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Coding Long COVID: Characterizing a new disease through an ICD-10 lens
Emily Pfaff; Charisse Madlock-Brown; John M Baratta; Abhishek Bhatia; Hannah Davis; Andrew T Girvin; Elaine Hill; Liz Kelly; Kristin Kostka; Johanna Loomba; Julie McMurry; Rachel Wong; Tellen D Bennett; Richard Moffitt; Christopher G Chute; Melissa Haendel; - The N3C Consortium; - The RECOVER Consortium.
Afiliación
  • Emily Pfaff; UNC Chapel Hill
  • Charisse Madlock-Brown; University of Tennessee Health Science Center
  • John M Baratta; University of North Carolina at Chapel Hill
  • Abhishek Bhatia; University of North Carolina at Chapel Hill
  • Hannah Davis; Patient-Led Research Collaborative
  • Andrew T Girvin; Palantir Technologies
  • Elaine Hill; University of Rochester
  • Liz Kelly; University of North Carolina at Chapel Hill
  • Kristin Kostka; Northeastern University
  • Johanna Loomba; University of Virginia
  • Julie McMurry; University of Colorado Anschutz Medical Campus
  • Rachel Wong; Stony Brook University
  • Tellen D Bennett; University of Colorado School of Medicine
  • Richard Moffitt; Stony Brook University
  • Christopher G Chute; Johns Hopkins University
  • Melissa Haendel; Oregon Health & Science University
  • - The N3C Consortium; -
  • - The RECOVER Consortium; -
Preprint en En | PREPRINT-MEDRXIV | ID: ppmedrxiv-22273968
ABSTRACT
BackgroundNaming a newly discovered disease is a difficult process; in the context of the COVID-19 pandemic and the existence of post-acute sequelae of SARS-CoV-2 infection (PASC), which includes Long COVID, it has proven especially challenging. Disease definitions and assignment of a diagnosis code are often asynchronous and iterative. The clinical definition and our understanding of the underlying mechanisms of Long COVID are still in flux, and the deployment of an ICD-10-CM code for Long COVID in the US took nearly two years after patients had begun to describe their condition. Here we leverage the largest publicly available HIPAA-limited dataset about patients with COVID-19 in the US to examine the heterogeneity of adoption and use of U09.9, the ICD-10-CM code for "Post COVID-19 condition, unspecified." MethodsWe undertook a number of analyses to characterize the N3C population with a U09.9 diagnosis code (n = 21,072), including assessing person-level demographics and a number of area-level social determinants of health; diagnoses commonly co-occurring with U09.9, clustered using the Louvain algorithm; and quantifying medications and procedures recorded within 60 days of U09.9 diagnosis. We stratified all analyses by age group in order to discern differing patterns of care across the lifespan. ResultsWe established the diagnoses most commonly co-occurring with U09.9, and algorithmically clustered them into four major categories cardiopulmonary, neurological, gastrointestinal, and comorbid conditions. Importantly, we discovered that the population of patients diagnosed with U09.9 is demographically skewed toward female, White, non-Hispanic individuals, as well as individuals living in areas with low poverty, high education, and high access to medical care. Our results also include a characterization of common procedures and medications associated with U09.9-coded patients. ConclusionsThis work offers insight into potential subtypes and current practice patterns around Long COVID, and speaks to the existence of disparities in the diagnosis of patients with Long COVID. This latter finding in particular requires further research and urgent remediation.
Licencia
cc_by_nc_nd
Texto completo: 1 Colección: 09-preprints Base de datos: PREPRINT-MEDRXIV Tipo de estudio: Prognostic_studies Idioma: En Año: 2022 Tipo del documento: Preprint
Texto completo: 1 Colección: 09-preprints Base de datos: PREPRINT-MEDRXIV Tipo de estudio: Prognostic_studies Idioma: En Año: 2022 Tipo del documento: Preprint