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September 8, 2020

New Analytics Tools Predict COVID-19 Patient Mortality


One of the most urgent needs in the care of patients with COVID-19 is a better understanding of which patients will require more intensive treatment and attention. Now, researchers from Oklahoma State University’s Center for Health Systems Innovation (CHSI) are applying big data analytics to build predictive models of COVID-19 patient risk that could help physicians better manage patient care during the pandemic.

Zhuqi Miao (the health data science program manager at CHSI) and Meghan Sealey (a doctoral student studying statistics at Oklahoma State) worked with anonymized data from nearly 19,000 COVID-19 patients from healthcare IT firm Cerner’s COVID datasets. Using this data, they developed two tools for modeling mortality risk: one based on patient data at time of admission, and one based on patient data from the first data of hospitalization.

“The models identified a similar set of medical conditions suggested by the Centers for Disease Control and Prevention as the essential risk factors for death, such as history of diabetes, respiratory disorders and hypertension, and onset of respiratory or kidney failures,” Miao said, “but we also found some unique ones.”

Using these tools, the researchers say they are able to accurately predict mortality for almost 70% of patients (with the first model) and nearly 75% of patients (with the second model). The team sees a slew of benefits in store for the healthcare industry if it were to deploy such tools in the field.

“These kinds of analytic tools are the wave of the future to diagnose, stage and monitor disease progression and save lives,” said William Paiva, CHSI’s executive director. “It can also help alleviate the financial burdens for both patients and health care systems alike. CHSI is one of the nation’s leading organizations leading the charge in this brand-new industry, and you can expect to see more and more of these exciting technologies coming out of our center.”

“There is an urgent need to determine which COVID patients are at highest risk for bad clinical outcomes as early as possible so that plans and actions can be made to save more lives,” added Miao. “With the rise in reported COVID cases in the U.S., there is no time better than right now to have a tool to predict which patients are most at risk as soon as possible.”