Predicting the likelihood of successful medical treatment of early pregnancy loss: development and internal validation of a clinical prediction model

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STUDY QUESTION: What are clinical predictors for successful medical treatment in case of early pregnancy loss (EPL)? SUMMARY ANSWER: Use of mifepristone, BMI, number of previous uterine aspirations and the presence of minor clinical symptoms (slight vaginal bleeding or some abdominal cramps) at treatment start are predictors for successful medical treatment in case of EPL. WHAT IS KNOWN ALREADY: Success rates of medical treatment for EPL vary strongly, between but also within different treatment regimens. Up until now, although some predictors have been identified, no clinical prediction model has been developed yet. STUDY DESIGN, SIZE, DURATION: Secondary analysis of a multicentre randomized controlled trial in 17 Dutch hospitals, executed between 28 June 2018 and 8 January 2020. PARTICIPANTS/MATERIALS, SETTING, METHODS: Women with a non-viable pregnancy between 6 and 14 weeks of gestational age, who opted for medical treatment after a minimum of 1 week of unsuccessful expectant management. Potential predictors for successful medical treatment of EPL were chosen based on literature and expert opinions. We internally validated the prediction model using bootstrapping techniques. MAIN RESULTS AND THE ROLE OF CHANCE: 237 out of 344 women had a successful medical EPL treatment (68.9%). The model includes the following variables: use of mifepristone, BMI, number of previous uterine aspirations and the presence of minor clinical symptoms (slight vaginal bleeding or some abdominal cramps) at treatment start. The model shows a moderate capacity to discriminate between success and failure of treatment, with an AUC of 67.6% (95% CI = 64.9-70.3%). The model had a good fit comparing predicted to observed probabilities of success but might underestimate treatment success in women with a predicted probability of success of ∼70%. LIMITATIONS, REASONS FOR CAUTION: The vast majority (90.4%) of women were Caucasian, potentially leading to less optimal model performance in a non-Cauc