Gestational age as a predictor for subsequent preterm birth in New South Wales, Australia

Prediction Models for Adverse Pregnancy Outcomes in India: Methodological Considerations for an Emerging Topic

Stillbirth is over-represented in lower and lower-middle-income countries and understandably this has motivated greater research investment in the development of prediction models. Prediction is particularly challenging for pregnancy outcomes because only part of the population is represented in observational research. Notably, unrecognised pregnancies and miscarriages are typically excluded from the development of prediction models and the consequences of such selection are not well understood. Other methodological challenges in developing stillbirth prediction models are within the control of the researcher. Identifying whether the intended model is for aetiological explanation versus prediction, attainment of a sufficiently large representative sample, and internal and external validation are among such methodological considerations. These considerations are discussed in relation to a recently published study on prediction of stillbirth after 28 weeks of pregnancy for women with hypertensive disorders of pregnancy in India. The predictive ability of this model amounts to the flip of a coin. Future screening based on such a model may be expensive, increase psychological distress among patients and introduce additional iatrogenic perinatal morbidities from over-treatment. Future research should address the methodological considerations described in this article.

Identifying the Early Signs of Preterm Birth from U.S. Birth Records Using Machine Learning Techniques

Preterm birth (PTB) is the leading cause of infant mortality in the U.S. and globally. The goal of this study is to increase understanding of PTB risk factors that are present early in pregnancy by leveraging statistical and machine learning (ML) techniques on big data. The 2016 U.S. birth records were obtained and combined with two other area-level datasets, the Area Health Resources File and the County Health Ranking. Then, we applied logistic regression with elastic net regularization, random forest, and gradient boosting machines to study a cohort of 3.6 million singleton deliveries to identify generalizable PTB risk factors. The response variable is preterm birth, which includes spontaneous and indicated PTB, and we performed a binary classification. Our results show that the most important predictors of preterm birth are gestational and chronic hypertension, interval since last live birth, and history of a previous preterm birth, which explains 10.92, 5.98, and 5.63% of the predictive power, respectively. Parents' education is one of the influential variables in predicting PTB, explaining 7.89% of the predictive power. The relative importance of race declines when parents are more educated or have received adequate prenatal care. The gradient boosting machines outperformed with an AUC of 0.75 (sensitivity: 0.64, specificity: 0.73) for the validation dataset. In this study, we compare our results with seminal and most related studies to demonstrate the superiority of our results. The application of ML techniques improved the performance measures in the prediction of preterm birth. The results emphasize the importance of socioeconomic factors such as parental education as one of the most important indicators of preterm birth. More research is needed on these mechanisms through which socioeconomic factors affect biological responses.