The 4-Hour Cairns Sepsis Model: A novel approach to predicting sepsis mortality at intensive care unit admission

Abstract

Background: Sepsis commonly causes intensive care unit (ICU) mortality, yet early identification of adults with sepsis at risk of dying in the ICU remains a challenge. Objective: The aim of the study was to derive a mortality prediction model (MPM) to assist ICU clinicians and researchers as a clinical decision support tool for adults with sepsis within 4 h of ICU admission. Methods: A cohort study was performed using 500 consecutive admissions between 2014 and 2018 to an Australian tertiary ICU, who were aged !18 years and had sepsis. A total of 106 independent variables were assessed against ICU episode-of-care mortality. Multivariable backward stepwise logistic regression derived an MPM, which was assessed on discrimination, calibration, fit, sensitivity, specificity, and predictive values and bootstrapped. Results: The average cohort age was 58 years, the Acute Physiology and Chronic Health Evaluation III-j severity score was 72, and the case fatality rate was 12%. The 4-Hour Cairns Sepsis Model (CSM-4) consists of age, history of renal disease, number of vasopressors, Glasgow Coma Scale, lactate, bicarbonate, aspartate aminotransferase, lactate dehydrogenase, albumin, and magnesium with an area under the receiver operating characteristic curve of 0.90 (95% confidence interval ¼ 0.84e0.95, p < 0.00001), a Nagelkerke R 2 of 0.51, specificity of 0.94, a negative predictive value of 0.98, and almost identical odds ratios during bootstrapping. The CSM-4 outperformed existing MPMs tested on our data set. The CSM-4 also performed similar to existing MPMs in their derivation papers whilst using fewer, routinely collected, and inexpensive variables. Conclusions: The CSM-4 is a newly derived MPM for adults with sepsis at ICU admission. It displays excellent discrimination, calibration, fit, specificity, negative predictive value, and bootstrapping values whilst being easy to use and inexpensive. External validation is required.

Machine learning for the prediction of sepsis-related death: a systematic review and meta-analysis

“…Among these models, 25 studies built 64 machine learning models for predicting in-hospital death [ 22 , 27 , 31 , 33 , 35 , 43 , 45 , 46 , 48 , 49 , 52 – 55 , 58 , 60 , 61 , 63 , 66 , 68 , 69 , 71 , 74 , 75 ], 21 studies built 30 machine learning models for predicting death within 1 month [ 28 – 30 , 34 , 37 – 39 , 41 , 44 , 47 , 48 , 50 , 51 , 55 , 56 , 59 , 65 , 68 , 72 , 73 ], two studies built two machine learning models for predicting death within 3 months [ 32 , 71 ], other two studies built two machine learning models for predicting death within 1 year [ 62 , 70 ], and 4 studies built 6 machine learning models that did not specifically describe the time of sepsis death [ 40 , 42 , 64 , 67 ], these models are depicted in Fig. 2 .…”