دورية أكاديمية
أعرض في EDS

Method used to identify adenomyosis and potentially undiagnosed adenomyosis in a large, U.S. electronic health record database.

التفاصيل البيبلوغرافية
العنوان: Method used to identify adenomyosis and potentially undiagnosed adenomyosis in a large, U.S. electronic health record database.
المؤلفون: Loughlin AM; Optum Epidemiology, Boston, Massachusetts, USA.; CorEvitas LLC, Waltham, Massachusetts, USA., Chiuve SE; AbbVie, Inc, North Chicago, Illinois, USA., Reznor G; Optum Epidemiology, Boston, Massachusetts, USA., Doherty M; Optum Epidemiology, Boston, Massachusetts, USA., Missmer SA; Department of Obstetrics, Gynecology, and Reproductive Biology, Michigan State University, Grand Rapids, Michigan, USA.; Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA., Chomistek AK; Optum Epidemiology, Boston, Massachusetts, USA., Enger C; Optum Epidemiology, Boston, Massachusetts, USA.
المصدر: Pharmacoepidemiology and drug safety [Pharmacoepidemiol Drug Saf] 2021 Dec; Vol. 30 (12), pp. 1675-1686. Date of Electronic Publication: 2021 Jul 29.
نوع المنشور: Journal Article; Research Support, Non-U.S. Gov't
اللغة: English
بيانات الدورية: Publisher: Wiley Country of Publication: England NLM ID: 9208369 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1099-1557 (Electronic) Linking ISSN: 10538569 NLM ISO Abbreviation: Pharmacoepidemiol Drug Saf Subsets: MEDLINE
أسماء مطبوعة: Original Publication: Chichester, West Sussex : Wiley, 1992-
مواضيع طبية MeSH: Adenomyosis*/diagnosis , Adenomyosis*/epidemiology, Cohort Studies ; Electronic Health Records ; Female ; Humans ; Hysterectomy ; Prevalence
مستخلص: Background: The prevalence of adenomyosis is underestimated due to lack of a specific diagnostic code and diagnostic delays given most diagnoses occur at hysterectomy.
Objectives: To identify women with adenomyosis using indicators derived from natural language processing (NLP) of clinical notes in the Optum Electronic Health Record database (2014-2018), and to estimate the prevalence of potentially undiagnosed adenomyosis.
Methods: An NLP algorithm identified mentions of adenomyosis in clinical notes that were highly likely to represent a diagnosis. The anchor date was date of first affirmed adenomyosis mention; baseline characteristics were assessed in the 12 months prior to this date. Characteristics common to adenomyosis cases were used to select a suitable pool of women from the underlying population, among whom undiagnosed adenomyosis might exist. A random sample of this pool was selected to form the comparator cohort. Logistic regression was used to compare adenomyosis cases to comparators; the predictive probability (PP) of being an adenomyosis case was assessed. Comparators having a PP ≥ 0.1 were considered potentially undiagnosed adenomyosis and were used to calculate the prevalence of potentially undiagnosed adenomyosis in the underlying population.
Results: Among 11 456 347 women aged 18-55 years in the underlying population, 19 503 were adenomyosis cases. Among 332 583 comparators, 22 696 women were potentially undiagnosed adenomyosis cases. The prevalence of adenomyosis and potentially undiagnosed adenomyosis was 1.70 and 19.1 per 1000 women aged 18-55 years, respectively.
Conclusions: Considering potentially undiagnosed adenomyosis, the prevalence of adenomyosis may be 10x higher than prior estimates based on histologically confirmed adenomyosis cases only.
(© 2021 John Wiley & Sons Ltd.)
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فهرسة مساهمة: Keywords: adenomyosis; algorithm; electronic health record data; methods
تواريخ الأحداث: Date Created: 20210722 Date Completed: 20211224 Latest Revision: 20211224
رمز التحديث: 20231215
DOI: 10.1002/pds.5333
PMID: 34292640
قاعدة البيانات: MEDLINE
الوصف
تدمد:1099-1557
DOI:10.1002/pds.5333