A Computational Model to Predict Brain Trauma Outcome in the Intensive Care Unit

Objectives To predict short-term outcomes of critically ill patients with traumatic brain injury (TBI) by training machine learning classifiers on two large intensive care databases Design Retrospective analysis of observational data. Patients Patients in the multicenter Philips eICU and single-center Medical Information Mart for Intensive Care–III (MIMIC-III) databases with a primary admission diagnosis of TBI, who were in intensive care for over 24 hours. Interventions None. Measurements and Main Results We identified 1,689 and 126 qualifying TBI patients in eICU and MIMIC-III, respectively. Generalized Linear Models were used to predict mortality and neurological function at ICU discharge using features derived from clinical, laboratory, medication and physiological time series data obtained in the first 24 hours after ICU admission. Models were trained, tested and validated in eICU then validated externally in MIMIC-III. Model discrimination determined by area under the receiver operating characteristic curve (AUROC) analysis was 0.903 and 0.874 for mortality and neurological function, respectively. Performance was maintained when the models were tested in the independent MIMIC-III dataset (AUROC 0.958 and 0.878 for mortality and neurological function, respectively). Conclusions Computational models trained with data available in the first 24 h after admission accurately predict discharge outcomes in ICU stratum TBI patients.