Identification of subjects with polycystic ovary syndrome using electronic health records

Abstract

BackgroundPolycystic 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.MethodsSubjects 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.ResultsChart 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.ConclusionsExtraction 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.Electronic supplementary materialThe online version of this article (doi:10.1186/s12958-015-0115-z) contains supplementary material, which is available to authorized users.

The emerging landscape of health research based on biobanks linked to electronic health records: Existing resources, statistical challenges, and potential opportunities

“…Unstructured data have also been used to define phenotypes, particularly for diseases with unreliable ICD9 classifications such as some psychiatric diseases, using natural language processing methods . Such methods can also be used to obtain patient measures such as smoking status .…”

Hidradenitis suppurativa and polycystic ovarian syndrome: Systematic review and meta‐analysis

“…However, there have been few formal studies to assess the relationship between HS and polycystic ovarian syndrome (PCOS). Furthermore, PCOS is one of the most common disorders in young adult females and often presents with hyperandrogenism, irregular menses and polycystic ovaries, diagnosed in accordance with the Rotterdam criteria . On the other hand, comorbidities of HS and PCOS also have considerable overlap, including obesity, diabetes melitus and metabolic syndrome …”

Diabetes risk in women with gestational diabetes mellitus and a history of polycystic ovary syndrome: a retrospective cohort study

“…We included women with either a diagnosis of PCO, as in a previous study [17], or with two out of three of the diagnoses incorporated in the Rotterdam criteria. We included women with either a diagnosis of PCO, as in a previous study [17], or with two out of three of the diagnoses incorporated in the Rotterdam criteria.…”

“…We applied ICD codes based on the Rotterdam criteria to the 3-year period prior to estimated conception to identify PCOS (two out of three of the diagnoses of polycystic ovaries (PCO), oligo/amenorrhoea, clinical or biochemical androgen excess) or diagnosis of PCO (ICD-9 2564 or ICD-10 E282) as in a previous study [17] (Appendix S1). We applied ICD codes based on the Rotterdam criteria to the 3-year period prior to estimated conception to identify PCOS (two out of three of the diagnoses of polycystic ovaries (PCO), oligo/amenorrhoea, clinical or biochemical androgen excess) or diagnosis of PCO (ICD-9 2564 or ICD-10 E282) as in a previous study [17] (Appendix S1).…”