Respiratory medicine · 2026
Validation of claims-based algorithms for identifying selected interstitial lung disease (idiopathic interstitial pneumonias and drug-induced interstitial lung disease).
СтатьяВалидационное исследованиеНазвание и аннотация: Перевод в очереди
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Kataoka Kensuke, Setoguchi Soko, Nakashima Naoki, Chen Haoqian, Rua Melanie, Okui Tasuku, Hamada Naoki, Yamauchi Hiroyoshi, Watanabe Satoshi, Kato Motoyasu, Arai Toru, Ogura Takashi, Inoue Yoshikazu, Kondoh Yasuhiro, Nakazuru Yoshiomi, Hiro Shintaro, Matsui Mari, Kishaba Tomoo
Аннотация
BACKGROUND: Interstitial lung disease (ILD) is a heterogeneous group of more than 200 diseases that cause fibrosis or inflammation of the pulmonary parenchyma. ILD is a leading cause of death, yet data on its precise incidence and prevalence are limited due to the complexity of diagnostic criteria. To address this gap, we developed and validated claims-based algorithms to identify selected ILD (restricted to idiopathic interstitial pneumonias and drug-induced ILD) in Japan.
METHODS: We identified potential ILD cases from 2010 to 2020 from electronic medical record databases at two large healthcare institutions using a primary claims-based algorithm for high positive predictive value (PPV) and a relaxed algorithm for improved sensitivity. We calculated sensitivity and assessed the validity of the algorithms by comparing them to two gold standard definitions: (1) physician's diagnosis on the medical record, and (2) adjudication by a team of ILD experts based on abstracted medical record data including chest computed tomography images.
RESULTS: Among 7638 potential ILD cases, we sampled 460 patients. The estimated PPV was 87.0% (95% CI 83.2-90.8%) for the primary algorithm and 71.4% (64.0-78.7%) for the relaxed algorithm, based on confirmed ILD responses in the expert adjudication as the gold standard. Estimated sensitivity was 30.4% (27.4-33.8%) and 59.4% (54.1-65.1%) for the primary and relaxed algorithms respectively.
CONCLUSIONS: The algorithms developed in this study may be useful for identifying selected ILD from administrative data in post-marketing database studies and other clinical and epidemiologic research. The appropriate algorithm may be selected based on the specific research objective.
METHODS: We identified potential ILD cases from 2010 to 2020 from electronic medical record databases at two large healthcare institutions using a primary claims-based algorithm for high positive predictive value (PPV) and a relaxed algorithm for improved sensitivity. We calculated sensitivity and assessed the validity of the algorithms by comparing them to two gold standard definitions: (1) physician's diagnosis on the medical record, and (2) adjudication by a team of ILD experts based on abstracted medical record data including chest computed tomography images.
RESULTS: Among 7638 potential ILD cases, we sampled 460 patients. The estimated PPV was 87.0% (95% CI 83.2-90.8%) for the primary algorithm and 71.4% (64.0-78.7%) for the relaxed algorithm, based on confirmed ILD responses in the expert adjudication as the gold standard. Estimated sensitivity was 30.4% (27.4-33.8%) and 59.4% (54.1-65.1%) for the primary and relaxed algorithms respectively.
CONCLUSIONS: The algorithms developed in this study may be useful for identifying selected ILD from administrative data in post-marketing database studies and other clinical and epidemiologic research. The appropriate algorithm may be selected based on the specific research objective.