New Tool Aims to Detect Postpartum Depression in Delivery Room

A novel machine-learning model is emerging to identify postpartum depression risks much earlier. This advancement could significantly improve care for new mothers, potentially preventing the condition from worsening and minimizing its impact on their lives.

Early Detection is Key

Typically, new mothers have a follow-up appointment six to eight weeks after giving birth. According to Dr. Mark Clapp, a maternal-fetal medicine provider at Massachusetts General Hospital, this delay can be a prolonged period for those experiencing postpartum depression symptoms.

Researchers, including Dr. Clapp, have pioneered new research to detect the risk of postpartum depression rapidly. The approach involves a straightforward machine-learning model used before a new mother is discharged from the hospital.

“We want to be able to identify and treat depression as early as possible,”

—Dr. Andrea Edlow, Maternal-Fetal Medicine Specialist

Understanding Postpartum Depression

Postpartum depression is a mood disorder that can manifest in the weeks and months after childbirth. It goes beyond the “baby blues,” and experts say it can involve various symptoms. These symptoms might include depressed moods, severe mood swings, and difficulty bonding with the baby.

Other symptoms are feelings of hopelessness, reduced ability to think clearly, loss of interest in activities, and even thoughts of death or suicide. Perinatal and postpartum mood and anxiety disorders have historically been stigmatized.

Addressing the Problem

The tragic case of Lindsay Clancy, a Massachusetts mother who is charged with killing her three children, has brought this topic to the forefront. Her attorney stated she suffered from severe mental illness, was overmedicated, and struggled with postpartum depression and potentially postpartum psychosis.

Dr. Edlow, a researcher on the study, emphasizes that the goal is to provide early intervention. Early treatment can prevent the condition from worsening and lessen its impact on individuals’ lives.

The implementation of this research aligns with state objectives. Last summer, Massachusetts Governor Maura Healey signed a maternal health bill. This bill established a task force on maternal health access and birthing patient safety. Additionally, state law requires the Department of Public Health to gather data on screening for postpartum depression from healthcare providers.

How the New Tool Works

The study, published in the American Journal of Psychiatry, shows how the machine-learning detection tool can help identify patients at the highest risk for postpartum depression. By doing so, the aim is to facilitate prevention, facilitate ongoing connection with health care providers, and ensure appropriate treatment.

Dr. Clapp notes that the hospital after delivery is a “unique capture point” for early screening. In the testing phase, researchers used data from electronic health records to establish which patients could be at risk. The model was effective in ruling out postpartum depression in 90% of cases.

According to a 2024 study, approximately 1 in 7 women experience postpartum depression, highlighting the widespread need for effective screening and intervention strategies (CDC).

Next Steps

The researchers are now determining how to implement the machine-learning tool in practical clinical settings. Dr. Clapp emphasizes the urgency, noting that “many preventable deaths occur by suicide in the postpartum period.”