id, age, gender), septic shock or not, coexisting comorbidities(coronary atherosclerotic heart disease, diabetes, hypertension, chronic pulmonary disease, and renal failure) of the patients that met the Sepsis-3 criteria from MIMIC-III database by SQL language (Structure query language), and extracted the relevant items to calculate the four scoring systems (SOFA, SAPS II, LODS, and SIRS) with the help of the MIMIC-III Concepts provided by Github community (https:// github. com/ MIT- LCP/ mimic- code/ tree/ master/ conce pts). Statistical analysis. Continuous variables were assessed for normality using the Kolmogorov–Smirnov test. Continuous variables with a normal distribution were expressed as mean ± standard deviation (M ± SD), and the independent sample t test was used for the comparison; if the distribution was not normal, continuous variables were expressed as the median with interquartile range (IQR), and the Wilcoxon rank-sum test was used for comparison. Categorical variables were expressed as numbers and percentages, and compared using the Chi-square test. Multiple and binomial logistic regression analysis of the four scoring systems for 30-day mortality among intensive care patients with sepsis were conducted to adjust the results of the statistical analysis for potential confounding factors. Variables with a P-value of < 0.1 in univariate analysis were included in mul- tivariate analysis. Z test was used to compare the predictive value of each scoring system by comparing the area under curves (AUC) of the receiver operating characteristic curves (ROC), and the larger the AUC, the better the predictive performance. All the analyses were conducted using SPSS sofware (v26.0; IBM, Armonk, NY), MedCalc Statistical Sofware (v19.6.1; MedCalc Sofware Ltd, Ostend, Belgium), and R sofware (version 4.0.3, CRAN). Among them, Z test was performed with Medcalc Statistical Sofware following the method of Delong et al.13; the DCA was performed with R sofware, mainly using the “rmda” package. A P-value < 0.05 was consid- ered to be statistically signifcant. Results Baseline characteristics. A total of 5784 sepsis patients (elderly, n = 3138; non-elderly, n = 2646) were included in our study, in which, 1042 died and 4742 survived within 30 days. Te age of the death group (71.66 ± 15.61) was higher than that of the survival group (64.17 ± 17.77), and the diference was statistically sig- nifcant (P < 0.001). In addition, in the death group, the incidence of septic shock and coexisting comorbidities (chronic pulmonary disease and renal failure) was higher (P < 0.001, P = 0.004, and P = 0.006, respectively) and the four scoring systems (SOFA, SAPS II, LODS, and SIRS) scored higher (all P < 0.001). Te gender and other coexisting comorbidities (coronary atherosclerotic heart disease, diabetes, and hypertension) show no signif- cant diference between the two groups. Te baseline data are summarized in Table 1. Comparison of ROC curves. ROC curves were performed to evaluate the predictive value of four scoring systems for 30-day mortality for all cases in the cohort study (Fig.