Researchers test the power of machine learning to unravel long Covid’s mysteries
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.
“People have to really think carefully about what does this mean,” said Zack Strasser, an internist at Massachusetts General Hospital who has used existing patient records to study the characteristics of long Covid. “Is this true? Is this not some artifact that’s just happening because of the people that we’re looking at within the electronic health record? Because there are biases.”
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.
“There’s a concern that trials relating to long Covid are going to not be that successful,” said Melissa Haendel, a health informatics researcher at the University of Colorado Anschutz Medical Campus and co-lead of N3C, because its definition is still so diffuse.
Supporting more targeted recruitment is what Pfaff calls the project’s “sweet spot.” She and her colleagues hope that machine learning models could help identify potential participants who would otherwise be missed or underrepresented in prospective research. And by using algorithmic approaches to narrow down a cohort of people who are more likely to have long Covid, said Pfaff, “a research coordinator who’s making calls to potential participants is making calls from a list of 200 patients, rather than 2 million patients.”
That effort is still a work in progress. The team’s first stab at building an algorithm that could identify long Covid patients, released in a preprint now accepted at the Lancet Digital Health, had its limitations. At that point, “there was literally no structured way for a physician to enter ‘I think this patient has long Covid’ in their EHR,” said Pfaff. “We had to get creative and find a proxy.” They settled on records from about 500 patients who showed up at three long Covid specialty clinics.
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