Purpose: Routinely collected data are useful for epidemiological study in hemophilia, but few studies validated the algorithm accuracy. We aimed to develop and validate algorithms to identify patients with hemophilia A and hemophilia A-related events. Patients and Methods: This validation study compared data from medical chart reviews to a database of routinely collected health data, including claims data and discharge abstracts, and especially electronic medical records (EMR), at a single Japanese hospital (Kurashiki Central Hospital) using a stratified sampling method. Two physicians reviewed the charts for all patients at high risk for hemophilia A, and randomly sampled patients with moderate risk. Diagnostic accuracy was determined based on sensitivity, specificity, positive predictive value (PPV), and negative predictive value. Results: There were 1,033,845 eligible patients, of whom 31 had a diagnosis of hemophilia A. ICD-10 diagnosis code D66 in the EMR identified hemophilia A with a sensitivity of 93.5% (95% confidence interval: 78.6-99) and PPV of 61.7% (95% confidence interval: 46.4-75.5). The administration of ≥10,000 units/month of factor VIII products, as documented in the EMR, identified 81.3% of patients with prophylactic factor replacement therapy. The ICD-10 diagnosis code for intracranial bleeding in the EMR identified 75.0% of patients with intracranial bleeding, but those of gastrointestinal bleeding and major joint bleeding identified only 11.1% and 1.7%, respectively. Conclusion:We developed and validated algorithms to identify congenital hemophilia A and hemophilia A-related events. Hemophilia A could be identified with high sensitivity and PPV, but it was still challenging to identify hemophilia A-related events.

ObjectivesValidation studies in oncology are limited in Japan. This study was conducted to evaluate the accuracy of diagnosis and adverse event (AE) definitions for specific cancers in a Japanese health administrative real-world database (RWD).Design and settingRetrospective observational validation study to assess the diagnostic accuracy of electronic medical records (EMRs) and claim coding regarding oncology diagnosis and AEs based on medical record review in the RWD. The sensitivity and positive predictive value (PPV) with 95% CIs were calculated.ParticipantsThe validation cohort included patients with lung (n=2257), breast (n=1121), colorectal (n=1773), ovarian (n=216) and bladder (n=575) cancer who visited the hospital between January 2014 and December 2018, and those with prostate cancer (n=3491) visiting between January 2009 and December 2018, who were identified using EMRs.OutcomesKey outcomes included primary diagnosis, deaths and AEs.ResultsFor primary diagnosis, sensitivity and PPV for the respective cancers were as follows: lung, 100.0% (96.6 to 100.0) and 81.0% (74.9 to 86.2); breast, 100.0% (96.3 to 100.0) and 74.0% (67.3 to 79.9); colorectal, 100.0% (96.6 to 100.0) and 80.5% (74.3 to 85.8); ovarian, 89.8% (77.8 to 96.6) and 75.9% (62.8 to 86.1); bladder, 78.6% (63.2 to 89.7) and 67.3% (52.5 to 0.1); prostate, 100.0% (93.2 to 100.0) and 79.0% (69.7 to 86.5). Sensitivity and PPV for death were as follows: lung, 97.0% (84.2 to 99.9) and 100.0% (84.2 to 100.0); breast, 100.0% (1.3 to 100.0) and 100.0% (1.3 to 100.0); colorectal, 100.0% (28.4 to 100.0) and 100.0% (28.4 to 100.0); ovarian, 100.0% (35.9 to 100.0) and 100.0% (35.9 to 100.0); bladder, 100.0% (9.4–100.0) and 100.0% (9.4 to 100.0); prostate, 75.0% (19.4 to 99.4) and 100.0% (19.4 to 100.0). Overall, PPV tended to be low, with the definition based on International Classification of Diseases, 10th revision alone for AEs.ConclusionDiagnostic accuracy was not so high, and therefore needs to be further investigated.Trial registration numberUniversity Hospital Medical Information Network (UMIN) Clinical Trials Registry (UMIN000039345).