| アブストラクト | BACKGROUND: This retrospective observational study validated case-finding algorithms for malignant tumors and serious infections in a Japanese administrative healthcare database. METHODS: Random samples of possible cases of each disease (January 2015-January 2018) from two hospitals participating in the Medical Data Vision Co., Ltd. (MDV) database were identified using combinations of ICD-10 diagnostic codes and other procedural/billing codes. For each disease, two physicians identified true cases among the random samples of possible cases by medical record review; a third physician made the final decision in cases where the two physicians disagreed. The accuracy of case-finding algorithms was assessed using positive predictive value (PPV) and sensitivity. RESULTS: There were 2,940 possible cases of malignant tumor; 180 were randomly selected and 108 were identified as true cases after medical record review. One case-finding algorithm gave a high PPV (64.1%) without substantial loss in sensitivity (90.7%) and included ICD-10 codes for malignancy and photographing/imaging. There were 3,559 possible cases of serious infection; 200 were randomly selected and 167 were identified as true cases after medical record review. Two case-finding algorithms gave a high PPV (85.6%) with no loss in sensitivity (100%). Both case-finding algorithms included the relevant diagnostic code and immunological infection test/other related test and, of these, one also included pathological diagnosis within 1 month of hospitalization. CONCLUSIONS: The case-finding algorithms in this study showed good PPV and sensitivity for identification of cases of malignant tumors and serious infections from an administrative healthcare database in Japan. |
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| ジャーナル名 | Annals of clinical epidemiology |
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| 投稿日 | 2022/1/7 |
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| 投稿者 | Nishikawa, Atsushi; Yoshinaga, Eiko; Nakamura, Masaki; Suzuki, Masayoshi; Kido, Keiji; Tsujimoto, Naoto; Ishii, Taeko; Koide, Daisuke |
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| 組織名 | Eli Lilly Japan K.K.;Medical Data Vision Co., Ltd.;Department of Biostatistics and Bioinformatics, Graduate School of Medicine,;University of Tokyo. |
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| Pubmed リンク | https://www.ncbi.nlm.nih.gov/pubmed/38505283/ |
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