Risk assessment of acute respiratory failure requiring advanced respiratory support using machine learning

Abstract

Abstract Background: Acute respiratory failure (ARF) presents within a spectrum of clinical manifestations and illness severity, and mortality occurs in approximately 30% of patients who develop ARF. Early risk identification is imperative for implementation of prophylactic measures prior to ARF onset. In this study, we develop and validate a machine learning algorithm (MLA) to predict patients at risk of ARF requiring advanced respiratory support. Methods: This retrospective study used data from 155,725 patient electronic health records obtained from five United States community hospitals. An XGBoost classifier was developed using patient EHR data to produce risk scores at 3-hour intervals to predict the risk of ARF within 24 hours. An alert was generated only once prior to ARF onset, defined by implementation of advanced respiratory support, for patients whose risk score exceeded a predefined threshold. We used a novel time-sensitive area under the receiver operating characteristic (tAUROC) curve that integrated the timing of the alert relative to ARF onset to evaluate the accuracy of the MLA. The MLA was assessed on two testing sets and compared with oxygen saturation (SpO2) measurement and the modified early warning score (MEWS). Results: The MLA demonstrated significantly higher eSensitivity and specificity operating points on the temporal testing and external validation sets (tAUROC of 0.858/0.883, respectively) than SpO2 (0.771/0.810) and MEWS (0.676/0.774) for prediction of ARF requiring advanced respiratory support. The MLA also achieved lower false positive rates than SpO2 and MEWS at these operating points. Conclusions: The MLA predicts patients at risk of ARF requiring advanced respiratory support and achieves higher accuracy and produces earlier alerts than the use of SpO2 or MEWS. Importantly for clinical practice, the MLA has a lower false positive rate than these comparators while maintaining high sensitivity and specificity.