Stratifying Risk for Postpartum Depression at Time of Hospital Discharge

Abstract

Objective: Postpartum depression (PPD) represents a major contributor to postpartum morbidity and mortality. Beyond efforts at routine screening, risk stratification models could enable more targeted interventions in settings with limited resources. Thus, we aimed to develop and estimate the performance of a generalizable risk stratification model for PPD in patients without a history of depression using information collected as part of routine clinical care.

Methods: We performed a retrospective cohort study of all individuals who delivered between 2017 and 2022 in one of two large academic medical centers and six community hospitals. An elastic net model was constructed and externally validated to predict PPD using sociodemographic factors, medical history, and prenatal depression screening information, all of which was known before discharge from the delivery hospitalization.

Results: The cohort included 29,168 individuals; 2,703 (9.3%) met at least one criterion for postpartum depression in the 6 months following delivery. In the external validation data, the model had good discrimination and remained well-calibrated: area under the receiver operating characteristic curve 0.721 (95% CI: 0.707-0.734), Brier calibration score 0.088 (95% CI: 0.084 - 0.092). At a specificity of 90%, the positive predictive value was 28.0% (95% CI: 26.0-30.1%), and the negative predictive value was 92.2% (95% CI: 91.8-92.7%).

Conclusions: These findings demonstrate that a simple machine-learning model can be used to stratify the risk for PPD before delivery hospitalization discharge. This tool could help identify patients within a practice at the highest risk and facilitate individualized postpartum care planning regarding the prevention of, screening for, and management of PPD at the start of the postpartum period and potentially the onset of symptoms.

Figure 1.

CONSORT Diagram PPD, postpartum depression.

Figure 2.

Postpartum depression (PPD) prediction model discrimination receiver operating characteristic curve in a random test (A) and independent external validation (B) cohort. The red line corresponds to the full elastic net model incorporating sociodemographic characteristics and prenatal diagnosis, medications, and Edinburgh postnatal depression score (EPDS). The blue line shows an elastic net model that includes all the same terms except prenatal EPDS.

Figure 3.

Primary postpartum depression (PPD) prediction model calibration in a random test (A) and independent external validation (B) cohort. The line indicates the PPD rate at intervals of the model prediction score. The shaded area represents a 95% confidence interval.

Figure 4.

Postpartum depression (PPD) prediction model (excluding prenatal Edinburgh Postnatal Depression Scale score) calibration in a random test (A) and independent external validation (B) cohort. The line indicates the PPD rate at intervals of the model prediction score. The shaded area represents a 95% confidence interval.