Literature DB >> 31313427

A concept-wide association study to identify potential risk factors for nonadherence among prevalent users of antihypertensives.

Karandeep Singh1, Niteesh K Choudhry2,3, Alexis A Krumme2, Caroline McKay4, Newell E McElwee5, Joe Kimura6, Jessica M Franklin2.   

Abstract

PURPOSE: We sought to determine whether an association study using information contained in clinical notes could identify known and potentially novel risk factors for nonadherence to antihypertensive medications.
METHODS: We conducted a retrospective concept-wide association study (CWAS) using clinical notes to identify potential risk factors for medication nonadherence, adjusting for age, sex, race, baseline blood pressure, estimated glomerular filtration rate, and a combined comorbidity score. Participants included Medicare beneficiaries 65 years and older receiving care at the Harvard Vanguard Medical Associates network from 2010-2012 and enrolled in a Medicare Advantage program. Concepts were extracted from clinical notes in the year prior to the index prescription date for each patient. We tested associations with the outcome for 5013 concepts extracted from clinical notes in a derivation cohort (4382 patients) and accounted for multiple hypothesis testing by using a false discovery rate threshold of less than 5% (q < .05). We then confirmed the associations in a validation cohort (3836 patients). Medication nonadherence was defined using a proportion of days covered (PDC) threshold less than 0.8 using pharmacy claims data.
RESULTS: We found 415 concepts associated with nonadherence, which we organized into 11 clusters using a hierarchical clustering approach. Volume depletion and overload, assessment of needs at the point of discharge, mood disorders, neurological disorders, complex coordination of care, and documentation of noncompliance were some of the factors associated with nonadherence.
CONCLUSIONS: This approach was successful in identifying previously described and potentially new risk factors for antihypertensive nonadherence using the clinical narrative.
© 2019 John Wiley & Sons, Ltd.

Entities:  

Keywords:  electronic health record; hypertension; medications; nonadherence; pharmacoepidemiology

Mesh:

Substances:

Year:  2019        PMID: 31313427     DOI: 10.1002/pds.4850

Source DB:  PubMed          Journal:  Pharmacoepidemiol Drug Saf        ISSN: 1053-8569            Impact factor:   2.890


  2 in total

Review 1.  Artificial intelligence-enabled decision support in nephrology.

Authors:  Tyler J Loftus; Benjamin Shickel; Tezcan Ozrazgat-Baslanti; Yuanfang Ren; Benjamin S Glicksberg; Jie Cao; Karandeep Singh; Lili Chan; Girish N Nadkarni; Azra Bihorac
Journal:  Nat Rev Nephrol       Date:  2022-04-22       Impact factor: 42.439

2.  Development and Validation of a Natural Language Processing Algorithm to Extract Descriptors of Microbial Keratitis From the Electronic Health Record.

Authors:  Maria A Woodward; Nenita Maganti; Leslie M Niziol; Sejal Amin; Andrew Hou; Karandeep Singh
Journal:  Cornea       Date:  2021-12-01       Impact factor: 2.651

  2 in total

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