دورية أكاديمية
Stratifying Risk for Postpartum Depression at Time of Hospital Discharge.
| العنوان: | Stratifying Risk for Postpartum Depression at Time of Hospital Discharge. |
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| المؤلفون: | Clapp MA; Department of Obstetrics and Gynecology, Massachusetts General Hospital and Harvard Medical School, Boston, MA., Castro VM; Center for Quantitative Health and Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Boston, MA.; Research Information Science and Computing, Mass General Brigham, Somerville, MA., Verhaak P; Center for Quantitative Health and Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Boston, MA., McCoy TH; Center for Quantitative Health and Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Boston, MA., Shook LL; Department of Obstetrics and Gynecology, Massachusetts General Hospital and Harvard Medical School, Boston, MA., Edlow AG; Department of Obstetrics and Gynecology, Massachusetts General Hospital and Harvard Medical School, Boston, MA., Perlis RH; Center for Quantitative Health and Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Boston, MA. |
| المصدر: | MedRxiv : the preprint server for health sciences [medRxiv] 2024 May 27. Date of Electronic Publication: 2024 May 27. |
| نوع المنشور: | Journal Article; Preprint |
| اللغة: | English |
| بيانات الدورية: | Country of Publication: United States NLM ID: 101767986 Publication Model: Electronic Cited Medium: Internet NLM ISO Abbreviation: medRxiv Subsets: PubMed not MEDLINE |
| مستخلص: | Objective: Postpartum depression (PPD) represents 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. Thus, we aimed 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: We performed 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 using sociodemographic factors, medical history, and prenatal depression screening information, all of which was known before discharge from the delivery hospitalization. Results: The cohort included 29,168 individuals; 2,703 (9.3%) 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: area under the receiver operating characteristic curve 0.721 (95% CI: 0.707-0.734), Brier calibration score 0.088 (95% CI: 0.084 - 0.092). At a specificity of 90%, the positive predictive value was 28.0% (95% CI: 26.0-30.1%), 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 regarding the prevention of, screening for, and management of PPD at the start of the postpartum period and potentially the onset of symptoms. |
| معلومات مُعتمدة: | R01 HD100022 United States HD NICHD NIH HHS; R01 MH116270 United States MH NIMH NIH HHS; RF1 MH132336 United States MH NIMH NIH HHS; U54 MH118919 United States MH NIMH NIH HHS |
| تواريخ الأحداث: | Date Created: 20240610 Latest Revision: 20240617 |
| رمز التحديث: | 20240617 |
| مُعرف محوري في PubMed: | PMC11160818 |
| DOI: | 10.1101/2024.05.27.24307973 |
| PMID: | 38854098 |
| قاعدة البيانات: | MEDLINE |
| DOI: | 10.1101/2024.05.27.24307973 |
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