Less is more: Detecting clinical deterioration in the hospital with machine learning using only age, heart rate, and respiratory rate

Aim: We sought to develop a machine learning analytic (eCART Lite) for predicting clinical deterioration using only age, heart rate, and respiratory data, which can be pulled in real time from patient monitors and updated continuously without need for additional inputs or cumbersome electronic health record integrations. Methods: We utilized a multicenter dataset of adult admissions from five hospitals. We trained a gradient boosted machine model using only current and 24-hour trended heart rate, respiratory rate, and patient age to predict the probability of intensive care unit (ICU) transfer, death, or the combined outcome of ICU transfer or death. The area under the receiver operating characteristic curve (AUC) was calculated in the validation cohort and compared to those for the Modified Early Warning Score (MEWS), National Early Warning Score (NEWS), and eCARTv2, a previously-described, 27-variable, cubic spline, logistic regression model without trends. Results: Of the 556,848 included admissions, 19,509 (3.5%) were transferred to an ICU and 5764 (1.0%) died within 24 hours of a ward obser-vation. eCART Lite significantly outperformed the MEWS, NEWS, and eCART v2 for predicting ICU transfer (0.79 vs 0.71, 0.74, and 0.78, respec-tively; p < 0.01) and the combined outcome (0.80 vs 0.72, 0.76, and 0.79, respectively; p < 0.01). Two of the strongest predictors were respiratory rate and heart rate. Conclusion: Using only three inputs, we developed a tool for predicting clinical deterioration that is similarly or more accurate than commonly-used algorithms, with potential for use in inpatient settings with limited resources or in scenarios where low-cost tools are needed.