ArticleScientific reports2022
Risk-factor model for postpartum hemorrhage after cesarean delivery: a retrospective study based on 3498 patients.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The efficacy of tranexamic acid for the prevention of postpartum hemorrhage among women at high risk of postpartum hemorrhage undergoing cesarean section: a meta-analysis.BMC pregnancy and childbirth · 2026Pooled it
- Prenatal predictors of severe maternal outcomes in placenta accreta spectrum: A multicenter cohort study in China.European journal of obstetrics & gynecology and reproductive biology: X · 2026Article
- Prophylactic Use of Tranexamic Acid to Prevent Postpartum Hemorrhage in High-Risk Cesarean Deliveries: A Systematic Review and Meta-Analysis.Journal of clinical medicine · 2026Review
- Contemporary Approach to Postpartum Hemorrhage: Early Diagnosis and Evidence-Based Therapeutic Management.Cureus · 2026Review
- Postpartum Hemorrhage: A Comprehensive Review of Medical and Surgical Treatment.Maternal-fetal medicine (Wolters Kluwer Health, Inc.) · 2026Review
- Association of uterine fibroids with adverse pregnancy outcomes: a retrospective cohort study.BMC pregnancy and childbirth · 2026Article
- Feasibility and effectiveness of the novel HemoClear microfiltration cell-salvage system in cesarean sections in South Africa.International journal of gynaecology and obstetrics: the official organ of the International Federation of Gynaecology and Obstetrics · 2026Article
- The differential role of anesthetic technique by etiology of postpartum hemorrhage: a dual-cohort analysis of emergency cesarean delivery and placenta accreta spectrum.Frontiers in medicine · 2026Article
- Incidence and Risk Factors of Postpartum Hemorrhage in Cesarean Versus Vaginal Deliveries: A Retrospective Study.Cureus · 2025Article
- Machine Learning Models to Predict Risk of Maternal Morbidity and Mortality From Electronic Medical Record Data: Scoping Review.Journal of medical Internet research · 2025Article
- PV integrated multi-leg powered constant quasi-dynamic charging system for low-speed vehicles.Scientific reports · 2024Article
- Predicting intraoperative blood loss during cesarean sections based on multi-modal information: a two-center study.Abdominal radiology (New York) · 2024Article
- Nomogram to predict postpartum hemorrhage in cesarean delivery for twin pregnancies: a retrospective cohort study in China.Frontiers in medicine · 2023Article
- Application of Predictive Analytics in Pregnancy, Birth, and Postpartum Nursing Care.MCN. The American journal of maternal child nursingArticle
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study aimed to investigate the risk factors of patients with postpartum hemorrhage (PPH) after cesarean delivery (CD) and to develop a risk-factor model for PPH after CD. Patients were selected from seven affiliated medical institutions of Chongqing Medical University from January 1st, 2015, to January 1st, 2020. Continuous and categorical variables were obtained from the hospital's electronic medical record systems. Independent risk factors were identified by univariate analysis, least absolute shrinkage and selection operator and logistic regression. Furthermore, logistic, extreme gradient boosting, random forest, classification and regression trees, as well as an artificial neural network, were used to build the risk-factor model. A total of 701 PPH cases after CD and 2797 cases of CD without PPH met the inclusion criteria. Univariate analysis screened 28 differential indices. Multi-variable analysis screened 10 risk factors, including placenta previa, gestational age, prothrombin time, thrombin time, fibrinogen, anemia before delivery, placenta accreta, uterine atony, placental abruption and pregnancy with uterine fibroids. Areas under the curve by random forest for the training and test sets were 0.957 and 0.893, respectively. The F1 scores in the random forest training and test sets were 0.708. In conclusion, the risk factors for PPH after CD were identified, and a relatively stable risk-factor model was built.
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