COVID-19 Mortality Prediction From Deep Learning in a Large Multistate Electronic Health Record and Laboratory Information System Data Set: Algorithm Development and Validation (Preprint)

Abstract

BACKGROUND COVID-19 is caused by the SARS-CoV-2 virus and has strikingly heterogeneous clinical manifestations with most individuals contracting mild disease but a substantial minority experiencing fulminant cardiopulmonary symptoms or death. The clinical covariates and the lab tests performed on a patient provides robust statistics to guide clinical treatment. Deep learning approaches on a dataset of this nature enables patient stratification and provide methods to guide clinical treatment. OBJECTIVE Here we report on the development and prospective validation of a state-of-the-art machine learning model to provide mortality prediction shortly after confirmation of SARS-CoV-2 infection in the Mayo Clinic patient population. METHODS We constructed one of the largest reported and most geographically diverse laboratory information system (LIS) and electronic health record (EHR) COVID-19 datasets in the published literature, which included 11,808 patients with residence in 41 states, treated at medical sites across five states in three time zones. This data was split by date into an 80/20 training and prospective testing cohort. In the training data, model selection and evaluation were performed using stratified 10-fold cross-validation. Traditional machine learning models were evaluated independently as well as in a stacked learner approach using Autogluon, and various recurrent neural network architectures were considered. We trained these models to operate solely using routine laboratory measurements and clinical covariates available within 72 hours of a patient’s first positive COVID-19 nucleic acid test. RESULTS The GRU-D recurrent neural network achieved peak cross-validation performance with 0.938±0.004 AUROC. In cross-validation, this model provides accuracy of 89% (95% CI: [88,90]), a recall of 80% (95% CI: [74,85]), a precision of 17% (95% CI: [15,19]), a negative predictive value (NPV) of 99% (95% CI: [99,100]), and statistically significant stratification in our Cox proportional hazards survival model (risk 18.9, P<.001). The model retained strong performance when reducing the follow-up time down to 12 hours (0.916±0.005 AUROC), and leave-one-out feature importance analysis indicates the most independently valuable features were: age, Charlson score, minimum oxygen saturation, fibrinogen and serum iron level. In the prospective testing cohort this model provides AUROC of 0.901, an accuracy of 78% (95% CI: [76,79]), a recall of 85% (95% CI: [77,91]), a precision of 14% (95% CI: [12,17]), a negative predictive value (NPV) of 99% (95% CI: [99,100]), and statistically significant difference in survival (P<.001, hazard ratio for those predicted to survive: 95% CI [0.043,0.106]). CONCLUSIONS Our deep learning approach using GRU-D provides an alert system to flag mortality on COVID-19 positive patients, using clinical covariates and lab values within a 72-hour window after the first positive nucleic acid test.