Data-driven identification of post-acute SARS-CoV-2 infection subphenotypes
Zhang, H; Zang, C; Xu, Z; et al., Nature Medicine
Published
January 2023
Journal
Nature Medicine
Abstract
The post-acute sequelae of SARS-CoV-2 infection (PASC) refers to a broad spectrum of symptoms and signs that are persistent, exacerbated or newly incident in the period after acute SARS-CoV-2 infection. Most studies have examined these conditions individually without providing evidence on co-occurring conditions. In this study, we leveraged the electronic health record data of two large cohorts, INSIGHT and OneFlorida+, from the national Patient-Centered Clinical Research Network. We created a development cohort from INSIGHT and a validation cohort from OneFlorida+ including 20,881 and 13,724 patients, respectively, who were SARS-CoV-2 infected, and we investigated their newly incident diagnoses 30-180 days after a documented SARS-CoV-2 infection. Through machine learning analysis of over 137 symptoms and conditions, we identified four reproducible PASC subphenotypes, dominated by cardiac and renal (including 33.75% and 25.43% of the patients in the development and validation cohorts); respiratory, sleep and anxiety (32.75% and 38.48%); musculoskeletal and nervous system (23.37% and 23.35%); and digestive and respiratory system (10.14% and 12.74%) sequelae. These subphenotypes were associated with distinct patient demographics, underlying conditions before SARS-CoV-2 infection and acute infection phase severity. Our study provides insights into the heterogeneity of PASC and may inform stratified decision-making in the management of PASC conditions.
Authors
Hao Zhang, Chengxi Zang, Zhenxing Xu, Yongkang Zhang, Jie Xu, Jiang Bian, Dmitry Morozyuk, Dhruv Khullar, Yiye Zhang, Anna S Nordvig, Edward J Schenck, Elizabeth A Shenkman, Russell L Rothman, Jason P Block, Kristin Lyman, Mark G Weiner, Thomas W Carton, Fei Wang, Rainu Kaushal
Keywords
Humans; COVID-19/epidemiology; SARS-CoV-2; Post-Acute COVID-19 Syndrome; Anxiety; Anxiety Disorders; Disease Progression
Short Summary
RECOVER researchers wanted to learn if there are different types of Long COVID based on symptoms and health problems that often happen together. Researchers used data from electronic health records (EHRs) of about 35,000 people diagnosed with COVID. The EHRs were from 2 healthcare systems in PCORnet, the National Patient-Centered Clinical Research Network. They used a computer program to look for patterns in people’s new symptoms and health problems that started 30 to 180 days after having COVID.
The researchers found 4 main types of Long COVID based on symptoms and health problems that happen together. The 4 types of Long COVID are related to the 1) heart and kidneys, 2) breathing, sleep, and anxiety, 3) muscles and nerves, and 4) digestive tract and breathing. This research could help define types of Long COVID to give people more specific diagnoses and treatment plans.