Development and external validation of tools for categorizing diagnosis codes in international hospital data

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Standard

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.

I: International Journal of Medical Informatics, Bind 189, 105508, 2024.

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningfagfællebedømt

Harvard

Malecki, SL, Loffler, A, Tamming, D, Dyrby Johansen, N, Biering-Sørensen, T, Fralick, M, Sohail, S, Shi, J, Roberts, SB, Colacci, M, Ismail, M, Razak, F & Verma, AA 2024, 'Development and external validation of tools for categorizing diagnosis codes in international hospital data', International Journal of Medical Informatics, bind 189, 105508. https://doi.org/10.1016/j.ijmedinf.2024.105508

APA

Malecki, S. L., Loffler, A., Tamming, D., Dyrby Johansen, N., Biering-Sørensen, T., Fralick, M., Sohail, S., Shi, J., Roberts, S. B., Colacci, M., Ismail, M., Razak, F., & Verma, A. A. (2024). Development and external validation of tools for categorizing diagnosis codes in international hospital data. International Journal of Medical Informatics, 189, [105508]. https://doi.org/10.1016/j.ijmedinf.2024.105508

Vancouver

Malecki SL, Loffler A, Tamming D, Dyrby Johansen N, Biering-Sørensen T, Fralick M o.a. Development and external validation of tools for categorizing diagnosis codes in international hospital data. International Journal of Medical Informatics. 2024;189. 105508. https://doi.org/10.1016/j.ijmedinf.2024.105508

Author

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. / Development and external validation of tools for categorizing diagnosis codes in international hospital data. I: International Journal of Medical Informatics. 2024 ; Bind 189.

Bibtex

@article{1978a94a31b24d6d859f017401b05c55,
title = "Development and external validation of tools for categorizing diagnosis codes in international hospital data",
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.",
keywords = "Algorithm, Clinical Classification Software Refined (CCSR), Diagnosis codes, ICD-10, Validation",
author = "Malecki, {Sarah L.} and Anne Loffler and Daniel Tamming and {Dyrby Johansen}, Niklas and Tor Biering-S{\o}rensen and Michael Fralick and Shahmir Sohail and Jessica Shi and Roberts, {Surain B.} and Michael Colacci and Marwa Ismail and Fahad Razak and Verma, {Amol A.}",
note = "Publisher Copyright: {\textcopyright} 2024 The Author(s)",
year = "2024",
doi = "10.1016/j.ijmedinf.2024.105508",
language = "English",
volume = "189",
journal = "International Journal of Medical Informatics",
issn = "1386-5056",
publisher = "Elsevier Ireland Ltd",

}

RIS

TY - JOUR

T1 - Development and external validation of tools for categorizing diagnosis codes in international hospital data

AU - Malecki, Sarah L.

AU - Loffler, Anne

AU - Tamming, Daniel

AU - Dyrby Johansen, Niklas

AU - Biering-Sørensen, Tor

AU - Fralick, Michael

AU - Sohail, Shahmir

AU - Shi, Jessica

AU - Roberts, Surain B.

AU - Colacci, Michael

AU - Ismail, Marwa

AU - Razak, Fahad

AU - Verma, Amol A.

N1 - Publisher Copyright: © 2024 The Author(s)

PY - 2024

Y1 - 2024

N2 - 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.

AB - 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.

KW - Algorithm

KW - Clinical Classification Software Refined (CCSR)

KW - Diagnosis codes

KW - ICD-10

KW - Validation

U2 - 10.1016/j.ijmedinf.2024.105508

DO - 10.1016/j.ijmedinf.2024.105508

M3 - Journal article

C2 - 38851134

AN - SCOPUS:85195313623

VL - 189

JO - International Journal of Medical Informatics

JF - International Journal of Medical Informatics

SN - 1386-5056

M1 - 105508

ER -

ID: 394986475