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Validation of Algorithms to Identify Bone Metastases Using Administrative Claims Data in a Japanese Hospital

BACKGROUND: Validated coding algorithms are essential to generate high-quality, real-world evidence from claims data studies. OBJECTIVE: We aimed to evaluate the validity of the algorithms to identify patients with bone metastases using claims data from a Japanese hospital. PATIENTS AND METHODS: Thi...

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Autores principales: Hirano, Takahiro, Saito, Naoko, Wakabayashi, Ryozo, Kuwatsuru, Ryohei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10232691/
https://www.ncbi.nlm.nih.gov/pubmed/36652116
http://dx.doi.org/10.1007/s40801-022-00347-x
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author Hirano, Takahiro
Saito, Naoko
Wakabayashi, Ryozo
Kuwatsuru, Ryohei
author_facet Hirano, Takahiro
Saito, Naoko
Wakabayashi, Ryozo
Kuwatsuru, Ryohei
author_sort Hirano, Takahiro
collection PubMed
description BACKGROUND: Validated coding algorithms are essential to generate high-quality, real-world evidence from claims data studies. OBJECTIVE: We aimed to evaluate the validity of the algorithms to identify patients with bone metastases using claims data from a Japanese hospital. PATIENTS AND METHODS: This study used administrative claims data and electronic medical records at Juntendo University Hospital from April 2017 to March 2019. We developed two candidate claims-based algorithms to detect bone metastases, one based on diagnosis codes alone (Algorithm 1) and the other based on the combination of diagnosis and imaging test codes (Algorithm 2). Of the patients identified by Algorithm 1, 100 patients were randomly sampled. Among these 100 patients, 88 patients met the conditions of Algorithm 2; further, 12 additional patients were randomly sampled from those identified by Algorithm 2, thus obtaining a total of 100 patients for Algorithm 2. They were evaluated for their true diagnosis using the patient chart review as the gold standard. The positive predictive value (PPV) was calculated to assess the accuracy of each algorithm. RESULTS: For Algorithm 1, 82 patients were analyzed after excluding 18 patients without diagnostic imaging reports. Of these, 69 patients were true positive by chart review, resulting in a PPV of 84.1% (95% confidence interval (CI) 74.5–90.6). For Algorithm 2, 92 patients were analyzed after excluding eight patients whose diagnoses were not judged by chart review. Of these, 76 patients were confirmed positive by chart review, yielding a PPV of 82.6% (95% CI 73.4–89.1). CONCLUSION: Both claims-based algorithms yielded high PPVs of approximately 85%, with no improvement in PPV by adding imaging test conditions. The diagnosis code-based algorithm is sufficient and valid for identifying bone metastases in this Japanese hospital. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s40801-022-00347-x.
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spelling pubmed-102326912023-06-02 Validation of Algorithms to Identify Bone Metastases Using Administrative Claims Data in a Japanese Hospital Hirano, Takahiro Saito, Naoko Wakabayashi, Ryozo Kuwatsuru, Ryohei Drugs Real World Outcomes Original Research Article BACKGROUND: Validated coding algorithms are essential to generate high-quality, real-world evidence from claims data studies. OBJECTIVE: We aimed to evaluate the validity of the algorithms to identify patients with bone metastases using claims data from a Japanese hospital. PATIENTS AND METHODS: This study used administrative claims data and electronic medical records at Juntendo University Hospital from April 2017 to March 2019. We developed two candidate claims-based algorithms to detect bone metastases, one based on diagnosis codes alone (Algorithm 1) and the other based on the combination of diagnosis and imaging test codes (Algorithm 2). Of the patients identified by Algorithm 1, 100 patients were randomly sampled. Among these 100 patients, 88 patients met the conditions of Algorithm 2; further, 12 additional patients were randomly sampled from those identified by Algorithm 2, thus obtaining a total of 100 patients for Algorithm 2. They were evaluated for their true diagnosis using the patient chart review as the gold standard. The positive predictive value (PPV) was calculated to assess the accuracy of each algorithm. RESULTS: For Algorithm 1, 82 patients were analyzed after excluding 18 patients without diagnostic imaging reports. Of these, 69 patients were true positive by chart review, resulting in a PPV of 84.1% (95% confidence interval (CI) 74.5–90.6). For Algorithm 2, 92 patients were analyzed after excluding eight patients whose diagnoses were not judged by chart review. Of these, 76 patients were confirmed positive by chart review, yielding a PPV of 82.6% (95% CI 73.4–89.1). CONCLUSION: Both claims-based algorithms yielded high PPVs of approximately 85%, with no improvement in PPV by adding imaging test conditions. The diagnosis code-based algorithm is sufficient and valid for identifying bone metastases in this Japanese hospital. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s40801-022-00347-x. Springer International Publishing 2023-01-18 /pmc/articles/PMC10232691/ /pubmed/36652116 http://dx.doi.org/10.1007/s40801-022-00347-x Text en © The Author(s) 2022 https://creativecommons.org/licenses/by-nc/4.0/Open AccessThis article is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License, which permits any non-commercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) .
spellingShingle Original Research Article
Hirano, Takahiro
Saito, Naoko
Wakabayashi, Ryozo
Kuwatsuru, Ryohei
Validation of Algorithms to Identify Bone Metastases Using Administrative Claims Data in a Japanese Hospital
title Validation of Algorithms to Identify Bone Metastases Using Administrative Claims Data in a Japanese Hospital
title_full Validation of Algorithms to Identify Bone Metastases Using Administrative Claims Data in a Japanese Hospital
title_fullStr Validation of Algorithms to Identify Bone Metastases Using Administrative Claims Data in a Japanese Hospital
title_full_unstemmed Validation of Algorithms to Identify Bone Metastases Using Administrative Claims Data in a Japanese Hospital
title_short Validation of Algorithms to Identify Bone Metastases Using Administrative Claims Data in a Japanese Hospital
title_sort validation of algorithms to identify bone metastases using administrative claims data in a japanese hospital
topic Original Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10232691/
https://www.ncbi.nlm.nih.gov/pubmed/36652116
http://dx.doi.org/10.1007/s40801-022-00347-x
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