A recent study using electronic health records from the National Institutes of Health’s RECOVER Initiative, analyzed the persistence of symptoms after SARS-CoV-2 infection (long COVID) among diverse populations in the United States. The findings emphasized the need for further investigation to improve the diagnosis and treatment of the disease.
The study, which was recently published in Nature Communications, examined electronic health records as a component of the National Institutes of Health’s RECOVER Initiative. This research aimed to gain a deeper understanding of lingering symptoms following a SARS-CoV-2 infection, commonly referred to as long COVID, across a wide range of diverse populations.
Headed by Dr. Rainu Kaushal, the chair of the Department of Population Health Sciences at Weill Cornell Medicine and physician-in-chief of population health sciences at New York-Presbyterian Hospital/Weill Cornell Medical Center, the study offers insights into the possible symptoms experienced after an acute COVID-19 infection and how the likelihood of these conditions may differ among various population groups in the United States.
“Long COVID is a new disease that is very complicated and quite difficult to characterize,” said Dr. Chengxi Zang, an instructor in population health sciences at Weill Cornell Medicine and lead author on the paper. “It affects multiple organs and presents a severe burden to society, making it urgent that we define this disease and determine how well that definition applies among different populations. This paper provides the basis for furthering research on long COVID.”
The team studied electronic health records from two clinical research networks that are part of the National Patient-Centered Clinical Research Network (PCORnet). One dataset, from the INSIGHT Clinical Research Network—which Dr. Kaushal leads—included data from 11 million New York-based patients, while the other came from the OneFlorida+ network, which included 16.8 million patients from Florida, Georgia, and Alabama.
The team identified a broad list of diagnoses that occurred more frequently in patients who had recently had COVID compared with non-infected individuals. The researchers also found more types of symptoms and a higher risk of long COVID in New York City than in Florida. Specific conditions found across the New York City and Florida populations included dementia, hair loss, sores in the stomach and small intestine, blood clots in the lung, chest pain, abnormal heartbeat, and fatigue.
“Our approach, which uses machine learning with electronic health records, provides a data-driven way to define long COVID and determine how generalizable our definition of the disease is,” Dr. Zang said. Comparing records across diverse populations in regions that experienced the COVID-19 pandemic differently highlighted how variable long COVID is for patients and emphasized the need for further investigation to improve the diagnosis and treatment of the disease.
Some of the differences between the results from the two populations might be explained by the fact that New York City had a more diverse patient population, endured one of the first waves of the pandemic and faced the lack of personal protective equipment such as masks, compared with Florida, Dr. Zang said.
The new study is related to previous work by Dr. Kaushal, who is also senior associate dean for clinical research and the Nanette Laitman Distinguished Professor of Population Health Sciences at Weill Cornell Medicine, and colleagues, that categorized different subtypes of long COVID.
“In this new research, we examined a broad list of potential long COVID conditions one by one,” said Dr. Fei Wang, associate professor of population health sciences and co-senior author of the study. “These findings can help us better recognize the broad involvement of multiple organ systems in long COVID, and design appropriate plans for patient management and treatment development.”
Reference: “Data-driven analysis to understand long COVID using electronic health records from the RECOVER initiative” by Chengxi Zang, Yongkang Zhang, Jie Xu, Jiang Bian, Dmitry Morozyuk, Edward J. Schenck, Dhruv Khullar, Anna S. Nordvig, Elizabeth A. Shenkman, Russell L. Rothman, Jason P. Block, Kristin Lyman, Mark G. Weiner, Thomas W. Carton, Fei Wang and Rainu Kaushal, 7 April 2023, Nature Communications.
DOI: 10.1038/s41467-023-37653-z
So that’s what 1 billion gets you, it’s complicated, we used Ai to the work for us.