Development and evaluation of a multi-source machine learning model to predict critical illness and death in patients with substance misuse.

Objectives: Patients with substance misuse are at high risk for clinical deterioration, and pre-hospital encounters constitute important risk factors. We sought to incorporate these risk factors into a novel prediction model using linked electronic health record, emergency medical services (EMSs), and claims data. Materials and methods: Using 23 454 hospital encounters, we developed machine-learning models to predict mechanical ventilation, vasopressor initiation, or death within 12 hours of a vital sign or laboratory measurement. Models were compared to the modified early warning score. Results: Extreme gradient boosting achieved an area under the receiver operating characteristic curve of 0.93, significantly outperforming modified early warning score (MEWS 0.79). Notably, removing EMS and claims data did not reduce predictive performance. Discussion: The model demonstrated strong predictive performance of clinical deterioration in this high-risk population. Incorporation of pre-hospital risk factors did not improve performance beyond EHR data alone. Conclusion: Electronic health record-based machine learning models can support accurate short-term prediction of clinical deterioration among this population at risk for delayed recognition.