Identification of subjects with polycystic ovary syndrome using electronic health records.

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: Subjects 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.

Cigarette smoking, nicotine levels and increased risk for metabolic syndrome in women with polycystic ovary syndrome.

Women with polycystic ovary syndrome (PCOS) are at risk for metabolic syndrome, which may be exacerbated by smoking. We hypothesized that smoking worsens androgen levels and the metabolic profile in women with PCOS. PCOS smokers (n = 47) and non-smokers (n = 64) and control smokers (n = 30) and non-smokers (n = 28), aged 18-45 years, underwent anthropomorphic measurements, pelvic ultrasound and blood sampling. Smokers had higher cotinine (801 ± 83 versus <11 nmol/L; smokers versus non-smokers, respectively; p < 0.001) and nicotine levels (37 ± 4 versus <12 µmol/L; p < 0.001). Triglyceride levels were higher in women with PCOS who smoked compared to non-smokers (1.55 ± 0.18 versus 0.95 ± 0.08 mmol/L; p < 0.001), even when adjusted for BMI. Metabolic syndrome was more common in smokers with PCOS compared to non-smokers with PCOS and smokers who were controls (28.6 versus 3.6%; p = 0.02). There were no differences in reproductive parameters including androgen levels. Cotinine (r = 0.3; p < 0.001) and nicotine levels (r = 0.2; p = 0.005) correlated with triglycerides. Nicotine levels also correlated with pulse rate (r = 0.2; p = 0.02) and waist:hip ratio (WHR; r = 0.2; p = 0.02). Taken together, smoking may worsen the already high risk for metabolic syndrome in women with PCOS.