Pfaff discusses how machine learning models could help in long Covid research

Published in STAT News on April 29, 2022

Researchers test the power of machine learning to unravel long Covid's mysteries

by Katie Palmer

Long Covid, with its constellation of symptoms, is proving a challenging moving target for researchers trying to conduct large studies of the syndrome. As they take aim, they're debating how to responsibly use growing piles of real-world data — drawing from the full experiences of long Covid patients, not just their participation in stewarded clinical trials.

One of the largest sources of real-world data on long Covid is a first-of-its-kind centralized federal database of electronic health records called the National Covid Cohort Collaborative, or N3C. Kickstarted as part of a $25 million National Institutes of Health award early in the pandemic, N3C now includes deidentified patient data from 72 sites around the country, representing 13 million patients and nearly 5 million Covid cases.

"If we are able to identify these sort of constellations of symptoms that make up these potential long Covid subtypes then, first of all, we might find out that long Covid is not one disease, but it's five diseases or 10 diseases," said Emily Pfaff, who co-leads the long Covid working group at N3C. The real-world data effort has garnered additional funding as part of RECOVER, the four-year NIH initiative to study long Covid, to more precisely characterize the syndrome.

That work has started to trace a clearer image of long Covid, most recently describing co-occurring clusters of cardiopulmonary, neurological, and metabolic diagnoses. But a firmer definition of the syndrome could also potentially support recruitment efforts for critical long Covid trials, some of which have been slow to make progress.

Read more of this article at statnews.com