A new machine learning tool aims to help scientists investigate why some people develop long COVID, a series of debilitating, chronic symptoms that last months to years after the initial COVID-19 infection. Developed by a team of researchers from institutions across the country, led by Justin Reese of Berkeley Lab and Peter Robinson of Jackson Lab, the software analyzes entries in electronic health records (EHRs) to find symptoms in common between people who have been diagnosed with long COVID and to define subtypes of the condition. The research, which is described in a new paper in eBioMedicine, also identified strong correlations between different long COVID subtypes and pre-existing conditions such as diabetes and hypertension. According to Reese, a computer research scientist in Berkeley Lab's Biosciences Area, this research will help improve our understanding of how and why some individuals develop long COVID symptoms and may enable more effective treatments by helping clinicians develop tailored therapies for each group. For example, the best treatment for patients experiencing nausea and abdominal pain might be quite different from a treatment for those suffering from persistent cough and other lung symptoms.

Clinical scientists have explored de-identified electronic health record data in the National COVID Cohort Collaborative(N3C), a National Institutes of Health-funded national clinical database, using machine learning models to help decipher characteristics of individuals with long COVID and attributes that may help identify such patients using information from medical records. The discoveries published in The Lancet Digital Health have the potential to enhance clinical research on extended COVID and inspire a more consistent COVID treatment regimen. The author Emily R. Pfaff, Ph.D., an assistant professor in the UNC School of Medicine's Division of Endocrinology and Metabolism, said that characterizing, diagnosing, treating, and caring for long COVID patients has turned out to be difficult owing to the list of characteristic symptoms constantly evolving over time. They needed to better grasp the intricacies of long COVID, and it made sense to use current data analysis methods and a unique, extensive data resource like N3C, which represents many of the properties of long COVID. The N3C data enclave, funded by the National Institutes of Health's National Center for Advancing Translational Sciences (NCATS), already has information on more than 13 million people from 72 locations, including approximately 5 million COVID-19-positive patients.

In a recent study posted to Preprints with The Lancet*, researchers developed a machine learning approach to identify patients with long coronavirus disease (COVID). The post-acute sequelae of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection are called long COVID. In the present study, researchers aimed to generate a robust clinical definition for long COVID using data related to long COVID patients. The team utilized data obtained from electronic health records that were integrated and harmonized in the secure N3C Data Enclave. This allowed the team to identify unique patterns and clinical characteristics among COVID-19-infected patients.

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 not some artifact that's just happening because of the people that we're looking at within the electronic health record? 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.