Evidence map›Paper›PMID 42040646›Full record

ArticlePregnancy (Hoboken, N.J.)2026

A model to predict red blood cell transfusion during/after cesarean delivery.

Samuel Parry, Steven J Weiner, George R Saade, Rebecca G Clifton, John M Thorp, Monica Longo, Ashley Salazar, Wendy Dalton, Alan T N Tita, Cynthia Gyamfi-Bannerman and 9 more

Abstract read
In one paragraph

Article in Pregnancy (Hoboken, N.J.), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
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

1 citing paper in PubMed.

  1. Article
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

19 authors.

Samuel ParryDepartment of Obstetrics and Gynecology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania, USA.
Steven J WeinerDepartment of Epidemiology, The George Washington University Biostatistics Center, Washington, District of Columbia, USA.
George R SaadeDepartment of Obstetrics and Gynecology, The University of Texas Medical Branch, Galveston, Texas, USA.
Rebecca G CliftonDepartment of Epidemiology, The George Washington University Biostatistics Center, Washington, District of Columbia, USA.
John M ThorpDepartment of Obstetrics and Gynecology, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.
Monica LongoThe Eunice Kennedy Shriver National Institute of Child Health and Human Development, Bethesda, Maryland, USA.
Ashley SalazarDepartment of Obstetrics and Gynecology, The University of Texas Medical Branch, Galveston, Texas, USA.
Wendy DaltonDepartment of Obstetrics and Gynecology, Case Western Reserve University, Cleveland, Ohio, USA.
Alan T N TitaDepartment of Obstetrics and Gynecology, The University of Alabama at Birmingham, Birmingham, Alabama, USA.
Cynthia Gyamfi-BannermanDepartment of Obstetrics and Gynecology, Columbia University, New York, New York, USA.
Suneet P ChauhanDepartment of Obstetrics and Gynecology, The Texas Health Science Center at Houston, Children's Memorial Hermann Hospital, Houston, Texas, USA.
Torri D MetzDepartment of Obstetrics and Gynecology, University of Utah Health Sciences Center, Salt Lake City, Utah, USA.
Kara RoodDepartment of Obstetrics and Gynecology, The Ohio State University, Columbus, Ohio, USA.ORCID 0000-0002-8504-6715
Dwight J RouseDepartment of Obstetrics and Gynecology, Brown University, Providence, Rhode Island, USA.
Jennifer L BailitDepartment of Obstetrics and Gynecology, Case Western Reserve University, Cleveland, Ohio, USA.ORCID 0009-0000-1489-9190
William A GrobmanDepartment of Obstetrics and Gynecology, Northwestern University, Chicago, Illinois, USA.
Hyagriv N SimhanDepartment of Obstetrics and Gynecology, The University of Pittsburgh, Pittsburgh, Pennsylvania, USA.
George A MaconesDepartment of Obstetrics and Gynecology, The University of Texas at Austin, Austin, Texas, USA.
Luis D PachecoDepartment of Obstetrics and Gynecology, The University of Texas Medical Branch, Galveston, Texas, USA.

Funding

SARS-CoV-2 Vaccine Pregnancy RegistryU24HD036801 · NICHD · GEORGE WASHINGTON UNIVERSITY · PI Rebecca Gersnoviez Clifton · 2021 to 2026
$39.2M
Maternal-Fetal Medicine Units (MFMU) NetworkUG1HD040485 · NICHD · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI Noelia Milena Modad Zork · 2016 to 2026
$3.6M
NICHD Maternal Fetal Medicine Units NetworkUG1HD087192 · NICHD · UNIVERSITY OF PENNSYLVANIA · PI Lorraine Dugoff · 2016 to 2026
$3.6M
NICHD Maternal-Fetal Medicine Units (MFMU) NetworkUG1HD040544 · NICHD · CASE WESTERN RESERVE UNIVERSITY · PI Kelly S Gibson, Angela C. Ranzini · 2016 to 2026
$3.5M
Maternal-Fetal Medicine Units Network -- The Northwestern Study CenterUG1HD040512 · NICHD · NORTHWESTERN UNIVERSITY AT CHICAGO · PI Lynn M Yee · 2016 to 2026
$3.4M
NICHD Maternal-Fetal Medicine Units (MFMU) NetworkUG1HD040500 · NICHD · WOMEN AND INFANTS HOSPITAL-RHODE ISLAND · PI DWIGHT J ROUSE · 2016 to 2026
$3.4M
UofU Maternal-Fetal Medicine Units (MFMU) Network: Clinical CentersUG1HD034208 · NICHD · UTAH STATE HIGHER EDUCATION SYSTEM--UNIVERSITY OF UTAH · PI Torri D Metz · 2016 to 2026
$3.4M
NICHD Maternal Fetal Medicine Units NetworkUG1HD027915 · NICHD · OHIO STATE UNIVERSITY · PI Maged Costantine · 2016 to 2026
$3.3M
UNC MFMU: Advancing Perinatal Health via Definitive, Multi-Site Clinical StudiesUG1HD040560 · NICHD · UNIV OF NORTH CAROLINA CHAPEL HILL · PI John M Thorp · 2016 to 2026
$3.3M
NICHD Maternal-Fetal Medicine Units (MFMU) NetworkUG1HD087230 · NICHD · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI HYAGRIV N SIMHAN · 2016 to 2026
$3.2M
NICHD Maternal-Fetal Medicine Units (MFMU) NetworkUG1HD040545 · NICHD · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI Hector Mendez-Figueroa · 2016 to 2026
$3.2M
NICHD Maternal-Fetal Medicine Units NetworksUG1HD027869 · NICHD · UNIVERSITY OF ALABAMA AT BIRMINGHAM · PI ALAN THEVENET N. TITA · 2016 to 2026
$3.0M
NICHD NIH HHS U24 HD036801NICHD NIH HHS UG1 HD027869NICHD NIH HHS UG1 HD027915NICHD NIH HHS UG1 HD034208NICHD NIH HHS UG1 HD040485NICHD NIH HHS UG1 HD040500NICHD NIH HHS UG1 HD040512NICHD NIH HHS UG1 HD040544NICHD NIH HHS UG1 HD040545NICHD NIH HHS UG1 HD040560NICHD NIH HHS UG1 HD053097NICHD NIH HHS UG1 HD087192NICHD NIH HHS UG1 HD087230
6 · The paper itself

Abstract

Background: Cesarean delivery is a major risk factor for postpartum hemorrhage. A priority for postpartum hemorrhage safety bundles is the assessment of hemorrhage and transfusion risk following cesarean delivery and the identification of patients at increased risk for postpartum hemorrhage requiring red blood cell transfusion. A prediction model based on antenatal and intrapartum risk factors could be used in a standardized manner among all patients when considering crossmatch or other preparatory measures prior to cesarean delivery. Objective: To develop a multivariable prediction model for red blood cell transfusion associated with cesarean delivery using a prospective contemporary cohort. Methods: Secondary analysis of a multicenter trial of tranexamic acid (TXA) versus placebo for the prevention of red blood cell transfusion after cesarean delivery was performed. The primary outcome for this analysis was transfusion of packed red blood cells by hospital discharge or 7 days postpartum, whichever came first. Maternal characteristics and risk factors related to labor were compared between patients who received a transfusion and those who did not. Any of the characteristics that differed with a Results: A total of 418 of 10,961 patients (3.8%) received a red blood cell transfusion. Factors associated with transfusion and retained in the final model included gestational age at delivery <34 weeks or ≥41 weeks, chorioamnionitis, aspirin use in the week before delivery, preoperative hemoglobin level, preoperative platelet count <100,000, oxytocin duration, and cesarean delivery during the second stage of labor. The final multivariable model had excellent discrimination with an area under the curve (AUC) of 0.79 (95% confidence interval [CI], 0.76, 0.81). Using the same set of seven factors identified in the prediction model for transfusion, the AUC was 0.62 (95% CI, 0.60, 0.65) for differentiating patients who did and did not require surgical/radiological interventions in response to bleeding. Conclusion: We identified several factors associated with risk of red blood cell transfusion during/after cesarean delivery and a model with excellent predictive ability. Risk factors in this model, including lower preoperative hemoglobin level, aspirin use <7 days before delivery, and delivery during the second stage of labor, may be used to risk-stratify patients for preparation for transfusion to reduce postoperative complications that result from bleeding.

Indexed as

cesarean deliverypostpartum hemorrhageprediction modeltransfusion

Identifiers

PMID42040646
PMCPMC13105277

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.