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Development and external validation of tools for categorizing diagnosis codes in international hospital data.
Malecki, Sarah L; Loffler, Anne; Tamming, Daniel; Dyrby Johansen, Niklas; Biering-Sørensen, Tor; Fralick, Michael; Sohail, Shahmir; Shi, Jessica; Roberts, Surain B; Colacci, Michael; Ismail, Marwa; Razak, Fahad; Verma, Amol A.
Afiliação
  • Malecki SL; Department of Medicine, University of Toronto, Toronto, ON, Canada. Electronic address: sarah.voll@mail.utoronto.ca.
  • Loffler A; St. Michael's Hospital, University of Toronto, Toronto, ON, Canada.
  • Tamming D; St. Michael's Hospital, University of Toronto, Toronto, ON, Canada.
  • Dyrby Johansen N; Department of Cardiology, Copenhagen University Hospital - Herlev and Gentofte, Copenhagen, Denmark; Center for Translational Cardiology and Pragmatic Randomized Trials, Department of Biomedical Sciences, Faculty of Health and Medical Sciences, University of Copenhagen, Denmark.
  • Biering-Sørensen T; Department of Cardiology, Copenhagen University Hospital - Herlev and Gentofte, Copenhagen, Denmark; Center for Translational Cardiology and Pragmatic Randomized Trials, Department of Biomedical Sciences, Faculty of Health and Medical Sciences, University of Copenhagen, Denmark.
  • Fralick M; Division of General Internal Medicine, Sinai Health System, ON, Toronto, Canada; Institute of Health Policy, Management, and Evaluation, University of Toronto, Toronto, ON, Canada.
  • Sohail S; Department of Medicine, University of Toronto, Toronto, ON, Canada.
  • Shi J; St. Michael's Hospital, University of Toronto, Toronto, ON, Canada.
  • Roberts SB; St. Michael's Hospital, University of Toronto, Toronto, ON, Canada.
  • Colacci M; Department of Medicine, University of Toronto, Toronto, ON, Canada; St. Michael's Hospital, University of Toronto, Toronto, ON, Canada; Institute of Health Policy, Management, and Evaluation, University of Toronto, Toronto, ON, Canada.
  • Ismail M; St. Michael's Hospital, University of Toronto, Toronto, ON, Canada.
  • Razak F; Department of Medicine, University of Toronto, Toronto, ON, Canada; St. Michael's Hospital, University of Toronto, Toronto, ON, Canada; Institute of Health Policy, Management, and Evaluation, University of Toronto, Toronto, ON, Canada.
  • Verma AA; Department of Medicine, University of Toronto, Toronto, ON, Canada; St. Michael's Hospital, University of Toronto, Toronto, ON, Canada; Institute of Health Policy, Management, and Evaluation, University of Toronto, Toronto, ON, Canada; Department of Laboratory Medicine and Pathobiology, University o
Int J Med Inform ; 189: 105508, 2024 Sep.
Article em En | MEDLINE | ID: mdl-38851134
ABSTRACT

BACKGROUND:

The Clinical Classification Software Refined (CCSR) is a tool that groups many thousands of International Classification of Diseases 10th Revision (ICD-10) diagnosis codes into approximately 500 clinically meaningful categories, simplifying analyses. However, CCSR was developed for use in the United States and may not work well with other country-specific ICD-10 coding systems.

METHOD:

We developed an algorithm for semi-automated matching of Canadian ICD-10 codes (ICD-10-CA) to CCSR categories using discharge diagnoses from adult admissions at 7 hospitals between Apr 1, 2010 and Dec 31, 2020, and manually validated the results. We then externally validated our approach using inpatient hospital encounters in Denmark from 2017 to 2018. KEY

RESULTS:

There were 383,972 Canadian hospital admissions with 5,186 distinct ICD-10-CA diagnosis codes and 1,855,837 Danish encounters with 4,612 ICD-10 diagnosis codes. Only 46.6% of Canadian codes and 49.4% of Danish codes could be directly categorized using the official CCSR tool. Our algorithm facilitated the mapping of 98.5% of all Canadian codes and 97.7% of Danish codes. Validation of our algorithm by clinicians demonstrated excellent accuracy (97.1% and 97.0% in Canadian and Danish data, respectively). Without our algorithm, many common conditions did not match directly to a CCSR category, such as 96.6% of hospital admissions for heart failure.

CONCLUSION:

The GEMINI CCSR matching algorithm (available as an open-source package at https//github.com/GEMINI-Medicine/gemini-ccsr) improves the categorization of Canadian and Danish ICD-10 codes into clinically coherent categories compared to the original CCSR tool. We expect this approach to generalize well to other countries and enable a wide range of research and quality measurement applications.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Algoritmos / Classificação Internacional de Doenças Limite: Humans País/Região como assunto: America do norte / Europa Idioma: En Revista: Int J Med Inform Assunto da revista: INFORMATICA MEDICA Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Algoritmos / Classificação Internacional de Doenças Limite: Humans País/Região como assunto: America do norte / Europa Idioma: En Revista: Int J Med Inform Assunto da revista: INFORMATICA MEDICA Ano de publicação: 2024 Tipo de documento: Article