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http://conacyt.repositorioinstitucional.mx/jspui/handle/1000/8247| Finding Long-COVID: Temporal Topic Modeling of Electronic Health Records from the N3C and RECOVER Programs | |
| Shawn O'Neil Charisse Madlock-Brown Kenneth Wilkins Brenda McGrath Hannah Davis Gina Assaf Hannah Wei Parya Zareie Evan French Johanna Loomba Julie McMurry Andrea Zhou Christopher Chute Richard Moffitt Emily Pfaff Yun Jae Yoo Peter Leese Robert Chew Michael Lieberman Melissa Haendel | |
| Acceso Abierto | |
| Atribución-NoComercial-SinDerivadas | |
| https://doi.org/10.1101/2023.09.11.23295259 | |
| https://www.medrxiv.org/content/10.1101/2023.09.11.23295259v1 | |
| Post-Acute Sequelae of SARS-CoV-2 infection (PASC), also known as Long-COVID, encompasses a variety of complex and varied outcomes following COVID-19 infection that are still poorly understood. We clustered over 600 million condition diagnoses from 14 million patients available through the National COVID Cohort Collaborative (N3C), generating hundreds of highly detailed clinical phenotypes. Assessing patient clinical trajectories using these clusters allowed us to identify individual conditions and phenotypes strongly increased after acute infection. We found many conditions increased in COVID-19 patients compared to controls, and using a novel method to predict patient/cluster assignment over time, we additionally found phenotypes specific to patient sex, age, wave of infection, and PASC diagnosis status. While many of these results reflect known PASC symptoms, the resolution provided by this unprecedented data scale suggests avenues for improved diagnostics and mechanistic understanding of this multifaceted disease. Competing Interest Statement The authors have declared no competing interest. Funding Statement The analyses described in this publication were conducted with data and tools accessed through the NCATS N3C Data Enclave https://covid.cd2h.org and N3C Attribution & Publication Policy v 1.2-2020-08-25b supported by NCATS U24 TR002306, Axle Informatics Subcontract: NCATS-P00438-B, National Institutes of Health grant NHLBI RECOVER Agreement OT2HL161847-01, and CTSA award No. UM1TR004360 from the National Center for Advancing Translational Sciences. | |
| bioRxiv | |
| 12-09-2023 | |
| Preimpreso | |
| Inglés | |
| Público en general | |
| VIRUS RESPIRATORIOS | |
| Aparece en las colecciones: | Materiales de Consulta y Comunicados Técnicos |
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| Fichero | Tamaño | Formato | |
|---|---|---|---|
| Finding Long-COVID_Temporal Topic Modeling of Electronic Health Records from the N3C and RECOVER Programs.pdf | 9.88 MB | Adobe PDF | Visualizar/Abrir |