A simple electronic medical record-based predictors of illness severity in sepsis (sepsis) score

• Published in: PloS one 2024-06, Vol.19 (6), p.e0299473
• Main Authors: Cressman, Alex M, Wen, Bijun, Saha, Sudipta, Jun, Hae Young, Waters, Riley, Lail, Sharan, Jabeen, Aneela, Koppula, Radha, Lapointe-Shaw, Lauren, Sheehan, Kathleen A, Weinerman, Adina, Daneman, Nick, Verma, Amol A, Razak, Fahad, MacFadden, Derek
• Format: Article
• Language: English
• Subjects: Aged; Aged, 80 and over; Antibiotics; Biology and Life Sciences; Canada; Clinical decision making; Cohort analysis; Computer and Information Sciences; Electronic Health Records; Electronic medical records; Female; Health aspects; Health care expenditures; Health care networks; Health sciences; Hospital Mortality; Hospital patients; Hospitalization; Hospitals; Humans; Illnesses; Infection; Intensive Care Units; Length of Stay; Male; Medical records; Medicine and Health Sciences; Middle Aged; Mortality; Ontario; Patients; Population studies; Research and Analysis Methods; Research ethics; Retrospective Studies; ROC Curve; Sepsis; Sepsis - diagnosis; Sepsis - mortality; Severity of Illness Index; Social Sciences; Teaching hospitals; Vital signs
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0299473

Current scores for predicting sepsis outcomes are limited by generalizability, complexity, and electronic medical record (EMR) integration. Here, we validate a simple EMR-based score for sepsis outcomes in a large multi-centre cohort. A simple electronic medical record-based predictor of illness severity in sepsis (SEPSIS) score was developed (4 additive lab-based predictors) using a population-based retrospective cohort study. Internal medicine services across four academic teaching hospitals in Toronto, Canada from April 2010-March 2015 (primary cohort) and 2015-2019 (secondary cohort). We identified patients admitted with sepsis based upon receipt of antibiotics and positive cultures. The primary outcome was in-hospital mortality and secondary outcomes were ICU admission at 72 hours, and hospital length of stay (LOS). We calculated the area under the receiver operating curve (AUROC) for the SEPSIS score, qSOFA, and NEWS2. We then evaluated the SEPSIS score in a secondary cohort (2015-2019) of hospitalized patients receiving antibiotics. Our primary cohort included 1,890 patients with a median age of 72 years (IQR: 56-83). 9% died during hospitalization, 18.6% were admitted to ICU, and mean LOS was 12.7 days (SD: 21.5). In the primary and secondary (2015-2019, 4811 patients) cohorts, the AUROCs of the SEPSIS score for predicting in-hospital mortality were 0.63 and 0.64 respectively, which were similar to NEWS2 (0.62 and 0.67) and qSOFA (0.62 and 0.68). AUROCs for predicting ICU admission at 72 hours, and length of stay > 14 days, were similar between scores, in the primary and secondary cohorts. All scores had comparable calibration for predicting mortality. An EMR-based SEPSIS score shows a similar ability to predict important clinical outcomes compared with other validated scores (qSOFA and NEWS2). Because of the SEPSIS score's simplicity, it may prove a useful tool for clinical and research applications.