Identification of subjects with polycystic ovary syndrome using electronic health records. Issue 1 (December 2015)

• Record Type: Journal Article
• Authors: Castro, Victor; Shen, Yuanyuan; Yu, Sheng; Finan, Sean; Pau, Cindy; Gainer, Vivian; Keefe, Candace; Savova, Guergana; Murphy, Shawn; Cai, Tianxi; Welt, Corrine
• Is Part Of: Reproductive biology and endocrinology. Volume 13:Issue 1(2015)
• Page Start: 1
• Page End: 8
• Publication Date: 2015-12
• Subjects: Natural language processing -- ICD9 code -- Hyperandrogenism -- Polycystic ovary morphology; Reproduction -- Periodicals; Generative organs -- Pathophysiology -- Periodicals; Reproduction -- Endocrine aspects -- Periodicals; Endocrine glands -- Diseases -- Periodicals; Embryology -- Periodicals; 571.8
• DOI: 10.1186/s12958-015-0115-z
• Languages: English
• ISSNs: 1477-7827

Abstract

Abstract Background Polycystic ovary syndrome (PCOS) is a heterogeneous disorder because of the variable criteria used for diagnosis. Therefore, International Classification of Diseases 9 (ICD-9) codes may not accurately capture the diagnostic criteria necessary for large scale PCOS identification. We hypothesized that use of electronic medical records text and data would more specifically capture PCOS subjects. Methods S ubjects with PCOS were identified in the Partners Healthcare Research Patients Data Registry by searching for the term "polycystic ovary syndrome" using natural language processing (n = 24, 930). A training subset of 199 identified charts was reviewed and categorized based on likelihood of a true Rotterdam PCOS diagnosis, i.e. two out of three of the following: irregular menstrual cycles, hyperandrogenism and/or polycystic ovary morphology. Data from the history, physical exam, laboratory and radiology results were codified and extracted from notes of definite PCOS subjects. Thirty-two terms were used to build an algorithm for identifying definite PCOS cases and applied to the rest of the dataset. The positive predictive value cutoff was set at 76.8 % to maximize the number of subjects available for study. A true positive predictive value for the algorithm was calculated after review of 100 charts from subjects identified as definite PCOS cases with at least two documented Rotterdam criteria. The positive predictive value was compared to that calculated using 200 charts identified using the ICD-9 code for PCOS (256.4;n = 13, 670). In addition, a cohort of previously recruited PCOS subjects was submitted for algorithm validation. Results Chart review demonstrated that 64 % were confirmed as definitely PCOS using the algorithm, with a 9 % false positive rate. 66 % of subjects identified by ICD-9 code for PCOS could be confirmed as definitely PCOS, with an 8.5 % false positive rate. There was no significant difference in the positive predictive values using the two methods (p = 0.2). However, the number of charts that had insufficient confirmatory data was lower using the algorithm (5 % vs 11 %;p < 0.04). Of 477 subjects with PCOS recruited and examined individually and present in the database as patients, 451 were found within the algorithm dataset. Conclusions Extraction of text parameters along with codified data improves the confidence in PCOS patient cohorts identified using the electronic medical record. However, the positive predictive value was not significantly different when using ICD-9 codes or the specific algorithm. Further studies are needed to determine the positive predictive value of the two methods in additional electronic medical record datasets.