Predicting Patients Requiring Treatment for Depression in the Postpartum Period Using Common Electronic Medical Record Data Available Antepartum

Abstract

Depression requiring treatment in the postpartum period significantly impacts maternal and neonatal health. Although preventive management of depression in pregnancy has been shown to decrease the negative impacts, current methods for identifying at-risk patients are insufficient. Given the complexity of the diagnosis and interplay of clinical/demographic factors, we tested whether machine learning techniques can accurately identify at-risk patients in the postpartum period. This is a retrospective cohort study of the NIH Nulliparous Pregnancy Outcomes Study: Monitoring Mothers-to-Be, which enrolled 10,038 nulliparous people. The primary outcome was depression in the postpartum period. We constructed and optimized 4 machine learning models using distributed random forest modeling and 1 logistic regression model on the basis of the NIH Nulliparous Pregnancy Outcomes Study: Monitoring Mothers-to-Be dataset. Model 1 utilized only readily obtainable sociodemographic data. Model 2 added maternal prepregnancy mental health data. Model 3 utilized recursive feature elimination to construct a parsimonious model. Model 4 further titrated the input data to simplify prepregnancy mental health variables. The logistic regression model used the same input data as Model 3 as a proof of concept. Of 8,454 births, 338 (4%) were complicated by depression in the postpartum period. Model 3 was the highest performing, showing the area under the receiver operating characteristics curve of 0.91 (±0.02). Models 1-3 identified the 9 variables most predictive of depression hierarchically, ranging from depression history (highest), history of mental health condition, recent psychiatric medication use, BMI, income, age, anxiety history, education, and preparedness for pregnancy (lowest). In Model 4, the area under the receiver operating characteristics curve remained at 0.79 (±0.05). Postpartum depression can be predicted with high accuracy for individual patients using antepartum information commonly found in electronic medical records. In addition, baseline mental health status and sociodemographic factors have a larger role in the postpartum period than previously understood.