Early prediction and fairness evaluation of perinatal depression using EHR: A study of 18,000+ Pregnancies

Perinatal depression (PND), defined as a depressive illness occurring during pregnancy or following childbirth, affects between 10-20% of mothers. It is one of the greatest causes of mortality and morbidity in mothers and is associated with poor outcomes in children. Early identification of at-risk mothers has the potential to greatly reduce its impact. While specific risk factors for PND have been identified, most notably a history of prior depression, it is unclear whether mothers’ Electronic Health Records (EHR) can be used early in pregnancy to predict who will go on to develop PND, especially in mothers without a history of prior depression. In this paper, we used clinical EHR data from the UCLA health system to develop predictive models of perinatal depression at a patients’ first prenatal visit (n = 18,081 pregnant mothers, n=4,307 with PND). We used a variety of predictive models, including Ridge Regression, Gradient Boosting Trees, Random Forests, and ExtraTrees. We performed separate analyses including only mothers without a history of prior depression. We further evaluated the robustness and fairness of our algorithms comparing models stratified by self-reported ethnoracial group and social determinants of health (e.g., social vulnerability index (SVI)). All model architectures used perform similarly. The Random Forest model provided robust performance with the highest accuracy and well-balanced sensitivity and specificity (AUROC 0.75, CI [0.66,0.84] in the full cohort). However, performance was reduced among mothers without prior depression (AUROC 0.71, CI [0.6,0.8]). Important risk factors identified by our model include known risk factors, such as prior mental health histories (prior depression, anxiety disorders), socioeconomic factors (social vulnerability), patient vitals (blood pressure), and measures of inflammation in blood (white blood cell counts, platelet counts), as well as novel ones (patient pulse, mean platelet volume (MPV), red blood cell distribution width (RDWSD) and rapid plasma reagin (RPR)). We observed similar model performance when stratifying our cohort by social determinants of health, with overlapping ROC bounds, equalized odds ratios between groups close to 0.8, and largely overlapping predictors of importance across models. This was not the case for ethnoracial groups, where despite observing top predictive features varied by ethnoracial category.