Literature DB >> 34292640

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

Anita M Loughlin1,2, Stephanie E Chiuve3, Gally Reznor1, Michael Doherty1, Stacey A Missmer4,5, Andrea K Chomistek1, Cheryl Enger1.   

Abstract

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.

Entities:  

Keywords:  adenomyosis; algorithm; electronic health record data; methods

Mesh:

Year:  2021        PMID: 34292640     DOI: 10.1002/pds.5333

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


  1 in total

1.  The Prevalence and Clinical Impact of Adenomyosis in Pregnancy-Related Hysterectomy.

Authors:  Michele Orsi; Edgardo Somigliana; Fulvia Milena Cribiù; Gianluca Lopez; Laura Buggio; Manuela Wally Ossola; Enrico Ferrazzi
Journal:  J Clin Med       Date:  2022-08-17       Impact factor: 4.964

  1 in total

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