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

Overview of attention for article published in Journal of Blood Medicine, July 2017
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  • Above-average Attention Score compared to outputs of the same age and source (60th percentile)

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2 X users
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1 Facebook page
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1 Google+ user

Citations

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32 Mendeley
Title
Identification of people with acquired hemophilia in a large electronic health record database
Published in
Journal of Blood Medicine, July 2017
DOI 10.2147/jbm.s136060
Pubmed ID
Authors

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

Abstract

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. To identify patients with AH using an EHR database. 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. 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. 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.

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The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 32 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 32 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 7 22%
Researcher 6 19%
Student > Doctoral Student 2 6%
Student > Bachelor 2 6%
Professor 2 6%
Other 3 9%
Unknown 10 31%
Readers by discipline Count As %
Medicine and Dentistry 5 16%
Computer Science 4 13%
Nursing and Health Professions 2 6%
Economics, Econometrics and Finance 2 6%
Social Sciences 2 6%
Other 3 9%
Unknown 14 44%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 05 August 2017.
All research outputs
#15,305,492
of 25,584,565 outputs
Outputs from Journal of Blood Medicine
#142
of 318 outputs
Outputs of similar age
#171,516
of 327,299 outputs
Outputs of similar age from Journal of Blood Medicine
#3
of 5 outputs
Altmetric has tracked 25,584,565 research outputs across all sources so far. This one is in the 38th percentile – i.e., 38% of other outputs scored the same or lower than it.
So far Altmetric has tracked 318 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.3. This one has gotten more attention than average, scoring higher than 53% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 327,299 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 45th percentile – i.e., 45% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 5 others from the same source and published within six weeks on either side of this one. This one has scored higher than 2 of them.