A team of data scientists from the University of Michigan has developed an app to help physicians compare the risk to a cancer patient from a months-long postponement of care to the additional risk posed by a potential COVID-19 infection if they undergo surgery, chemotherapy and/or radiation, according to a press release.
The OncCOVID app draws on large, national cancer data sets to help assess the risk from of immediate treatment versus delayed treatment, depending on a patient’s individual characteristics, as well as on COVID’s impact on their local community.
The researchers — from U-M’s Rogel Cancer Center and School of Public Health— envision OncCOVID being used by doctors to help identify patients whose risk from COVID is outweighed by the benefits of immediate treatment.
The app allows doctors to enter more than 45 characteristics about a patient — including their age, location, cancer type and stage, treatment plan, underlying medical conditions, and the proposed length of a delay in care. It then calculates the patient’s likely five-year survival following immediate treatment and delayed treatment.
Meanwhile, OncCOVID could also be used by healthcare systems that are ramping services back up and need to prioritize a backlog of patients whose treatment was put on hold due to the pandemic, says Daniel Spratt, associate professor of radiation oncology at Michigan Medicine and one of the senior researchers on the project.
The app draws on millions of records contained in the National Cancer Institute’s Surveillance, Epidemiology, and End Results (SEER) registry and the National Cancer Database, combined with county-level COVID infection data from Johns Hopkins University.
In some cases, the personalized risk assessments run counter to the generic, three-tiered approach.
For example, Spratt says, the team’s model shows that an otherwise healthy 45-year-old woman from Ann Arbor, MI. with stage 1 breast cancer actually has a slightly higher risk of dying over the next five years if treatment is delayed by more than three months than if she’s treated immediately.
Conversely, under the team’s data model, a 70-year-old woman from New York City — where COVID infections have been high — with stage 1 breast cancer and several underlying health conditions would be put at significantly more risk by undergoing immediate treatment than the risk posed by a three-month delay.
