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Rationale Prediction of patients at risk for mortality can help triage patients and assist in resource allocation. Objectives Develop and evaluate a machine learning-based algorithm which accurately predicts mortality in COVID-19, pneumonia, and mechanically ventilated patients. Methods Retrospective study of 53,001 total ICU patients, including 9166 patients with pneumonia and 25,895 mechanically ventilated patients, performed on the MIMIC dataset. An additional retrospective analysis was performed on a community hospital dataset containing 114 patients positive for SARS-COV-2 by PCR test. The outcome of interest was in-hospital patient mortality. Results When trained and tested on the MIMIC dataset, the XGBoost predictor obtained area under the receiver operating characteristic (AUROC) values of 0.82, 0.81, 0.77, and 0.75 for mortality prediction on mechanically ventilated patients at 12-, 24-, 48-, and 72- hour windows, respectively, and AUROCs of 0.87, 0.78, 0.77, and 0.734 for mortality prediction on pneumonia patients at 12-, 24-, 48-, and 72- hour windows, respectively. The predictor outperformed the qSOFA, MEWS and CURB-65 risk scores at all prediction windows. When tested on the community hospital dataset, the predictor obtained AUROCs of 0.91, 0.90, 0.86, and 0.87 for mortality prediction on COVID-19 patients at 12-, 24-, 48-, and 72- hour windows, respectively, outperforming the qSOFA, MEWS and CURB-65 risk scores at all prediction windows. Conclusions This machine learning-based algorithm is a useful predictive tool for anticipating patient mortality at clinically useful timepoints, and is capable of accurate mortality prediction for mechanically ventilated patients as well as those diagnosed with pneumonia and COVID-19., Highlights • Mortality predictions have not previously been evaluated for COVID-19 patients. • Machine learning may be a useful predictive tool for anticipating patient mortality. • Prediction can be estimated at clinically useful windows up to 72 h in advance.

Introduction: Acute heart failure (AHF) syndromes are associated with significant morbidity, mortality, and hemodynamic complications. Patients at risk of AHF are likely to experience rehospitalization or death post-discharge. AHF patient risk stratification remains an unmet need. Hypothesis: Gradient boosted machine learning can be used to predict likelihood of AHF diagnosis, enabling identification of at-risk patients. Methods: A gradient boosted machine learning algorithm was developed on retrospective data from 236,275 total emergency department patients, 807 of whom experienced AHF and were prescribed a vasodilator during their stay. A model was developed using age, sex, and six vital signs (systolic and diastolic blood pressure, heart rate, respiratory rate, peripheral oxygen saturation, and temperature). A second model used these demographics, vitals, and laboratory data (troponin, bicarbonate, and O2 saturation) as available to predict AHF diagnosis, as defined by ICD code and prescription of beta-blocker and angiotensin converting enzyme (ACE) inhibitor vasodilators. Model performance was assessed with regard to area under the receiver operating characteristic (AUROC). Results: The algorithm obtained AUROCs of 0.860 and 0.767 for prediction of AHF diagnosis and prescription of vasodilator at discharge with and without laboratory data, respectively (Figure 1). Predictions from the vitals only model were made an average of 38.5 hours prior to AHF diagnosis. Predictions from the model which included labs were made an average of 34 hours prior to AHF diagnosis. Conclusion: Machine learning is a useful method for predicting AHF diagnosis. Patient risk stratification enabled by the tool may serve to reduce duration of hospital stay for patients at low risk of developing AHF. Identification of high risk patient populations may also allow for earlier diagnosis and treatment, preventing development of chronic heart failure and other sequelae of AHF.