Modern rheumatology · 2026
Multicentre development and validation of data-driven claims-based algorithms for identifying dermatomyositis and polymyositis in Japan.
СтатьяМногоцентровое исследованиеВалидационное исследованиеНазвание и аннотация: Перевод в очереди
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Sada Ken-Ei, Miyawaki Yoshia, Yanai Ryo, Kida Takashi, Onishi Akira, Yoshimi Ryusuke, Ichinose Kunihiro, Shimojima Yasuhiro
Аннотация
OBJECTIVE: To develop and validate data-driven algorithms for identifying patients with dermatomyositis and polymyositis using Japanese administrative claims data.
METHODS: This multicentre retrospective, cross-sectional study included outpatients from six university hospitals between November and December 2023. Administrative claims data covering 1 year were linked with chart-confirmed diagnoses. Twenty-six candidate variables, including diagnosis codes, laboratory tests, prescriptions, and administrative billing items, were evaluated. Three feature-selection methods were applied to identify relevant predictors. Decision tree analysis was used to construct simplified rule-based algorithms, which were validated in independent internal and external cohorts.
RESULTS: Among 8199 training, 3512 testing, and 1827 external validation patients, 576 (7.0%), 244 (6.9%), and 136 (4.2%) carried diagnosis codes for dermatomyositis/polymyositis, of whom 352, 150, and 98 were confirmed as true cases, respectively. The performance of diagnosis codes alone yielded positive predictive values of 0.602 in the testing set and 0.713 in the external validation set. Anti-double-stranded DNA antibody testing, intractable disease management fees, and diagnosis codes for Sjögren's syndrome were identified as key discriminative variables. The optimal algorithm demonstrated a sensitivity of 0.878, a specificity of 0.984, a positive predictive value of 0.761, and an F1-score of 0.815 in the external validation cohort, providing superior accuracy compared to the use of International Classification of Diseases-10 codes alone.
CONCLUSIONS: International Classification of Diseases-10 codes alone were insufficient for the accurate identification of dermatomyositis/polymyositis in Japanese claims data. Integrating laboratory tests and administrative information substantially improved the positive predictive value, providing a practical and generalizable framework for claims-based research in Japan.
METHODS: This multicentre retrospective, cross-sectional study included outpatients from six university hospitals between November and December 2023. Administrative claims data covering 1 year were linked with chart-confirmed diagnoses. Twenty-six candidate variables, including diagnosis codes, laboratory tests, prescriptions, and administrative billing items, were evaluated. Three feature-selection methods were applied to identify relevant predictors. Decision tree analysis was used to construct simplified rule-based algorithms, which were validated in independent internal and external cohorts.
RESULTS: Among 8199 training, 3512 testing, and 1827 external validation patients, 576 (7.0%), 244 (6.9%), and 136 (4.2%) carried diagnosis codes for dermatomyositis/polymyositis, of whom 352, 150, and 98 were confirmed as true cases, respectively. The performance of diagnosis codes alone yielded positive predictive values of 0.602 in the testing set and 0.713 in the external validation set. Anti-double-stranded DNA antibody testing, intractable disease management fees, and diagnosis codes for Sjögren's syndrome were identified as key discriminative variables. The optimal algorithm demonstrated a sensitivity of 0.878, a specificity of 0.984, a positive predictive value of 0.761, and an F1-score of 0.815 in the external validation cohort, providing superior accuracy compared to the use of International Classification of Diseases-10 codes alone.
CONCLUSIONS: International Classification of Diseases-10 codes alone were insufficient for the accurate identification of dermatomyositis/polymyositis in Japanese claims data. Integrating laboratory tests and administrative information substantially improved the positive predictive value, providing a practical and generalizable framework for claims-based research in Japan.