Evidence map›Paper›PMID 42232248›Full record

ArticleResearch and practice in thrombosis and haemostasis2026

Validation of electronic health record-based International Society on Thrombosis and Haemostasis bleeding algorithms in a US health system.

Jaejin An, Alexander Hartenstein, Soon Kyu Choi, Bernadine Dizon, Hui Zhou, Xuan Huang, Susie Flores, Ming-Sum Lee, Brian Hocum, Kai Vogtländer and 1 more

Abstract readValidation Study
In one paragraph

Article in Research and practice in thrombosis and haemostasis, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

11 authors.

Jaejin AnDepartment of Research & Evaluation, Kaiser Permanente Southern California, Pasadena, California, USA.
Alexander HartensteinGlobal Medical and Evidence, Bayer AG, Berlin, Germany.
Soon Kyu ChoiDepartment of Research & Evaluation, Kaiser Permanente Southern California, Pasadena, California, USA.
Bernadine DizonDepartment of Research & Evaluation, Kaiser Permanente Southern California, Pasadena, California, USA.
Hui ZhouDepartment of Research & Evaluation, Kaiser Permanente Southern California, Pasadena, California, USA.
Xuan HuangDepartment of Research & Evaluation, Kaiser Permanente Southern California, Pasadena, California, USA.
Susie FloresDepartment of Research & Evaluation, Kaiser Permanente Southern California, Pasadena, California, USA.
Ming-Sum LeeDepartment of Health Systems Science, Kaiser Permanente Bernard J. Tyson School of Medicine, Pasadena, California, USA.
Brian HocumDivision of Pharmaceuticals, Medical Affairs (HEOR), Bayer U.S. LLC, Whippany, New Jersey, USA.
Kai VogtländerMedical Affairs Statistics, Bayer AG, Wuppertal, Germany.
Khaled AbdelgawwadGlobal Medical and Evidence, Bayer AG, Berlin, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Novel algorithms were developed to identify International Society on Thrombosis and Haemostasis (ISTH)-defined major and clinically relevant nonmajor (CRNM) bleeding events using electronic health record (EHR) data. Objectives: We applied and validated these EHR-based algorithms in a large US integrated health system. Methods: We conducted a retrospective cohort and chart review study of adults with atrial fibrillation and ≥1 oral anticoagulant (OAC) prescription between 2013 and 2021. EHR-based algorithms were applied to identify ISTH-defined major and CRNM bleeding from the first OAC fill until disenrollment, death, or June 2022, whichever occurred first. Through conditional sampling, 433 patients were selected for clinician chart review. Sensitivity, specificity, positive predictive values, and negative predictive values were calculated. Results: Among 62,106 adults (mean age, 72.6 years; 57.1% male), the ISTH major and CRNM bleeding event rates per the algorithms were 3.54 and 19.67 per 100 person-years, respectively, during a median follow-up of 4.3 years. Sensitivity and specificity were 0.91 (95% CI, 0.86, 0.94) and 0.92 (95% CI, 0.88, 0.95) for major bleeding, respectively, and 0.66 (95% CI, 0.54, 0.76) and 0.86 (95% CI, 0.82, 0.90) for CRNM bleeding, respectively. The positive and negative predictive values were 0.91 (95% CI, 0.87, 0.94) and 0.92 (95% CI, 0.89, 0.95) for major bleeding, respectively, and 0.52 (95% CI, 0.44, 0.60) and 0.92 (95% CI, 0.90, 0.94) for CRNM bleeding, respectively. Conclusion: The EHR-based ISTH major bleeding algorithm demonstrated high sensitivity and specificity, while the CRNM bleeding algorithm showed suboptimal sensitivity. The EHR-based ISTH algorithms may enhance understanding of OAC safety using real-world evidence.

Indexed as

AlgorithmsAnticoagulantsAtrial FibrillationElectronic Health RecordsHemorrhageThrombosisAgedAged, 80 and overFemaleHemostasisHumansMaleMiddle AgedPredictive Value of TestsReproducibility of ResultsRetrospective StudiesAnticoagulantsalgorithmsanticoagulantselectronic health recordshemorrhagevalidation studies as topic

Identifiers

PMID42232248
PMCPMC13224354

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.