Evidence map›Paper›PMID 28769599›Full record

ArticleJournal of blood medicine2017

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

Michael Wang, Anissa Cyhaniuk, David L Cooper, Neeraj N Iyer

Open access · goldAbstract read
In one paragraph

Article in Journal of blood medicine, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
0.9field-weighted citation impact, top 24% of its field
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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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

7 citing papers in PubMed, 8 citations in OpenAlex.

  1. Article
  2. Article
  3. Leveraging unstructured data to identify hereditary angioedema patients in electronic medical records.Allergy, asthma, and clinical immunology : official journal of the Canadian Society of Allergy and Clinical Immunology · 2021
    Article
  4. medExtractR: A targeted, customizable approach to medication extraction from electronic health records.Journal of the American Medical Informatics Association : JAMIA · 2020
    Article
  5. Article
  6. Identifying surgical site infections in electronic health data using predictive models.Journal of the American Medical Informatics Association : JAMIA · 2018
    Article
  7. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors at 3 institutions in 1 country.

Michael WangHemophilia and Thrombosis Center, University of Colorado School of Medicine, Aurora, CO.
Anissa CyhaniukAC Analytic Solutions, Barrington, IL.
David L CooperClinical Development, Medical and Regulatory Affairs, Novo Nordisk Inc., Plainsboro, NJ, USA.
Neeraj N IyerClinical Development, Medical and Regulatory Affairs, Novo Nordisk Inc., Plainsboro, NJ, USA.
Novo Nordisk (United States) · USBarrington Public Library · USIndiana Hemophilia and Thrombosis Center · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundElectronic 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.

aimTo identify patients with AH using an EHR database.

methodsRecords 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.

resultsOf 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.

conclusionIn 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.

Indexed as

acquired hemophiliabig datadatabaseelectronic health record

Identifiers

PMID28769599
PMCPMC5529096
OpenAlexW2737806091

What OpenQuestion holds

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LicenceCC BY-NC
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Registered trials

None linked

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.