Sepsis is a life-threatening type of organ dysfunction caused by a dysregulated host response to an infection. Using the model in clinical practice might help to identify patients at risk of sepsis in a time window that enables personalized intervention and early treatment. In comparison, the logistic regression model achieved lower performance, with AUROC ranging from 0.76 to 0.84 at the 4-to-24-hour prediction window.Ĭonclusions: The machine learning–based model had good discrimination and calibration performance for sepsis prediction in critical trauma patients. The DCA showed our model had a positive net benefit in the threshold probability range of 0 to 0.6. With a ratio of 9 false alerts for every true alert, it predicted 73% (386/529) of sepsis-positive timesteps and 91% (163/179) of sepsis events in the subsequent 6 hours. The XGBoost model achieved an area under the receiver operating characteristics curve (AUROC) ranging from 0.83 to 0.88 at the 4-to-24-hour prediction window in the test set. Results: We included 4603 trauma patients in the study, 1196 (26%) of whom developed sepsis. In addition, we trained an L2-regularized logistic regression model to compare its performance with XGBoost. A Shapley additive explanation algorithm was applied to show the effect of features on the prediction model. Clinical applicability of the model was evaluated with varying levels of precision, and the potential clinical net benefit was assessed with decision curve analysis (DCA). We evaluated model performance for discrimination and calibration both at time-step and outcome levels.
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An Extreme Gradient Boosting (XGBoost) model was developed to predict the hourly risk of sepsis at prediction windows of 4, 6, 8, 12, and 24 hours. The data set was randomly split into 70% for model development with stratified 5-fold cross-validation, 15% for calibration, and 15% for testing. We further derived a total of 485 features, including measurement pattern features, scoring features, and time-series variables, from the raw variables by feature engineering. A total of 42 raw variables were collected, including demographics, vital signs, arterial blood gas, and laboratory tests. Methods: We extracted data from adult trauma patients admitted to the ICU at Beth Israel Deaconess Medical Center between 20. Objective: To develop a machine learning model to predict the risk of sepsis at an hourly scale among ICU-admitted trauma patients. Machine learning–based predictive modeling has shown great promise in evaluating and predicting sepsis risk in the general intensive care unit (ICU) setting, but there has been no sepsis prediction model specifically developed for trauma patients so far. A hypermetabolic baseline and explosive inflammatory immune response mask clinical signs and symptoms of sepsis in trauma patients, making early diagnosis of sepsis more challenging. School of Public Health and Key Laboratory of Public Health Safetyīackground: Sepsis is a leading cause of death in patients with trauma, and the risk of mortality increases significantly for each hour of delay in treatment.
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