A Bayesian Survival Analysis on Long COVID and non Long COVID patients: A Cohort Study Using National COVID Cohort Collaborative (N3C) Data [preprint]
Jiang, Sihang Loomba, Johanna Zhou, Andrea Sharma, Suchetha Sengupta, Saurav Liu, Jiebei Brown, Donald
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Abstract
Since the outbreak of COVID-19 pandemic in 2020, numerous researches and studies have focused on the long-term effects of COVID infection. The Centers for Disease Control (CDC) implemented an additional code into the International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) for reporting 'Post COVID-19 condition, unspecified (U09.9)' effective on October 1st 2021, representing that Long COVID is a real illness with potential chronic conditions. The National COVID Cohort Collaborative (N3C) provides researchers with abundant electronic health records (EHR) data by aggregating and harmonizing EHR data across different clinical organizations in the United States, making it convenient to build up a survival analysis on Long COVID patients and non Long COVID patients among large amounts of COVID positive patients.
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Jiang S, Loomba J, Zhou A, Sharma S, Sengupta S, Liu J, Brown D. A Bayesian Survival Analysis on Long COVID and non Long COVID patients: A Cohort Study Using National COVID Cohort Collaborative (N3C) Data. medRxiv [Preprint]. 2024 Jun 25:2024.06.25.24309478. doi: 10.1101/2024.06.25.24309478. PMID: 38978664; PMCID: PMC11230301.
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This article is a preprint. Preprints are preliminary reports of work that have not been certified by peer review.