Evidence map›Paper›PMID 35223881›Full record

ArticleFrontiers in medicine2021

Developing and Validating Nomogram to Predict Severe Postpartum Hemorrhage in Women With Placenta Previa Undergoing Cesarean Delivery: A Multicenter Retrospective Case-Control Study.

Xiaohe Dang, Li Zhang, Yindi Bao, Jie Xu, Hui Du, Shaoshuai Wang, Yanyan Liu, Dongrui Deng, Suhua Chen, Wanjiang Zeng and 2 more

Open access · goldAbstract read
In one paragraph

Article in Frontiers in medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
3.8field-weighted citation impact, top 7% of its field
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed, 10 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors at 5 institutions in 1 country.

Xiaohe DangDepartment of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Li ZhangDepartment of Obstetrics and Gynecology, The Central Hospital of Wuhan, Wuhan, China.
Yindi BaoDepartment of Obstetrics, Renmin Hospital of Wuhan University, Wuhan, China.
Jie XuDepartment of Obstetrics, Xianning Central Hospital, The First Affiliated Hospital of Hubei University of Science and Technology, Xianning, China.
Hui DuDepartment of Obstetrics, Maternal and Child Health Hospital of Hubei Province, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Shaoshuai WangDepartment of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Yanyan LiuDepartment of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Dongrui DengDepartment of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Suhua ChenDepartment of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Wanjiang ZengDepartment of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Ling FengDepartment of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Haiyi LiuDepartment of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Tongji Hospital · CNHuazhong University of Science and Technology · CNCentral Hospital of Wuhan · CNHubei University of Science and Technology · CNWuhan University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveDeveloping and validating nomogram to predict severe postpartum hemorrhage (SPPH) in women with placenta previa (PP) undergoing cesarean delivery.

methodsWe conducted a multicenter retrospective case-control study in five hospitals. In this study, 865 patients from January, 2018 to June, 2020 were enrolled in the development cohort, and 307 patients from July, 2020 to June, 2021 were enrolled in the validation cohort. Independent risk factors for SPPH were obtained by using the multivariate logistic regression, and preoperative nomogram and intraoperative nomogram were developed, respectively. We compared the discrimination, calibration, and net benefit of the two nomograms in the development cohort and validation cohort. Then, we tested whether the intraoperative nomogram could be used before operation.

resultsThere were 204 patients (23.58%) in development cohort and 80 patients (26.06%) in validation cohort experienced SPPH. In development cohort, the areas under the receiver operating characteristic (ROC) curve (AUC) of the preoperative nomogram and intraoperative nomogram were 0.831 (95%

conclusionWe developed the preoperative nomogram and intraoperative nomogram to predict the risk of SPPH in women with PP undergoing cesarean delivery. By comparing the discrimination, calibration, and net benefit of the two nomograms in the development cohort and validation cohort, we think that the intraoperative nomogram performed better. Moreover, application of the intraoperative nomogram before operation can still achieve good prediction effect, which can be improved if the severity of placenta accreta spectrum (PAS) can be accurately distinguished preoperatively. We expect to conduct further prospective external validation studies on the intraoperative nomogram to evaluate its application value.

Indexed as

multivariate logistic regressionnomogramplacenta previaprediction modelsevere postpartum hemorrhage

Identifiers

PMID35223881
PMCPMC8873861
OpenAlexW4211196862

What Socratic holds

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Registered trials

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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 Socratic graph.