Predicting the development of stress urinary incontinence 3 years after hysterectomy

We aimed to develop a prediction rule to predict the individual risk to develop stress urinary incontinence (SUI) after hysterectomy. Prospective observational study with 3-year follow-up among women who underwent abdominal or vaginal hysterectomy for benign conditions, excluding vaginal prolapse, and who did not report SUI before surgery (n = 183). The presence of SUI was assessed using a validated questionnaire. Significant prognostic factors for de novo SUI were BMI (OR 1.1 per kg/m(2), 95% CI 1.0-1.2), younger age at time of hysterectomy (OR 0.9 per year, 95% CI 0.8-1.0) and vaginal hysterectomy (OR 2.3, 95% CI 1.0-5.2). Using these variables, we developed the following rule to predict the risk of developing SUI: 32 + BMI-age + (7.5 × route of surgery). We defined a prediction rule that can be used to counsel patients about their individual risk on developing SUI following hysterectom

External validation and calibration of IVFpredict:A national prospective cohort study of 130,960 in vitro fertilisation Cycles

© 2015 Smith et al. Background Accurately predicting the probability of a live birth after in vitro fertilisation (IVF) is important for patients, healthcare providers and policy makers. Two prediction models (Templeton and IVFpredict) have been previously developed from UK data and are widely used internationally. The more recent of these, IVFpredict, was shown to have greater predictive power in the development dataset. The aim of this study was external validation of the two models and comparison of their predictive ability. Methods and Findings 130,960 IVF cycles undertaken in the UK in 2008-2010 were used to validate and compare the Templeton and IVFpredict models. Discriminatory power was calculated using the area under the receiver-operator curve and calibration assessed using a calibration plot and Hosmer-Lemeshow statistic. The scaled modified Brier score, with measures of reliability and resolution, were calculated to assess overall accuracy. Both models were compared after updating for current live birth rates to ensure that the average observed and predicted live birth rates were equal. The discriminative power of both methods was comparable: the area under the receiver-operator curve was 0.628 (95% confidence interval (CI): 0.625-0.631) for IVFpredict and 0.616 (95% CI: 0.613-0.620) for the Templeton model. IVFpredict had markedly better calibration and higher diagnostic accuracy, with calibration plot intercept of 0.040 (95% CI: 0.017-0.063) and slope of 0.932 (95% CI: 0.839 - 1.025) compared with 0.080 (95% CI: 0.044-0.117) and 1.419 (95% CI: 1.149-1.690) for the Templeton model. Both models underestimated the live birth rate, but this was particularly marked in the Templeton model. Updating the models to reflect improvements in live birth rates since the models were developed enhanced their performance, but IVFpredict remained superior. Conclusion External validation in a large population cohort confirms IVFpredict has superior discrimination and calibration for informing patients, clinicians and healthcare policy makers of the probability of live birth following IVF