ArticleFrontiers in medicine2022
Development and Validation of Prognostic Nomogram for Postpartum Hemorrhage After Vaginal Delivery: A Retrospective Cohort Study in China.
Article in Frontiers in medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed.
- Prediction of Postpartum Haemorrhage After Labour Induction: An Internally Validated 10-Hour Risk-Stratification Threshold.Diagnostics (Basel, Switzerland) · 2026Article
- Association of uterine fibroids with adverse pregnancy outcomes: a retrospective cohort study.BMC pregnancy and childbirth · 2026Article
- Factors associated with pulmonary complications after hepatectomy and establishment of nomogram: A real-world retrospective study.Indian journal of anaesthesia · 2025Article
- Construction of a predictive model for the risk of postpartum hemorrhage in women with advanced maternal age undergoing natural childbirth.American journal of translational research · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Postpartum hemorrhage (PPH) is a common complication following vaginal delivery and in severe cases can lead to maternal death. A straightforward predictive model is required to enable prenatal evaluations by obstetricians to prevent PPH complications. Methods: Data of patients who delivered vaginally after 37 weeks of gestation were retrospectively collected from the medical database at Shengjing Hospital of China Medical University for the period 2016 to 2020. PPH was defined as blood loss of 500 mL or more within 24 h of delivery, and important independent prognostic factors were determined using univariate and multivariate logistic regression analyses to construct nomograms regarding PPH. Results: A total of 24,833 patients who delivered vaginally were included in this study. The training cohort included 22,302 patients who delivered between 2016 and 2019 and the external validation cohort included 2,531 patients who delivered during 2020. Nomogram was created using data such as age, race, occupation, parity, gestational weeks, labor time, neonatal weight, analgesic delivery, gestational diabetes mellitus, premature rupture of membranes, anemia, hypertension, adenomyosis, and placental adhesion. The nomogram has good predictive power and clinical practicality through the analysis of the area under the curve and decision curve analysis. Internal verification was performed on the nomogram for PPH, demonstrating consistency between the nomogram's predicted probability and actual probability. Conclusions: The developed and validatable nomogram is a good predictor of PPH in vaginal delivery and can be used in clinical practice to guide obstetricians to administer preventive therapies before delivery.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.