Why the study?
The study aimed to validate major bleeding cases in DOAC new users within primary care EHRs in Spain by introducing more efficient, automated validation techniques.
A semi-automatic algorithm using hospitalization and critical localization is a valid and highly specific approach for identifying major bleeding in electronic health records.
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Supports algorithm-assisted major bleeding detection in EHR pharmacoepidemiology; leaves open generalizability and prospective validation.
PURPOSE: This study aims to validate major bleeding (MB) cases within a cohort of new users of direct oral anticoagulants (DOACs) in Electronic health records (EHRs) from primary care in Spain (BIFAP), introducing more efficient techniques and automating the process of validation in the pharmacoepidemiologic research with EHR data as much as possible. METHODS: Registered bleedings were identified in a cohort of new users of DOACs in BIFAP using ICPC 2 and ICD 9 codes; we ascertained these bleedings as MB through a validation strategy based on the MB definition from the International Society on Thrombosis and Hemostasis, which used hospitalization and critical localization as proxies. We assessed hospitalization with hospital discharge information (only available for some years and regions) and a free text-based algorithm created to identify hospitalization in EHR's clinical notes. Incidence rates (IR) of MB were evaluated by bleeding type. RESULTS: The study cohort included 104 614 patients, with 274521.5 p-y of follow up. There were 6143 registered bleedings during the study period (519 intracranial bleeding - ICB, 4606 gastrointestinal bleeding - GIB, 1018 extracranial bleeding - ECB), from which 1679 were confirmed as MB (416 ICB, 1086 GIB, and 177 ECB). The free text-based semi-automatic algorithm had moderate recall (0.59), but high specificity (0.99), and precision (0.94). CONCLUSION: The combination of hospitalization and critical localization is a valid approach to validate MB in EHRs with incomplete information. The use of more automatic methods for case validation instead of manual review of clinical notes is favored.
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Burgos‐Gonzalez et al. (2021) studied this question.
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