The impaired postpartum sexual functioning, the high prevalence of dyspareunia postpartum and their impact on QOL indicate the need for further investigation and extensive counselling of pregnant women, especially primiparous women, about sexuality after childbirth.

Objective: To develop a prediction model to predict surgical re-intervention within two years after endometrial ablation (EA) by using a random forest technique (RF). The performance of the developed prediction model was then compared with a previously published multivariate logistic regression model (LR) (1). Design: Retrospective cohort study. Setting: Data from two non-university teaching hospitals in the Netherlands were used. Population: 446 pre-menopausal women who have had an EA for heavy menstrual bleeding between January 2004 and April 2013. Methods: The RF model was trained in MATLAB (2018b) using the TreeBagger function in the Statistics and Machine Learning Toolbox. Main outcome measures: The performance of the two models was compared using the area under the Receiving Operating Characteristic (ROC) curve (AUROC). Measurements and Main Results: The LR model had an AUC of 0.71 (95% CI 0.64-0.78). The RF model had an AUC of 0.63 (95% CI 0.54-0.71). and an AUC of 0.65 (95% CI 0.56-0.74) after hyperparameter optimization. Conclusion: The RF model is not superior compared to the LR model in predicting the outcome of surgical re-intervention within two years after EA. Machine learning techniques are gaining popularity in development of clinical prediction tools, but they are not necessarily superior to traditional statistical logistic regression techniques. The performance of a model is influenced by the sample size and the number of features, hyperparameter tuning and the linearity of associations. Both techniques should be considered when developing a prediction model.

Background Five percent of pre-menopausal women experience abnormal uterine bleeding. Endometrial ablation (EA) is one of the treatment options for this common problem. However, this technique shows a decrease in patient satisfaction and treatment efficacy on the long term. Study objective To develop a prediction model to predict surgical re-intervention (for example re-ablation or hysterectomy) within 2 years after endometrial ablation (EA) by using machine learning (ML). The performance of the developed prediction model was compared with a previously published multivariate logistic regression model (LR). Design This retrospective cohort study, with a minimal follow-up time of 2 years, included 446 pre-menopausal women (18+) that underwent an EA for complaints of heavy menstrual bleeding. The performance of the ML and the LR model was compared using the area under the receiving operating characteristic (ROC) curve. Results We found out that the ML model (AUC of 0.65 (95% CI 0.56–0.74)) is not superior compared to the LR model (AUC of 0.71 (95% CI 0.64–0.78)) in predicting the outcome of surgical re-intervention within 2 years after EA. Based on the ML model, dysmenorrhea and duration of menstruation have the highest impact on the surgical re-intervention rate. Conclusion Although machine learning techniques are gaining popularity in development of clinical prediction tools, this study shows that ML is not necessarily superior to the traditional statistical LR techniques. Both techniques should be considered when developing a clinical prediction model. Both models can identify the clinical predictors to surgical re-intervention and contribute to the shared decision-making process in the clinical practice.