A recent study suggests that a machine-learning model can effectively predict the risk of postpartum depression in new mothers before they leave the hospital. Conducted by researchers at Massachusetts General Hospital and Harvard Medical School, the study analyzed electronic health record (EHR) data from women who gave birth between 2017 and 2022 at two academic medical centers and six community hospitals.
The model demonstrated good predictive performance, achieving an area under the receiver operating characteristic curve of 0.721 in external validation data. This indicates a reliable ability to distinguish between those likely to develop postpartum depression and those who would not. At a specificity rate of 90%, the model achieved a positive predictive value of 28.8% and a negative predictive value of 92.2%. The model’s calibration score was 0.087, indicating a strong fit between predicted and observed outcomes.
Mark Clapp, MD, MPH, who presented the findings at the American Psychiatric Association annual meeting, emphasized the model’s potential utility in clinical settings. By identifying high-risk individuals, healthcare providers can implement targeted interventions and screenings, improving postpartum care right from the start. The study estimates that about 9% of women—approximately 2,700 out of the cohort—were diagnosed with postpartum depression within six months of delivery.
Clapp noted, “We are close to moving this model into clinical practice. While developing predictive models is straightforward, integrating them into bedside practice is more challenging.” The researchers are currently working on this transition.
Postpartum depression affects around 15% of new mothers and is linked to increased risks of suicide, self-harm, and is responsible for over 10% of pregnancy-related deaths. The researchers outlined plans to translate their findings into practical applications within clinical settings, aiming to reduce the incidence and severity of postpartum depression.
Dr. Misty Richards from UCLA commented on the study’s implications, stating, “In our preventive clinic, we aim to identify postpartum depression early, but we often miss cases. If we could utilize predictive tools like this, especially for women without a history of depression, it would be invaluable.”
The model utilized a variety of predictors, including sociodemographic data, medical history, and prenatal depression screening results. Excluded from the study were women with prior psychiatric conditions, focusing solely on those who had received prenatal care at the participating facilities. The cohort included a median delivery age of 33 years, with 70% identifying as white, 13% as Asian, 7% as Black, and 11% as Hispanic.
The average prenatal EPDS score was 3, and the median hospital stay post-delivery was approximately three days. The study received funding from the National Institutes of Health and other organizations.
The next steps will involve studying how to leverage this machine-learning model effectively in clinical practice, ensuring it aids both patients and clinicians in managing postpartum depression risks during the critical postpartum period.
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