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Identification of people with acquired hemophilia in a large electronic health record database

Authors Wang M, Cyhaniuk A, Cooper DL, Iyer NN

Received 3 March 2017

Accepted for publication 2 June 2017

Published 19 July 2017 Volume 2017:8 Pages 89—97


Checked for plagiarism Yes

Review by Single-blind

Peer reviewers approved by Dr Colin Mak

Peer reviewer comments 2

Editor who approved publication: Dr Martin Bluth

Michael Wang,1 Anissa Cyhaniuk,2 David L Cooper,3 Neeraj N Iyer3

1Hemophilia and Thrombosis Center, University of Colorado School of Medicine, Aurora, CO, 2AC Analytic Solutions, Barrington, IL, 3Clinical Development, Medical and Regulatory Affairs, Novo Nordisk Inc., Plainsboro, NJ, USA

Background: Electronic health records (EHRs) can provide insights into diagnoses, treatment patterns, and clinical outcomes. Acquired hemophilia (AH) is an ultrarare bleeding disorder characterized by factor VIII inhibiting autoantibodies.
Aim: To identify patients with AH using an EHR database.
Methods: Records were accessed from a large EHR database (Humedica) between January 1, 2007 and July 31, 2013. Broad selection criteria were applied using the International Classification of Diseases, Ninth Revision, clinical modification (ICD-9-CM) code for intrinsic circulating anticoagulants (286.5 and all subcodes) and confirmation of records 6 months before and 12 months after the first diagnosis. Additional selection criteria included mention of “bleeding” within physician notes identified via natural language processing output and a normal prothrombin time and prolonged activated partial thromboplastin time.
Results: Of 6,348 patients with a diagnosis code of 286.5 or any subcodes, 16 males and 15 females met the selection criteria. The most common bleeding locations reported was gastrointestinal (23%), vaginal (16%), and endocrine (13%). A wide range of comorbidities was reported. Natural language processing identified chart note mention of “hemophilia” in 3 patients (10%), “bruise” in 15 patients (48%), and “pain” in all 31 patients. No patients received a prescription for approved/recommended AH treatments. Four patient cases were reviewed to validate whether the identified cohort had AH; each patient had bleeding symptoms and a normal prothrombin time and prolonged activated partial thromboplastin time, although none received hemostatic treatments.
Conclusion: In ultrarare disorders, ICD-9-CM coding alone may be insufficient to identify patient cohorts; multimodal analysis combined with in-depth reviews of physician notes may be more effective.

Keywords: acquired hemophilia, electronic health record, database, big data

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