Predictive models can help stratify patient risk during and after COVID-19

A pair of studies published in the Journal of the American Medical Association show how predictive models can assist with risk stratification when it comes to treating patients with COVID-19 and scheduling elective procedures after the pandemic.

The first study, which focused on 2,511 hospitalized COVID-19 patients in eastern Massachusetts, examined how laboratory studies, in conjunction with sociodemographic features and prior diagnosis, helped identify individuals at particularly high risk.

The second study, a Duke University study that developed predictive models from the electronic health records of 42,199 elective surgery patients, found that modeling, along with other factors, can be used to inform how to recommence elective inpatient procedures.   

"The novel coronavirus disease 2019 has changed the provision of hospital- and clinic-based surgical care," noted the authors of the Duke University study.  

"As hospitals prepared for possible surges of infected patients requiring admission and possible intensive care stay, entire institutions and health systems took stock of their resources to meet an uncertain demand," they continued.   

"This included estimating an ever-fluctuating number of available beds, securing sufficient personal protective equipment and ventilators, minimizing staff shortages, and establishing protocols to mitigate against nosocomial infection."