Predicting preterm birth using electronic medical records from multiple prenatal visits

Abstract

This study aimed to predict preterm birth in nulliparous women using machine learning and easily accessible variables from prenatal visits. Elastic net regularized logistic regression models were developed and evaluated using 5-fold cross-validation on data from 8,830 women in the Nulliparous Pregnancy Outcomes Study: New Mothers-to-Be (nuMoM2b) dataset at three prenatal visits: $6^0$ - ${13}^6$ , ${16}^0$ - ${21}^6$ , and ${22}^0$ - ${29}^6$ weeks of gestational age (GA). The models' performance, assessed using Area Under the Curve (AUC), sensitivity, specificity, and accuracy, consistently improved with the incorporation of data from later prenatal visits. AUC scores increased from 0.6161 in the first visit to 0.7087 in the third visit, while sensitivity and specificity also showed notable improvements. The addition of ultrasound measurements, such as cervical length and Pulsatility Index, substantially enhanced the models' predictive ability. Notably, the model achieved a sensitivity of 0.8254 and 0.9295 for predicting very preterm and extreme preterm births, respectively, at the third prenatal visit. These findings highlight the importance of ultrasound measurements and suggest that incorporating machine learning-based risk assessment and routine late-pregnancy ultrasounds into prenatal care could improve maternal and neonatal outcomes by enabling timely interventions for high-risk women.

Keywords: Logistics regression; Machine learning; Prediction; Preterm birth.