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Prediction of pregnancy-related complications in women undergoing assisted reproduction, using machine learning
Chen Wang1, Anna L V Johansson1, Cina Nyberg2
1Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
Objective:
To use machine learning methods to develop prediction models of pregnancy complications in women who conceived with assisted reproductive techniques (ART).
Design:
A nation-wide register-based cohort study with prospectively collected data.
Setting:
Swedish national registers and nationwide quality IVF register.
Patient(S):
all nulliparous women who achieved birth within the first 3 ART treatment cycles between 2008 and 2016 in Sweden.
Intervention(S):
Characteristics before the use of ART, such as demographics and medical history, were considered potential predictors in the development of before treatment prediction models. ART treatment details were further included in after treatment prediction models.
Main Outcome Measure(S):
Potential diagnoses of preeclampsia, placental complications (previa, accreta, and abruption), and postpartum hemorrhage were identified using the International Classification of Diseases recorded in the Swedish Medical Birth and Patient registers, respectively. Multiple prediction model algorithms were performed and compared for each outcome and treatment cycle, including logistic regression, decision tree model, naïve Bayes classification, support vector machine, random forest, and gradient boosting. The performance of each model was assessed with C statistic, and nested cross-validation was used to aid model selection and hyperparameter tuning.
Result(S):
A total of 14,732 women gave birth after the first (N = 7,302), second (N = 4,688), or third (N = 2,742) ART cycle, representing birth rates of 24.1%, 23.8%, and 22.0%. Overall prediction performance did not vary much across the different methods used. In the first cycle, the before treatment prediction performance was at best 66%, 66%, and 60% for preeclampsia, placental complications, and postpartum hemorrhage, respectively. Inclusion of after treatment characteristics conferred slight improvement (approximately 1%-5%), as did prediction in later cycles (approximately 1%-5%). The top influential and consistent predictors included age, region of residence, infertility diagnosis, and type of embryo transfer (fresh or frozen) in the later (2nd and 3rd) cycles. Body mass index was a top predictor of preeclampsia and was also influential for placental complications but not for postpartum hemorrhage.
Conclusion(S):
The combined use of demographics, medical history, and ART treatment information was not enough to confidently predict serious pregnancy complications in women who conceived with ART. Future studies are needed to assess if additional longitudinal follow-up during pregnancy can improve the prediction to allow clinical protocol development.
