Most of us know someone who is experiencing physical issues months after they have had Covid.”
Symptoms range from “brain fog” and other neurological problems to fatigue, fever, and headaches, to name a few. Symptoms last various lengths of time and can be confused with other health issues.
NIH Covid 19, 5-16-22 News Release
Scientists identify characteristics to better define long COVID.
Using machine learning, researchers find patterns in electronic health record data to better identify those likely to have the condition.
NIH (National Institutes of Health) has been researching the answers. Scientists analyzed de-identified electronic health records in a national centralized public database and found possible LONG COVID cases. There were more than 100,000 cases as of October 202l and as of May 2022, more than 200,000. This database is in the National COVID Cohort Collaborative (NEC) a national, centralized public database led by NIH’s National Center for Advancing Translational Sciences (NCATS).
Emily Pfaff, Ph.D, clinical informaticist at the University of North Carolina, Chapel Hill said
“It made sense to take advantage of modern data analysis tools and a unique big data resource like N3C, where many features of long COVID can be represented.”
Of the 13 milllion people included in the N3C database, about 5 million are COVID positive cases. The N3C enabling research of vaccines, therapies risk factors and health outcomes will be included in the larger trans-NIH initiative, Researching COVID to Enhance Recovery (RECOVER), with the goal to improve the understanding of the long-term effects of COVID-19, called post-acute sequelae of SARS-CoV-2 infection (PASC). RECOVER will accurately identify people with PASC and develop approaches for its prevention and treatment. The program also will answer critical research questions about the long-term effects of COVID through clinical trials, longitudinal observational studies, and more.
Melissa Haendel, Ph.D., and her colleagues at the University of Colorado Anschutz Medical Campus, in the Lancet study, examined in the N3C database 97,995 adult Covid patient’s demographics, health care use, diagnoses and medications. Using this information, along with data on nearly 600 long COVID patients from three long COVID clinics, they created three machine learning models to identify long COVID patients.
Josh Fessel, M.D., Ph.D., senior clinical advisor at NCATS and a scientific program lead in RECOVER:
“Once you’re able to determine who has long COVID in a large database of people, you can begin to ask questions about those people. Was there something different about those people before they developed long COVID? Did they have certain risk factors? Was there something about how they were treated during acute COVID that might have increased or decreased their risk for long COVID?”
The models searched for common features, including new medications, doctor visits and new symptoms, in patients with a positive COVID diagnosis who were at least 90 days out from their acute infection. The models identified patients as having long COVID if they went to a long COVID clinic or demonstrated long COVID symptoms and likely had the condition but hadn’t been diagnosed.
Melissa Haendel, Ph.D., University of Colorado Anschutz Medical:
“We want to incorporate the new patterns we’re seeing with the diagnosis code for COVID and include it in our models to try to improve their performance. The models can learn from a greater variety of patients and become more accurate. We hope we can use our long COVID patient classifier for clinical trial recruitment.”
This is a brief recap. For the full article, GO HERE