Machine Learning Model Helps Identify Patients at Risk of Postpartum Depression

Postpartum depression (PPD) affects up to 15 percent of individuals after childbirth. Early identification of patients at risk of PPD could improve proactive mental health support. Mass General Brigham researchers developed a machine learning model that can evaluate patients’ PPD risk using readily accessible clinical and demographic factors. Findings demonstrating the model’s promising predictive capabilities are published in the American Journal of Psychiatry.

“Postpartum depression is one of the biggest challenges that some parents may experience in the period after childbirth – a time when many cope with sleep deprivation, new stresses, and significant life changes,” said lead author Mark Clapp, MD, MPH, of the Department of Obstetrics and Gynecology at Massachusetts General Hospital, a founding member of the Mass General Brigham healthcare system. “Persistent feelings of sadness, depression, or anxiety can be more common than many people realize. Our team, under the leadership of Dr. Roy Perlis, undertook this work to better understand which patients may be at higher risk of PPD to help us facilitate strategies and solutions to either prevent PPD or reduce its severity.”

Typically, PPD symptoms are evaluated at postpartum visits, which occur 6-to-8 weeks post-delivery. As a result, many parents may struggle for several weeks before receiving mental health support. To help deliver earlier PPD care, the researchers designed a model that requires only information readily available in the electronic health record (EHR) at the time of delivery, including data on demographics, medical conditions, and visit history. This model weighs and integrates these complex variables to more accurately evaluate PPD risk. 

To develop and validate the model, the authors used information from 29,168 pregnant patients who delivered at two academic medical centers and six community-based hospitals in the Mass General Brigham system between 2017 and 2022. In this cohort, 9 percent of patients met the study’s criteria for PPD in the six months following delivery.

The researchers used health record data from approximately one-half of the patients to train the model to identify PPD. They then tested the model by asking it to predict PPD in the other half of the patients. The researchers found that the model was effective in ruling out PPD in 90 percent of cases. The model showed promise in predicting PPD: nearly 30 percent of those predicted to be high risk developed PPD within the six months after delivery. The model was about two to three times better at predicting PPD than estimating based on the general population risk.

In further analyses, the researchers showed that the model performed similarly regardless of race, ethnicity, and age at delivery. The study included only those without a previous psychiatric diagnosis to determine if the model can predict PPD even among low-risk patients and to better understand the risk factors that influence PPD outside of prior psychiatric diagnoses. Notably, scores on the Edinburgh Postnatal Depression Scale acquired in the prenatal period improved the predictive capabilities of the model, highlighting that this existing tool may be useful both pre- and post-delivery.

The researchers are prospectively testing the model’s accuracy, an essential step toward real-world use, and working with patients, clinicians and stakeholders to determine how information derived from the model might best be incorporated into clinical practice.  

“This is exciting progress toward developing a predictive tool that, paired with clinicians’ expertise, could help improve maternal mental health,” Clapp said. “With further validation, and in collaboration with clinicians and patients, we hope to achieve earlier identification and ultimately improved mental health outcomes for postpartum patients.”

Authorship: In addition to Clapp, Mass General Brigham authors include Victor M. Castro, Pilar Verhaak, Thomas H. McCoy,  Lydia L. Shook,  Andrea G. Edlow, and Roy H. Perlis.

Disclosures: Clapp serves on the scientific advisory board of and holds equity in Delfina Care; receives research support from grants to his institution from the Agency for Health Care Quality and Research; and receives a stipend for editorial services from the American College of Obstetrics and Gynecology. McCoy has received research support from grants to his institution from InterSystems, Koa Health, NIH, and Philips Medical, and receives payments from SpringerNature and MGH Psychiatry Academy. Edlow has served as a consultant for Merck and Mirvie and has received research funding from Merck. Perlis has served as a consultant for Alkermes, Belle Artificial Intelligence, Burrage Capital, Circular Genomics, Genomind, and Takeda; holds equity in Belle Artificial Intelligence, Circular Genomics, and Psy Therapeutics; and serves as Editor-in-Chief of JAMA+ AI and AI Editor at JAMA Network–Open.

Funding: This study was funded in part by the National Institute of Mental Health (NIMH) (RF1MH132336, R01 MH116270, U54 MH118919), the National Institute of Child Health and Human Development (R01 HD100022-02S2), and the Simons Foundation (SFARI grant 870754).

Paper cited: Clapp MA et al. “Stratifying Risk for Postpartum Depression at Time of Hospital Discharge” American Journal of Psychiatry DOI: 10.1176/appi.ajp.20240381