Predicting outcomes of expectant and medical management in early pregnancy miscarriage using machine learning to...

To determine whether readily available patient, ultrasound and treatment outcome data can be used to develop, validate and externally test two machine learning (ML) models for predicting the success of expectant and medical management of miscarriage respectively.

A retrospective, multi-site study of patients opting for expectant or medical management of miscarriage was undertaken. A total of 1075 patients across two hospital early pregnancy units were eligible for inclusion. Data pre-processing derived 14 features for predictive modelling. A combination of eight linear, Bayesian, neural-net and tree-based machine learning algorithms were applied to ten different feature sets. The area under the receiver operating characteristic curve (AUC) scores were the metrics used to demonstrate the performance of the best performing model and feature selection combination for the training, validation and external data set for expectant and medical management separately.

Parameters were in the majority well matched across training, validation and external test sets. The respective optimum training, validation and external test set AUC scores were as follows in the expectant management cohort: 0.72 (95% CI 0.67,0.77), 0.63 (95% CI 0.53,0.73) and 0.70 (95% CI 0.60,0.79) (Logistic Regression in combination with Least Absolute Shrinkage and Selection Operator (LASSO)). The AUC scores in the medical management cohort were 0.64 (95% CI 0.56,0.72), 0.62 (95% CI 0.45,0.77) and 0.71 (95% CI 0.58,0.83) (Logistic Regression in combination with Recursive Feature Elimination (RFE)).

Performance of our expectant and medical miscarriage management ML models demonstrate consistency across validation and external test sets. These ML methods, validated and externally tested, have the potential to offer personalised prediction outcome of expectant and medical management of miscarriage.