Screening for Androgen Excess in Women: Accuracy of Self-Reported Excess Body Hair Growth and Menstrual Dysfunction

Abstract

Context Epidemiologic studies of polycystic ovary syndrome (PCOS) are limited, especially in populations where diagnostic resources are less available. In these settings, an accurate, low-cost screening tool would be invaluable. Objective To test the use of a simple questionnaire to identify women at increased risk for PCOS and androgen excess (AE) disorders. Study Design Prospective cohort study from 2006–2010. Setting Community-based. Participants Women aged 14 to 45 years. Intervention A screening telephone questionnaire consisting of 3 questions was tested, where participants were asked to self-assess the presence/absence of male-like hair and menstrual irregularity. Participants were then invited to undergo a direct examination, including completing a medical history and undergoing a modified Ferriman-Gallwey (mFG) hirsutism score, ovarian ultrasound, and measurement of circulating total and free testosterone, DHEAS, TSH, prolactin and 17-hydroxyprogesterone levels. Main Outcome Measure Accuracy of questionnaire in predicting PCOS, AE, and irregular menses. Results Participants with self-assessed irregular menses and/or excess hair were labeled “Possible Androgen Excess (Poss-AE)” and those self-assessed with regular menses and no excess hair were labeled “Probable Non-Androgen Excess (Non-AE).” The study was completed in 206/298 (69%) of the Poss-AE and in 139/192 (73%) of the Non-AE. Of Poss-AE and Non-AE subjects, 82.5% and 15.8%, respextively, presented with PCOS. The calculated sensitivity, specificity, positive predictive value, and negative predictive value of the 3-question telephone survey to predict PCOS was 89%, 78%, 85%, and 83%, respectively. Conclusions A simple telephone questionnaire, based on self-assessment of body hair and menstrual status, can be used with a high predictive value to identify women at risk for AE disorders, including PCOS, and to detect healthy controls. This approach could be an important tool for needed epidemiologic studies.