Our models developed in the paper, "Predictive models of miscarriage based on data from a preconception cohort study" are provided in this repository.
@article{YLAND2024,
title = {Predictive models of miscarriage based on data from a preconception cohort study},
journal = {Fertility and Sterility},
year = {2024},
issn = {0015-0282},
doi = {https://doi.org/10.1016/j.fertnstert.2024.04.007},
url = {https://www.sciencedirect.com/science/article/pii/S0015028224002358},
author = {Jennifer J. Yland and Zahra Zad and Tanran R. Wang and Amelia K. Wesselink and Tammy Jiang and Elizabeth E. Hatch and Ioannis Ch. Paschalidis and Lauren A. Wise},
keywords = {miscarriage, spontaneous abortion, machine learning, predictive modeling, pregnancy},
abstract = {Structured Abstract
Objective
To use self-reported preconception data to derive models that predict risk of miscarriage.
Design
Prospective preconception cohort study.
Subjects
Study participants were female, aged 21-45 years, residents of the United States or Canada, and attempting spontaneous pregnancy at enrollment during 2013-2022. Participants were followed for up to 12 months of pregnancy attempts; those who conceived were followed through pregnancy and postpartum. We restricted analyses to participants who conceived during the study period.
Exposure
On baseline and follow-up questionnaires completed every 8 weeks until pregnancy, we collected self-reported data on sociodemographic factors, reproductive history, lifestyle, anthropometrics, diet, medical history, and male partner characteristics. We included 160 potential predictor variables in our models.
Main Outcome Measures
The primary outcome was miscarriage, defined as pregnancy loss before 20 weeks’ gestation. We followed participants from their first positive pregnancy test until miscarriage or a censoring event (induced abortion, ectopic pregnancy, loss to follow-up, or 20 weeks’ gestation), whichever occurred first. We fit both survival and static models, using Cox proportional hazards models, logistic regression, support vector machines, Gradient Boosted Trees, and Random Forest algorithms. We evaluated model performance using the concordance index (survival models) and the weighted-F1 score (static models).
Results
Among 8,720 participants who conceived, 20.4% reported miscarriage. In multivariable models, the strongest predictors of miscarriage were female age, history of miscarriage, and male partner age. The weighted-F1 score ranged from 73-89% for static models and the concordance index ranged from 53-56% for survival models, indicating better discrimination for the static models compared with the survival models (i.e., ability of the model to discriminate between individuals with and without miscarriage). No appreciable differences were observed across strata of miscarriage history or among models restricted to ≥8 weeks’ gestation.
Conclusion
Our findings suggest that miscarriage is not easily predicted based on preconception lifestyle characteristics, and that advancing age and history of miscarriage are the most important predictors of incident miscarriage.}
}
Email: Zahra Zad zad@bu.edu