Abstract Early detection of patients at risk for MET (Medical Emergency Team) activations is crucial for timely intervention. Threshold-based reference model used in general wards has low sensitivity in detecting clinical deterioration. Its fixed thresholds make early trend prediction difficult, whereas a predicted probability approach provides a more dynamic assessment, where a higher probability indicates a higher risk of deterioration. We describe a machine learning model that enables the identification of patients at risk of clinical deterioration or death 22 h prior to the event, which is 10 h earlier than the current reference (baseline). A retrospective single-center Asian cohort study was conducted from 2019 to 2020 of inpatients in Singapore. A total of 2755 patients with 6496 cases/admissions were included. An XGBoost (XGB) model was trained and tested to compare the performance of the machine learning model to the reference model using sensitivity, specificity, and area under the curve for receiver operating characteristic (AUC-ROC). The XGB model with all vital signs shows highest ROC-AUC (91.7%) and good balance between sensitivity (88.7%) and specificity (88%). The reference model shows highest specificity (98.5%), but very low sensitivity (74.2%) and moderate ROC-AUC (86.4%). The key advantages of machine learning models are they can predict clinical deterioration with higher accuracy (more than 12%) and 10 h earlier than the reference model. In addition, machine learning models require less vital sign inputs for prediction where the reference model requires all vital sign signals. This study also finds that respiration rate and oxygen saturation are primary features in early prediction of clinical deterioration. This aligns with clinical knowledge that abnormalities in these vital signs often serve as early indicators of critical conditions such as respiratory failure or sepsis.
Taha et al. (Sun,) studied this question.