{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,21]],"date-time":"2026-08-21T11:53:02Z","timestamp":1787313182034,"version":"build-2736575974"},"reference-count":28,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2018,11,15]],"date-time":"2018-11-15T00:00:00Z","timestamp":1542240000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Clinical Endocrinology"],"published-print":{"date-parts":[[2019,1]]},"abstract":"<jats:title>Summary<\/jats:title><jats:sec><jats:title>Objective<\/jats:title><jats:p>The aim of this study was to develop a simple phenotypic algorithm that can capture the underlying clinical and hormonal abnormalities to help in the diagnosis and risk stratification of polycystic ovary syndrome (PCOS).<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>The study consisted of 111 women with PCOS fulfilling the Rotterdam diagnostic criteria and 67 women without PCOS. A Firth's penalized logistic regression model was used for independent variable section. Model optimism, discrimination and calibration were assessed using bootstrapping, area under the curve (AUC) and Hosmer\u2010Lemeshow statistics, respectively. The prognostic index (PI) and risk score for developing PCOS were calculated using independent variables from the regression model.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Firth penalized logistic regression model with backward selection identified four independent predictors of PCOS namely free androgen index [\u03b2 0.30 (0.12), <jats:italic>P<\/jats:italic>\u00a0=\u00a00.008], 17\u2010OHP [\u03b2\u00a0=\u00a00.20 (0.01), <jats:italic>P<\/jats:italic>\u00a0=\u00a00.026], anti\u2010mullerian hormone [AMH; \u03b2\u00a0=\u00a00.04 (0.01) <jats:italic>P<\/jats:italic>\u00a0&lt;\u00a00.0001] and waist circumference [\u03b2\u00a0=\u00a00.08 (0.02), <jats:italic>P<\/jats:italic>\u00a0&lt;\u00a00.0001]. The model estimates indicated high internal validity (minimal optimism on 1000\u2010fold bootstrapping), good discrimination ability (bias corrected <jats:italic>c<\/jats:italic>\u2010statistic\u00a0=\u00a00.90) and good calibration (Hosmer\u2010Lemeshow <jats:italic>\u03c7<\/jats:italic><jats:sup>2<\/jats:sup>\u00a0=\u00a03.7865). PCOS women with a high\u2010risk score (q1\u00a0+\u00a0q2\u00a0+\u00a0q3 vs q4) presented with a worse metabolic profile characterized by a higher 2\u2010hour glucose (<jats:italic>P<\/jats:italic>\u00a0=\u00a00.01), insulin (<jats:italic>P<\/jats:italic>\u00a0=\u00a00.0003), triglycerides (<jats:italic>P<\/jats:italic>\u00a0=\u00a00.0005), C\u2010reactive protein (<jats:italic>P<\/jats:italic>\u00a0&lt;\u00a00.0001) and low HDL\u2010cholesterol (<jats:italic>P<\/jats:italic>\u00a0=\u00a00.02) as compared to those with lower risk score for PCOS.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>We propose a simple four\u2010variable model, which captures the underlying clinical and hormonal abnormalities in PCOS and can be used for diagnosis and metabolic risk stratification in women with PCOS.<\/jats:p><\/jats:sec>","DOI":"10.1111\/cen.13879","type":"journal-article","created":{"date-parts":[[2018,10,19]],"date-time":"2018-10-19T16:06:53Z","timestamp":1539965213000},"page":"162-169","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Development of a novel risk prediction and risk stratification score for polycystic ovary syndrome"],"prefix":"10.1111","volume":"90","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3859-3118","authenticated-orcid":false,"given":"Harshal","family":"Deshmukh","sequence":"first","affiliation":[{"name":"Department of Academic Diabetes, Endocrinology and Metabolism, Hull York Medical School University of Hull Hull UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maria","family":"Papageorgiou","sequence":"additional","affiliation":[{"name":"Department of Academic Diabetes, Endocrinology and Metabolism, Hull York Medical School University of Hull Hull 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