TY - JOUR
AU - M. A. Clapp
AU - V. M. Castro
AU - P. Verhaak
AU - T. H. McCoy
AU - L. L. Shook
AU - A. G. Edlow
AU - R. H. Perlis
A1 - 
AB - OBJECTIVE: Postpartum 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. METHODS: The 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. RESULTS: The 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). 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 for the prevention of, screening for, and management of PPD at the start of the postpartum period and potentially the onset of symptoms.
AD - Department of Obstetrics and Gynecology (Clapp, Shook, Edlow) and Center for Quantitative Health and Department of Psychiatry (Castro, Verhaak, McCoy, Perlis), Massachusetts General Hospital and Harvard Medical School, Boston; Research Information Science and Computing, Mass General Brigham, Somerville, MA (Castro).
AN - 40384019
BT - Am J Psychiatry
C5 - Healthcare Disparities
CP - 6
DA - Jun 1
DO - 10.1176/appi.ajp.20240381
DP - NLM
ET - 20250519
IS - 6
JF - Am J Psychiatry
LA - eng
N2 - OBJECTIVE: Postpartum 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. METHODS: The 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. RESULTS: The 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). 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 for the prevention of, screening for, and management of PPD at the start of the postpartum period and potentially the onset of symptoms.
PY - 2025
SN - 0002-953x
SP - 551
EP - 559+
ST - Stratifying Risk for Postpartum Depression at Time of Hospital Discharge
T1 - Stratifying Risk for Postpartum Depression at Time of Hospital Discharge
T2 - Am J Psychiatry
TI - Stratifying Risk for Postpartum Depression at Time of Hospital Discharge
U1 - Healthcare Disparities
U3 - 10.1176/appi.ajp.20240381
VL - 182
VO - 0002-953x
Y1 - 2025
ER -