Polycystic ovary syndrome: validated questionnaire for use in diagnosis.

Can Fam Physician

Division of Endocrinology and Metabolism, University of Calgary, Alberta.

Published: June 2007

Objective: To construct and validate a questionnaire for use in diagnosis of polycystic ovary syndrome (PCOS).

Design: All participants completed a questionnaire, which asked clinical questions designed to assist in the diagnosis of PCOS, before their appointments with an endocrinologist. Following completion of the questionnaire, the endocrinologist (blinded to the answers) made or excluded a diagnosis of PCOS using clinical criteria and biochemical data as indicated. Questions were then evaluated for their power to predict PCOS, and a model was constructed using the most reliable items to establish a system to predict a diagnosis of PCOS.

Setting: An outpatient reproductive endocrinology clinic in Calgary, Alta.

Participants: Adult women patients who had been referred to the clinic. Fifty patients with PCOS and 50 patients without PCOS were included in the study.

Main Outcome Measures: Demographic information, medical history, related diagnoses, menstrual history, and fertility history.

Results: A history of infrequent menses, hirsutism, obesity, and acne were strongly predictive of a diagnosis of PCOS, whereas a history of failed pregnancy attempts was not useful. A history of nipple discharge outside of pregnancy strongly predicted no diagnosis of PCOS. We constructed a 4-item questionnaire for use in diagnosis of PCOS; the questionnaire yielded a sensitivity of 85% and a specificity of 85% on multivariate logistic regression and a sensitivity of 77% and a specificity of 94% using the 4-item questionnaire. Predictive accuracy was validated using a second sample of 117 patients, in addition to internal validation using bootstrap analysis.

Conclusion: We have constructed a simple clinical tool to help diagnose PCOS. This questionnaire can be easily incorporated into family physicians' busy practices.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1949220PMC

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