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Polycystic ovary syndrome (PCOS) is a common reproductive disorder associated with many characteristic features, including hyperandrogenaemia, insulin resistance and obesity which may have significant implications for pregnancy outcomes and long-term health of the woman. This meta-analysis was conducted to evaluate the risk of pregnancy and neonatal complications in women with PCOS. Electronic databases were searched for the following MeSH headings: PCOS, hyperandrogenism, pregnancy outcome, pregnancy complications, diabetes mellitus, type II. A handsearch of human reproduction and fertility and sterility was also conducted. Studies in which pregnancy outcomes in women with PCOS were compared with controls were considered for inclusion in this meta-analysis. Fifteen of 525 identified studies were included, involving 720 women presenting with PCOS and 4505 controls. Women with PCOS demonstrated a significantly higher risk of developing gestational diabetes [odds ratio (OR) 2.94; 95% confidence interval (CI): 1.70-5.08], pregnancy-induced hypertension (OR 3.67; 95% CI: 1.98-6.81), pre-eclampsia (OR 3.47; 95% CI: 1.95-6.17) and preterm birth (OR 1.75; 95% CI: 1.16-2.62). Their babies had a significantly higher risk of admission to a neonatal intensive care unit (OR 2.31; 95% CI: 1.25-4.26) and a higher perinatal mortality (OR 3.07; 95% CI: 1.03-9.21), unrelated to multiple births. In conclusion, women with PCOS are at increased risk of pregnancy and neonatal complications. Pre-pregnancy, antenatal and intrapartum care should be aimed at reducing these risks.

A logistic regression model may be used to provide predictions of outcome for individual patients at another centre than where the model was developed. When empirical data are available from this centre, the validity of predictions can be assessed by comparing observed outcomes and predicted probabilities. Subsequently, the model may be updated to improve predictions for future patients. As an example, we analysed 30-day mortality after acute myocardial infarction in a large data set (GUSTO-I, n = 40 830). We validated and updated a previously published model from another study (TIMI-II, n = 3339) in validation samples ranging from small (200 patients, 14 deaths) to large (10,000 patients, 700 deaths). Updated models were tested on independent patients. Updating methods included re-calibration (re-estimation of the intercept or slope of the linear predictor) and more structural model revisions (re-estimation of some or all regression coefficients, model extension with more predictors). We applied heuristic shrinkage approaches in the model revision methods, such that regression coefficients were shrunken towards their re-calibrated values. Parsimonious updating methods were found preferable to more extensive model revisions, which should only be attempted with relatively large validation samples in combination with shrinkage.

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