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Characterization of Post-COVID-19 Definitions and Clinical Coding Practices: Longitudinal Study.
Maripuri, Monika; Dey, Andrew; Honerlaw, Jacqueline; Hong, Chuan; Ho, Yuk-Lam; Tanukonda, Vidisha; Chen, Alicia W; Panickan, Vidul Ayakulangara; Wang, Xuan; Zhang, Harrison G; Yang, Doris; Samayamuthu, Malarkodi Jebathilagam; Morris, Michele; Visweswaran, Shyam; Beaulieu-Jones, Brendin; Ramoni, Rachel; Muralidhar, Sumitra; Gaziano, J Michael; Liao, Katherine; Xia, Zongqi; Brat, Gabriel A; Cai, Tianxi; Cho, Kelly.
Afiliação
  • Maripuri M; Veterans Affairs Boston Healthcare System, Boston, MA, United States.
  • Dey A; Veterans Affairs Boston Healthcare System, Boston, MA, United States.
  • Honerlaw J; Veterans Affairs Boston Healthcare System, Boston, MA, United States.
  • Hong C; Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States.
  • Ho YL; Veterans Affairs Boston Healthcare System, Boston, MA, United States.
  • Tanukonda V; Veterans Affairs Boston Healthcare System, Boston, MA, United States.
  • Chen AW; Veterans Affairs Boston Healthcare System, Boston, MA, United States.
  • Panickan VA; Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States.
  • Wang X; Department of Population Health Sciences, University of Utah, Salt Lake City, UT, United States.
  • Zhang HG; Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States.
  • Yang D; Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States.
  • Samayamuthu MJ; Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, United States.
  • Morris M; Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, United States.
  • Visweswaran S; Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, United States.
  • Beaulieu-Jones B; Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States.
  • Ramoni R; Office of Research and Development, US Department of Veterans Affairs, Washington, DC, United States.
  • Muralidhar S; Office of Research and Development, US Department of Veterans Affairs, Washington, DC, United States.
  • Gaziano JM; Veterans Affairs Boston Healthcare System, Boston, MA, United States.
  • Liao K; Division of Aging, Department of Medicine, Mass General Brigham, Harvard Medical School, Boston, MA, United States.
  • Xia Z; Division of Rheumatology, Inflammation, and Immunity, Brigham and Women's Hospital, Boston, MA, United States.
  • Brat GA; Department of Neurology, University of Pittsburgh, Pittsburgh, PA, United States.
  • Cai T; Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States.
  • Cho K; Department of Surgery, Beth Israel Deaconess Medical Center, Boston, MA, United States.
Online J Public Health Inform ; 16: e53445, 2024 May 03.
Article em En | MEDLINE | ID: mdl-38700929
ABSTRACT

BACKGROUND:

Post-COVID-19 condition (colloquially known as "long COVID-19") characterized as postacute sequelae of SARS-CoV-2 has no universal clinical case definition. Recent efforts have focused on understanding long COVID-19 symptoms, and electronic health record (EHR) data provide a unique resource for understanding this condition. The introduction of the International Classification of Diseases, Tenth Revision (ICD-10) code U09.9 for "Post COVID-19 condition, unspecified" to identify patients with long COVID-19 has provided a method of evaluating this condition in EHRs; however, the accuracy of this code is unclear.

OBJECTIVE:

This study aimed to characterize the utility and accuracy of the U09.9 code across 3 health care systems-the Veterans Health Administration, the Beth Israel Deaconess Medical Center, and the University of Pittsburgh Medical Center-against patients identified with long COVID-19 via a chart review by operationalizing the World Health Organization (WHO) and Centers for Disease Control and Prevention (CDC) definitions.

METHODS:

Patients who were COVID-19 positive with either a U07.1 ICD-10 code or positive polymerase chain reaction test within these health care systems were identified for chart review. Among this cohort, we sampled patients based on two approaches (1) with a U09.9 code and (2) without a U09.9 code but with a new onset long COVID-19-related ICD-10 code, which allows us to assess the sensitivity of the U09.9 code. To operationalize the long COVID-19 definition based on health agency guidelines, symptoms were grouped into a "core" cluster of 11 commonly reported symptoms among patients with long COVID-19 and an extended cluster that captured all other symptoms by disease domain. Patients having ≥2 symptoms persisting for ≥60 days that were new onset after their COVID-19 infection, with ≥1 symptom in the core cluster, were labeled as having long COVID-19 per chart review. The code's performance was compared across 3 health care systems and across different time periods of the pandemic.

RESULTS:

Overall, 900 patient charts were reviewed across 3 health care systems. The prevalence of long COVID-19 among the cohort with the U09.9 ICD-10 code based on the operationalized WHO definition was between 23.2% and 62.4% across these health care systems. We also evaluated a less stringent version of the WHO definition and the CDC definition and observed an increase in the prevalence of long COVID-19 at all 3 health care systems.

CONCLUSIONS:

This is one of the first studies to evaluate the U09.9 code against a clinical case definition for long COVID-19, as well as the first to apply this definition to EHR data using a chart review approach on a nationwide cohort across multiple health care systems. This chart review approach can be implemented at other EHR systems to further evaluate the utility and performance of the U09.9 code.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: Online J Public Health Inform Ano de publicação: 2024 Tipo de documento: Article País de afiliação: Estados Unidos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: Online J Public Health Inform Ano de publicação: 2024 Tipo de documento: Article País de afiliação: Estados Unidos
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