Given the toll that the COVID-19 pandemic has taken on people’s health and lives worldwide, it is crucial to be able to accurately predict patients’ outcomes, including their chances of mortality from the disease. Using the largest clinical dataset to date, and a systematical machine learning framework, the research team at Mount Sinai identified an accurate and parsimonious prediction model of COVID-19 mortality.
This model was based on only three routinely collected clinical features, namely patient’s age, minimum oxygen saturation over the course of their medical encounter, and type of patient encounter (inpatient vs outpatient and telehealth visits).
This model could yield an additional “vital sign” that is assessed regularly during a patient’s hospital course, that can be integrated into the clinical care flow of a COVID-19 patient. Clinical teams could use results from the prediction model throughout COVID-19 patients’ hospital courses to flag individuals at high risk of death so that they can promptly focus treatment and attention on such individuals to prevent their mortality.
Using the largest development dataset yet (n=3841), and a systematic machine learning framework, we developed a COVID-19 mortality prediction model that showed high accuracy (AUC=0·91) when applied to test datasets of retrospective (n=961) and prospective (n=249) patients. This model was based on three clinical features: patient’s age, minimum oxygen saturation over the course of their medical encounter, and type of patient encounter (inpatient vs outpatient and telehealth visits).
“Predicting mortality among patients with COVID-19 who present with a spectrum of complications is very difficult, hindering the prognostication and management of the disease,” said Dr. Gaurav Pandey, Assistant Professor of Genetics and Genomic Sciences. “We aimed to develop an accurate prediction model of COVID-19 mortality using unbiased computational methods, and identify the clinical features most predictive of this outcome.”
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