Stratifying Risk for Postpartum Depression at Time of Hospital Discharge
Open accessPublished version of a preprint
Abstract
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.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The prior machine learning model had an AUC of 0.75.
“Their model demonstrated improved discriminatory ability as compared to our multivariable regression model, with an AUC of 0.75 (compared to our AUC of 0.67).”
From Factors associated with postpartum depression symptoms following antepartum hospitalization (2026) · cites this paper as [53]
All prior studies predicting PPD have found that varying aspects of mental health history are the strongest predictors of PPD.
“Consistent with our findings, all prior studies predicting PPD have found that varying aspects of mental health history (i.e., anxiety disorders and prenatal EPDS score [53]; depression and anxiety history, recent psychiatric medication use [54]; psychiatric history [55]; anxiety, depression, and antidepressants during pregnancy [56]) are the strongest predictors of PPD.”
From Factors associated with postpartum depression symptoms following antepartum hospitalization (2026) · cites this paper as [53]
A recent publication presented results from a machine learning model leveraging variables routinely documented during prenatal care and delivery hospitalization to predict PPD among a large cohort (n = 29,168) without history of depressive symptoms.
“Specific to perinatal mental health, a recent publication [53] presented results from a machine learning model leveraging variables routinely documented during prenatal care and delivery hospitalization to predict PPD among a large cohort (n = 29,168) without history of depressive symptoms.”
From Factors associated with postpartum depression symptoms following antepartum hospitalization (2026) · cites this paper as [53]
Cites
Collins, G. S., Reitsma, J. B., Altman, D. G., & Moons, K. G. M. (2015) · BMJ · doi:10.1136/bmj.g7594 · Cited by 4,388 · see citing works
Consequences of maternal postpartum depression: A systematic review of maternal and infant outcomes
Slomian, J., Honvo, G., Emonts, P., Reginster, J.-Y., & Bruyère, O. (2019) · Women's Health · doi:10.1177/1745506519844044 · Cited by 1,496 · see citing works
Shorey, S., Chee, C. Y. I., Ng, E. D., Chan, Y. H., Tam, W. W. S., & Chong, Y. S. (2018) · Journal of Psychiatric Research · doi:10.1016/j.jpsychires.2018.08.001 · Cited by 1,055 · see citing works
Levis, B., Negeri, Z., Sun, Y., Benedetti, A., & Thombs, B. D. (2020) · BMJ · doi:10.1136/bmj.m4022 · Cited by 922 · see citing works
ACOG Committee Opinion No. 757: Screening for Perinatal Depression
(2018) · Obstetrics & Gynecology · doi:10.1097/aog.0000000000002927 · Cited by 771 · see citing works
Review of validation studies of the Edinburgh Postnatal Depression Scale
Eberhard‐Gran, M., Eskild, A., Tambs, K., Opjordsmoen, S., & Samuelsen, S. O. (2001) · Acta Psychiatrica Scandinavica · doi:10.1034/j.1600-0447.2001.00187.x · Cited by 487 · see citing works
ACOG Committee on Clinical Practice Guidelines–Obstetrics (2023) · Obstetrics & Gynecology · doi:10.1097/aog.0000000000005200 · Cited by 294 · see citing works
Jacobs, M., Pradier, M. F., McCoy, T. H., Perlis, R. H., Doshi-Velez, F., & Gajos, K. Z. (2021) · Translational Psychiatry · doi:10.1038/s41398-021-01224-x · Cited by 247 · see citing works
Self-citationTrends and state variations in out‐of‐hospital births in the United States, 2004‐2017
MacDorman, M. F., & Declercq, E. R. (2018) · Birth · doi:10.1111/birt.12411 · Cited by 222 · see citing works
Trost, S. L., Beauregard, J. L., Smoots, A. N., Ko, J. Y., Haight, S. C., Simas, T. A. M., Byatt, N., Madni, S. A., & Goodman, D. A. (2021) · Health Affairs · doi:10.1377/hlthaff.2021.00615 · Cited by 219 · see citing works
Maternal self-harm deaths: an unrecognized and preventable outcome
Mangla, K., Hoffman, M. C., Trumpff, C., O’Grady, S., & Monk, C. (2019) · American Journal of Obstetrics and Gynecology · doi:10.1016/j.ajog.2019.02.056 · Cited by 215 · see citing works
Dennis, C. (2002) · Journal of Affective Disorders · doi:10.1016/s0165-0327(02)00299-9 · Cited by 212 · see citing works
Review of validation studies of the Edinburgh Postnatal Depression Scale
Eberhard‐Gran, M., Eskild, A., Tambs, K., et al. (2001) · Acta Psychiatrica Scandinavica · doi:10.1111/j.1600-0447.2001.00187.x · Cited by 194 · see citing works
All-cause mortality in women with severe postpartum psychiatric disorders
Johannsen, B. M. W., Larsen, J. T., Laursen, T. M., Bergink, V., Meltzer-Brody, S., & Munk-Olsen, T. (2016) · American Journal of Psychiatry · doi:10.1176/appi.ajp.2015.14121510 · Cited by 110 · see citing works
Using Electronic Health Records and Machine Learning to Predict Postpartum Depression
Shuojia, W., Jyotishman, P., & Yiye, Z. (2019) · Studies in Health Technology and Informatics · doi:10.3233/shti190351 · Cited by 99 · see citing works
Lilja, G., Edhborg, M., & Nissen, E. (2012) · Scandinavian Journal of Caring Sciences · doi:10.1111/j.1471-6712.2011.00925.x · Cited by 80 · see citing works
Perinatal Depression and Risk of Suicidal Behavior
Yu, H., Shen, Q., Bränn, E., Yang, Y., Öberg, A. S., Valdimarsdóttir, U. A., & Lu, D. (2024) · JAMA Network Open · doi:10.1001/jamanetworkopen.2023.50897 · Cited by 76 · see citing works
Perinatal depression and risk of mortality: Nationwide, register-based study in Sweden
Hagatulah, N., Bränn, E., Oberg, A. S., Valdimarsdóttir, U. A., Shen, Q., & Lu, D. (2024) · BMJ · doi:10.1136/bmj-2023-075462 · Cited by 72 · see citing works
Differentiation and Clinical Implications of Postpartum Depression and Postpartum Psychosis
Doucet, S., Dennis, C.-L., Letourneau, N., & Blackmore, E. R. (2009) · Journal of Obstetric, Gynecologic & Neonatal Nursing · doi:10.1111/j.1552-6909.2009.01019.x · Cited by 58 · see citing works
Meijer, J. L., Beijers, C., van Pampus, M. G., Verbeek, T., Stolk, R. P., Milgrom, J., Bockting, C., & Burger, H. (2014) · BJOG An International Journal of Obstetrics & Gynaecology · doi:10.1111/1471-0528.12759 · Cited by 52 · see citing works
Luciano, M., Vincenzo, M. D., Brandi, C., Tretola, L., Toricco, R., Perris, F., Volpicelli, A., Torella, M., Verde, M. L., Fiorillo, A., et al. (2022) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2022.1082762 · Cited by 51 · see citing works
Amiri, S., & Behnezhad, S. (2021) · International Journal of Mental Health · doi:10.1080/00207411.2021.1959814 · Cited by 44 · see citing works
Postpartum depression: a developed and validated model predicting individual risk in new mothers
Munk-Olsen, T., Liu, X., Madsen, K. B., Kjeldsen, M.-M. Z., Petersen, L. V., Bergink, V., Skalkidou, A., Vigod, S. N., Frokjaer, V. G., Pedersen, C. B., et al. (2022) · Translational Psychiatry · doi:10.1038/s41398-022-02190-8 · Cited by 36 · see citing works
Pregnancy-Associated Homicide and Suicide
Modest, A. M., Prater, L., & Joseph, N. T. (2022) · Obstetrics and Gynecology · doi:10.1097/aog.0000000000004932 · Cited by 35 · see citing works
Tanuma‐Takahashi, A., Tanemoto, T., Nagata, C., Yokomizo, R., Konishi, A., Takehara, K., Ishikawa, T., Yanaihara, N., Samura, O., & Okamoto, A. (2022) · BMC Pregnancy and Childbirth · doi:10.1186/s12884-022-04740-w · Cited by 27 · see citing works
Wakefield, C., & Frasch, M. G. (2023) · AJPM Focus · doi:10.1016/j.focus.2023.100100 · Cited by 21 · see citing works
Visualizing machine learning-based predictions of postpartum depression risk for lay audiences
Desai, P., Harkins, S., Rahman, S., Kumar, S., Hermann, A. D., Joly, R., Zhang, Y., Pathak, J., Kim, J., D’Angelo, D., et al. (2023) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocad198 · Cited by 10 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
GABAergic Neuroactive Steroids and Network States: Relevance to Peripartum Depression
Maguire, J. L., Antonoudiou, P., & Deligiannidis, K. M. (2025) · Biological Psychiatry · doi:10.1016/j.biopsych.2025.09.019 · Cited by 4 · see citing works
Qiao, L., Liu, P., Huang, G., Liu, L., Ge, Y., Wang, J., Hou, H., & Wang, Y. (2025) · The EPMA Journal · doi:10.1007/s13167-025-00426-3 · Cited by 2 · see citing works
Palacios, D., Aras, S., Zhong, Y., Zhao, J., Pasupuleti, S., Jeong, H.-H., Miller, E. S., Fletcher, T. L., Goulding, A. N., Chen, H., et al. (2025) · medRxiv · doi:10.1101/2025.11.14.25340265 · Cited by 2 · see citing works
Singh, M., Spada, M. L., Wright, M., Rodriguez, S., Jakubowski, K., Joseph, H. M., Johnson, L., Kullen, L., Siverling, C., Gustafson, K., et al. (2026) · General Hospital Psychiatry · doi:10.1016/j.genhosppsych.2026.08.011 · Cited by 1 · see citing works
AI tool flags people at high risk of post-partum depression
Lenharo, M. (2025) · Nature · doi:10.1038/d41586-025-01537-7 · Cited by 1 · see citing works
Lin, H., Hu, C.-L. J., Hu, S., Ying, Y., Shang, L., Qiu, X., Luo, Q., & Xu, H. (2025) · Research Square · doi:10.21203/rs.3.rs-7079798/v1 · Cited by 1 · see citing works
Factors associated with postpartum depression symptoms following antepartum hospitalization
Goulding, A. N., Palacios, D., Aras, S., Chen, H., Pasupuleti, S., Toscano, M., Cirino, N., Christie, I. C., Liu, Z., Miller, E. S., et al. (2026) · Pregnancy · doi:10.1002/pmf2.70319
Sarwal, V., Pimplaskar, A., Richards, M., Sobowale, K., Chiang, J. N., & Loohuis, L. M. O. (2025) · medRxiv · doi:10.1101/2025.06.19.25329946
Postpartum Depression in the Aftermath of the COVID-19 Pandemic: Restoration or Wreckage?
Alper, E., & Arslan, T. (2026) · Women s Health Reports · doi:10.1177/26884844251415478
Zainal, N. H., Peters, A. T., Jacobson, N. C., & Hsu, K. J. (2026) · BMC Psychiatry · doi:10.1186/s12888-026-07902-8 · Cited by 1 · see citing works
Dempsey, A. G., Singhal, S., Tabman, B., & Kwitowski, M. A. (2026) · Journal of Perinatology · doi:10.1038/s41372-026-02671-4
Ramanathan, S., & Keefe, R. H. (2026) · Community Mental Health Journal · doi:10.1007/s10597-026-01673-y
Scalable Privacy-Preserving Synthetic Postpartum Depression Narratives with Large Language Models
Dokhtfaroughi, S. S., & Winstead, C. (2026) · Intermountain Engineering, Technology and Computing (IETC) · doi:10.1109/ietc69527.2026.11568627
Aranda, S., Bada-Navarro, A., Cormand, B., Cano, M., Cardoner, N., Llurba, E., Mitjans, M., & Koller, D. (2026) · European Neuropsychopharmacology · doi:10.1016/j.euroneuro.2026.112988
Abebe, H. B., Tsegay, N., Berhie, G., Slopen, N. B., Ertel, K. K., & Shaw-Perry, M. (2026) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2026.106761
Research Progress and Mechanisms of Preventive Nursing Interventions for Postpartum Depression
李, 潇. (2026) · Advances in Clinical Medicine · doi:10.12677/acm.2026.16103446
- Authors
- Clapp, M. A., Castro, V. M., Verhaak, P., McCoy, T. H., Shook, L. L., Edlow, A. G., & Perlis, R. H.
- Affiliations
- Harvard University (US)
- Corresponding author
- Clapp, Mark A.
- Venue
- American Journal of Psychiatry
- Published
- 2025-06-01
- Volume
- 182
- Issue
- 6
- Pages
- 551-559
- ISSN
- 0002-953X (ISSN-L)
- 1535-7228
- PMC
- PMC11160818
- OpenAlex
- W4410479309
- Cited by
- 16
- References
- 26
- Citations by year
- 2026: 10 · 2025: 6
- FWCI
- 14.57
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- cc-by-nc-nd
- Locations
- American Journal of Psychiatry (publishedVersion)
- medRxiv (acceptedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed Central (submittedVersion, cc-by-nc-nd)
- Funders
- Simons Foundation Autism Research Initiative
- National Institute of Mental Health (MH118919, R01MH123804, R01MH116270, RF1MH132336, U54MH118919)
- National Institute of Child Health and Human Development
- Eunice Kennedy Shriver National Institute of Child Health and Human Development (R01HD100022)
- Topics
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Maternal Mental Health During Pregnancy and Postpartum
- Health Sciences › Medicine › Obstetrics and Gynecology › Maternal and Perinatal Health Interventions
- Health Sciences › Health Professions › General Health Professions › Maternal and Neonatal Healthcare
- Keywords
- postpartum depression, risk stratification, risk prediction model, hospital discharge, machine learning, elastic net, Edinburgh Postnatal Depression Scale, depression screening
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
