Abstract
Early detection of patients at risk for clinical deterioration is crucial for timely intervention. Traditional detection systems rely on a limited set of variables and are unable to predict the time of decline. We describe a machine learning model called MEWS++ that enables the identification of patients at risk of escalation of care or death six hours prior to the event. A retrospective single-center cohort study was conducted from July 2011 to July 2017 of adult (age > 18) inpatients excluding psychiatric, parturient, and hospice patients. Three machine learning models were trained and tested: random forest (RF), linear support vector machine, and logistic regression. We compared the models’ performance to the traditional Modified Early Warning Score (MEWS) using sensitivity, specificity, and Area Under the Curve for Receiver Operating Characteristic (AUC-ROC) and Precision-Recall curves (AUC-PR). The primary outcome was escalation of care from a floor bed to an intensive care or step-down unit, or death, within 6 h. A total of 96,645 patients with 157,984 hospital encounters and 244,343 bed movements were included. Overall rate of escalation or death was 3.4%. The RF model had the best performance with sensitivity 81.6%, specificity 75.5%, AUC-ROC of 0.85, and AUC-PR of 0.37. Compared to traditional MEWS, sensitivity increased 37%, specificity increased 11%, and AUC-ROC increased 14%. This study found that using machine learning and readily available clinical data, clinical deterioration or death can be predicted 6 h prior to the event. The model we developed can warn of patient deterioration hours before the event, thus helping make timely clinical decisions.
Highlights
Detection of patient deterioration and prompt clinical intervention is key to lowering the potentially preventable morbidity and mortality among hospital inpatients [1,2]
There was a significant variation of unexpected escalation rate seen between Major Diagnostic Categories (MDCs) categories (Supplementary Figure S2)
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Summary
Detection of patient deterioration and prompt clinical intervention is key to lowering the potentially preventable morbidity and mortality among hospital inpatients [1,2]. There are some limitations to this approach: (1) the schemas of these scores are usually defined manually; (2) alarm triggers rely on empirically chosen values; (3) the thresholds are usually set to capture the greatest percentage of clinically significant events, resulting in non-specific alerts that include a large number of false alarms. This creates an excess of warning notifications that can generate alarm fatigue [9,10,11]. The usefulness of these systems is limited by inability to quantify the risk for decompensation and the lack of a defined time window for intervention
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