Long-term COVID risk and symptoms play out very differently in different populations, study finds

by admin
Long-term COVID risk and symptoms play out very differently in different populations, study finds

Although the peak of the COVID-19 pandemic appears to have passed, the effects of post-COVID conditions on public health remain. A new study led by Weill Cornell Medicine and NewYork-Presbyterian researchers has found that the risk of prolonged COVID and its symptoms present very differently in different populations and suggests that further research is needed to accurately define the disease and improve diagnosis and treatment. .

The study, published on April 7 in Nature Communications, analyzed electronic health records as part of the National Institutes of Health’s COVID Research to Enhance Recovery (RECOVER) initiative to better understand the persistence of symptoms after infection with SARS-CoV-2, also known as long COVID, among wide and diverse populations. Led by Dr. Rainu Kaushal, chair of the Division of Population Health Sciences at Weill Cornell Medicine and chief of population health sciences at New York-Presbyterian Hospital/Weill Cornell Medical Center, the study provides an overview of the potential symptoms following acute COVID- 19 and how the risk of these conditions may vary among different populations in the United States.

Long-term COVID is a new disease that is very complex and quite difficult to characterize. It affects multiple organs and poses a serious burden to society, making it urgent to define this disease and determine how well this definition applies in different populations. This paper provides the foundation for further research into long-term COVID.”

Dr. Chengxi Zang, instructor of population health sciences at Weill Cornell Medicine and lead author of the paper

The team examined electronic health records from two clinical research networks that are part of the National Patient-Centered Clinical Research Network (PCORnet). One data set from the INSIGHT Clinical Research Network, led by Dr. Kaushal, includes data from 11 million patients based in New York, while the other comes from the OneFlorida+ network, which includes 16.8 million patients from Florida, Georgia and Alabama . The team identified a broad list of diagnoses that were more common in patients who had recently had COVID compared to uninfected individuals. The researchers also found more types of symptoms and a higher risk of prolonged COVID in New York than in Florida. Specific conditions found in the New York and Florida populations included dementia, hair loss, ulcers in the stomach and small intestine, blood clots in the lungs, chest pain, abnormal heart rhythms and fatigue.

“Our approach, which uses machine learning with electronic health records, provides a data-driven way to define long-term COVID and determine how generalizable our definition of the disease is,” said Dr. Zhang. Comparing records among different populations in regions that experienced the COVID-19 pandemic differently highlighted how variable the duration of COVID is for patients and highlighted the need for further research to improve diagnosis and treatment of the disease.

Some of the differences between the results of the two populations may be explained by the fact that New York had a more diverse patient population, endured one of the first waves of the pandemic, and faced a lack of personal protective equipment such as masks, compared to Florida. said Dr. Zang.

The new study ties into previous work by Dr. Kaushal, who is also senior associate dean for clinical research, and Nanette Lightman, distinguished professor of population health sciences at Weill Cornell Medicine, and colleagues who categorized different subtypes of prolonged COVID.

“In this new study, we examined a broad list of potential long-term conditions of COVID one by one,” said Dr. Fei Wang, associate professor of population health sciences and co-senior author of the study. “These findings may help us better recognize the broad involvement of multiple organ systems in prolonged COVID and develop appropriate plans for patient management and treatment development.”


Journal reference:

Zang, S., et al. (2023). Data-driven analysis to understand long-term COVID using electronic health records from the RECOVER initiative. Nature Communications. doi.org/10.1038/s41467-023-37653-z.

Source Link

You may also like