A Deep Learning Approach for Predicting Severity of COVID-19 Patients Using a Parsimonious Set of Laboratory Markers

Abstract

Background: SARS-CoV-2 virus has caused tremendous burden on both patients and providers across the globe. Our focus is to develop a practical and easy to deploy system to predict the severe manifestation of disease in COVID-19 patients with an aim to assist clinicians in triage and treatment decisions. \n \nMethods: We used multiple cohorts from 14,172 COVID-19 patients with captured clinical outcomes from four healthcare systems across the globe. Our proposed predictive algorithm is a trained artificial intelligence-based network using 8,427 patient records. We focused on building a parsimonious model with the fewest possible number of input parameters to facilitate clinical deployment. The model provides a severity risk score along with likelihoods of various clinical outcomes, namely ventilator use, end organ damage, and mortality. \n \nFindings: Model computed severity risk scores using nine laboratory markers taken from 4,293 patients at the initial presentation and the age have the prediction accuracy with the area under the curve (AUC) of 0 · 77 95% CI: 0·76-0·78, and the negative predictive value NPV of 0·87 95% CI: 0·85-0·87 for the need to use a ventilator. Similarly, the model has an accuracy with AUC of 0·83 95% CI: 0·82-0·84, and the NPV of 0·93 95% CI: 0·92-0·94 for predicting in-hospital 30-day mortality. \n \nInterpretations: Our deep learning model has a promising predictive performance in using various laboratory markers taken from patients admitted due to COVID-19 at the initial encounter to directly inform clinical and resource management and allocations, respectively. \n \nFunding: Provided through research and collaboration grants from Siemens Healthineers, Laboratory Diagnostics, Tarrytown, New York, USA. \n \nDeclaration of Interests: VS, DC, JS, ER, RM, SV, DC, and AK are employees of Siemens Healthcare, USA. All other authors have nothing to declare. \n \nEthics Approval Statement: This study was approved by the ethics committees at Hospital University of La Paz, Emory University Hospital, and Houston Methodist Hospital.