ArticleThe American journal of psychiatry2025
Stratifying Risk for Postpartum Depression at Time of Hospital Discharge.
Article in The American journal of psychiatry, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07460063 (Routine vs. Early Postpartum Depression Screening), which is not on this map. Cited by 8 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Routine vs. Early Postpartum Depression Screening: A Pragmatic Clinical Trial
Who cites it
8 citing papers in PubMed.
- Community Health Workers in Perinatal Mental Health: Feasibility and Challenges of Implementing a Task-Sharing Model.Community mental health journal · 2026Article
- Factors associated with postpartum depression symptoms following antepartum hospitalization.Pregnancy (Hoboken, N.J.) · 2026Article
- GABAergic Neuroactive Steroids and Network States: Relevance to Peripartum Depression.Biological psychiatry · 2026Review
- Building a program to improve postpartum depression screening, triage, and referrals at a large birthing hospital.Journal of perinatology : official journal of the California Perinatal Association · 2026Article
- ClinPreAI: An Agentic AI System for Early Postpartum Depression Risk Prediction from Multimodal EHR Data.medRxiv : the preprint server for health sciences · 2026Article
- Who's at risk for emergent depression years later? Predictive modeling in a nine-year longitudinal cohort.BMC psychiatry · 2026Article
- From Prediction to Action: Moving Beyond Machine Learning to Implementing Evidence-Based Perinatal Mental Health Care.The American journal of psychiatry · 2025Article
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7 authors.
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Abstract
objectivePostpartum depression (PPD) is 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. The authors sought 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.
methodsThe authors conducted 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, defined as having a mood disorder, an antidepressant prescription, or a positive screen on the postpartum Edinburgh Postnatal Depression Scale. Predictors used included sociodemographic factors, medical history, and prenatal depression screening information, all of which were known before discharge from the delivery hospitalization.
resultsThe cohort included 29,168 individuals; 2,696 (9.2%) 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: the area under the receiver operating characteristic curve was 0.721 (95% CI=0.709, 0.736), and the Brier calibration score was 0.087 (95% CI=0.083, 0.091). At a specificity of 90%, the positive predictive value was 28.8% (95% CI=26.7, 30.8), and the negative predictive value was 92.2% (95% CI=91.8, 92.7).
conclusionsThese 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 for the prevention of, screening for, and management of PPD at the start of the postpartum period and potentially the onset of symptoms.
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