1016 bles with P< 0.3 were kept in the model. This strategy eliminates most of the purely random variables and improves the chance that the model will perform well in future patients [15]. The predictive performance of the three resulting models was transformed into receiver operating characteristic (ROC) curves and the areas under the curves (AUC) were compared. There was a limited number of missing values on many of the variables. As multivariate analysis is severely hampered by missing values and more importantly, results may be biassed, we used multiple imputation with ten datasets [16]. Internal validation of the models was performed by a bootstrap re-sampling procedure: the model building process was repeated 200 times after creating 200 new datasets (bootstrap samples) by randomly drawing cases (with replacement) from the original data. The variable selection and estimation procedure was performed on each bootstrap sample. This yielded 200 sets of predictors and parameter estimates. The model estimates of each bootstrap sample were evaluated on the basis of the original data. On average, the predicted and observed outcomes should agree. Predictions that deviate strongly from the mean usually differ greatly from the observed outcomes due to over- fitting of the model. The size of the over- fitting effect was estimated by averaging the 200 bootstrap samples. This produced a shrinkage factor c to compensate for the over- fitting [17]. The bootstrap method was also used to estimate the amount of optimism in the AUC by optimally fine- tuning a model and subsequently evaluating its predictive performance on the same data [17]. The prediction model that showed the highest AUC was translated into a pragmatic prognostic score, the Slieker POP score. For each prognostic factor in the model, the regression coefficients in the logistic model were converted into score points. For ease of use, the regression coef- ficients were scaled and rounded to whole numbers, such that the minimum and maximum score of women in our data set were 0 and 100, respectively. From a graph, the corresponding risk of POP can be read off. The analyses were performed using the Statistical Package for Social Science (SPSS) 15.0. The Medical Ethics Research Committee of the Erasmus Medical Centre in Rotterdam, the Netherlands, approved this study. Results Response rate The response rate to the questionnaire was 62.7% (1,869 of 2,979). In the group of 1,869 responders, 472 (15.8%) women refused to participate, 1,397 (46.8%; group 1) women agreed to fill out the large questionnaire and 1,140 Int Urogynecol J (2009) 20:1013–1021 (38.2%; group 2) agreed to fill out the large questionnaire and undergo vaginal examination. In the non-responder group 3, 20.8% returned the completed short questionnaire (620 of 2,979). Feeling vaginal bulging was reported by 6.7% (n = 41) of this non-responder group versus 9.8% in the responder group (135 of 1,397). From group 2, 800 out of the 1,140 women who consented to undergo vaginal examination were selected at random and sent an invitation for vaginal examination: 649 women participated (81.1%), which was 21.7% of the total study population and 46.4% of the women who filled in the questionnaire. The vaginal examination group of 649 women was stratified into an asymptomatic control group (n = 570) and a symptomatic (n = 79) group in which the women had reported seeing and/or feeling vaginal bulging. Combining the data on the large and short questionnaires from the responders and the initial non-responders (1,397 +620= 2,017) revealed the report of a feeling of vaginal bulging prevalence rate of 8.7% (n = 176). Baseline characteristics Baseline characteristics of the total study population and the different groups (group 1 the total group, vaginal examination group 2 divided into a symptomatic group and an asymptomatic group and the non-responder group 3) are shown in Table 1. No significant differences were found between group 1 and group 3 or between the asymptomatic women and the symptomatic women in group 2. The prevalence of POP per POP stage in relation with the report of vaginal bulging in our general population is presented in Table 2. The overall prevalence of ≥stage 2B (all the women with stages 2B, 3 and 4) was 17.5% (114 of 649), of whom 30.7% (35 of 79) had symptoms of vaginal bulging (n = 35). The results of the multivariate analyses on POP stages 2A, 2B and 2C are shown in Table 3. Significantly higher odds ratios were found especially in stages 2B (at the hymen) and 2C (beyond the hymen) for the report of vaginal bulging (3.80 and 5.47, resp.), for ageing (1.04 and 1.04, resp.), parity of 2 (2.84 and 3.06, resp.), parity of ≥3 (2.63 and 3.33, resp.), and POP in the mother (1.96 and 2.00, resp.). The ROC curve in Fig. 2 shows that the largest AUC were 0.759 for ‘beyond the hymen’ and 0.723 for ‘at or beyond the hymen’. The AUC values were corrected for optimism 0.672 and 0.648, respectively. Due to small sample sizes, no ORs could be calculated in the multivariate analyses on HRT, hysterectomy, inconti- nence during pregnancy and incontinence in the mother. In Table 4, the Slieker-POP-Score-Chart and POP prognostic index are presented. The score chart is based on POP stage 2B (and 2C), i.e. POP at or beyond the hymen. After filling