Researchers at New York City-based Mount Sinai Health System developed a COVID-19 mortality predictive model that can accurately and cost-effectively aid clinical staff in assessing COVID-19 patients’ risk of death, according to a study published in the October 2020 issue of The Lancet.
Using what the research team called “the largest clinical dataset to date,” they analyzed data from 5,051 Mount Sinai COVID-19 patients by deploying machine learning algorithms that focused on three clinical features: age, minimum oxygen saturation over the span of the medical encounter and type of encounter (inpatient, outpatient or telehealth).
“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,” Gaurav Pandey, PhD, a member of the research team, said in the study. “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.”
The predictive model produced a vital sign that can be easily integrated into clinical staff’s workflows, allowing them to continually assess COVID-19 patients’ needs. The tool can flag patients with a high mortality risk so healthcare personnel can intervene more promptly to prevent death.
More articles on data analytics:
UCSF asks public to share their Google location data to improve contact tracing
12-health system consortium to answer public’s COVID-19 questions using patient data
Data unicorn debuts $3.4 billion IPO: 6 things to know about Snowflake
© Copyright ASC COMMUNICATIONS 2020. Interested in LINKING to or REPRINTING this content? View our policies by clicking here.