Health
Study finds long-term risks and symptoms of COVID
of studypublished on April 7 Nature Communications, analyzed electronic health records as part of the National Institutes of Health’s study of COVID to accelerate recovery (recovery) an initiative to better understand the persistence of symptoms following SARS-CoV-2 infection, also known as long COVID, among widely diverse populations.guided by Dr. Rainu Kaushal, Director of Population Health Sciences at Weill Cornell Medicine and Chief Physician of Population Health Sciences at New York-Presbyterian Hospital/Weill Cornell Medical Center, this study provides an overview of potential symptoms after acute COVID-19. and provide a method. The risk of these conditions may vary by US population.
“Long COVID is a new disease that is very complex and very difficult to characterize,” he said. Dr. Zhang Chengshi, an instructor in Population Health Sciences at Weill Cornell Medicine and the first author of the paper. “Because it affects multiple organs and places a severe burden on society, there is an urgent need to define the disease and determine the extent to which that definition applies to different populations.” It provides a foundation for advancing research on long-term COVID.”
The team examined electronic medical records from two clinical research networks that are part of a clinical research network. National Patient-Centered Clinical Research Network (PCORnet)One dataset from . INSIGHT clinical research network—Dr. Koshal lead— included data from 11 million New York-based patients, while another One Florida+ This network included 16.8 million patients in Florida, Georgia and Alabama. The team identified an extensive list of diagnoses that occurred more frequently in patients recently infected with COVID compared to uninfected individuals. Researchers also found a higher risk of more types of symptoms and long-term COVID in New York City than in Florida. Specific conditions seen across the New York City and Florida populations included dementia, hair loss, stomach and small bowel pain, lung clots, chest pain, abnormal heartbeat and fatigue.
“Our approach of using machine learning in electronic medical records provides a data-driven way to define COVID at length and determine how generalizable our definition of the disease is,” said Zang. said the doctor. Comparing records from diverse populations in regions that have experienced the COVID-19 pandemic reveals how variable the length of COVID is for patients, and warrants further investigation to improve disease diagnosis and treatment. The need was emphasized.
Some of the differences in results between the two populations are explained by the fact that the New York City patient population was more diverse, enduring one of the first waves of the pandemic and faced shortages of personal protective equipment such as masks. may be In Florida, Dr. Zhang said.
New research is related to previous job Kaushal, PhD, Senior Associate Dean of Clinical Research and Nanette Laitman Distinguished Professor of Population and Health Sciences at Weill Cornell Medicine, and colleagues who have long classified the various subtypes of COVID.
“In this new study, we looked one at a time through a broad list of potential long-term COVID conditions,” it said. Dr. Wang Fei, Associate Professor of Population and Health Sciences and co-lead author of the study. “These findings will help us better recognize the long-term and extensive involvement of multiple organ systems in COVID and design appropriate plans for patient management and therapeutic development.”
journal
Nature Communications
article title
Data-driven analytics for long-term understanding of COVID using electronic medical records from the RECOVER initiative
Article publication date
April 7, 2023
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