A machine learning approach to identifying pregnant women's risk for persistent post-traumatic stress following childbirth

Intro: Recent literature identifies childbirth as a potentially traumatic event, following which mothers may develop symptoms of Post-Traumatic-Stress-Following-Childbirth (PTS-FC). Especially when persistent, PTS-FC may interfere with mothers’ caregiving and associated infant development, underscoring the need for accurate predictive screening of risk. Drawing on recent developments in advanced statistical modeling, the aim of the current study was to identify a set of prenatal indicators and prediction rules that may accurately identify pregnant women's risk for developing symptoms of PTS-FC which persist throughout the early postpartum period. Methods: 182 women from the general population completed a comprehensive set of approximately 200 potentially predictive questions during pregnancy, and subsequently reported on their acute stress and PTS-FC at three days, one month, and three months postpartum (self-report and clinician-administered interview). Based on the postpartum acute stress and PTS-FC data, women were classified into profiles of “Stable-High-PTS-FC” and “Stable-Low-PTS-FC” by means of Latent-Class Analyses. Prenatal data were modeled to identify women at risk for “Stable-High PTS-FC”. Results: Employing machine-learning decision-tree analyses, a total of 36 questions and 7 prediction-rules were selected. Based on a cost-rate of 15 versus 100 for false-negative “Stable-Low-PTS-FC” versus false-negative “Stable-High-PTS-FC”, the final model showed 80.6% accuracy for “Stable-High-PTS-FC” prediction. Discussion: This study identifies a short set of questions and prediction rules that may be included in future large-scale validation studies aimed at developing and validating a brief PTS-FC screening instrument that could be implemented in general population prenatal healthcare practice. Accurate screening would allow for selective administering of preventive interventions towards women at risk.