Evidence map›Paper›PMID 38134939›Full record

Trial reportAmerican journal of perinatology2024

Validation of Three Models for Prediction of Blood Transfusion during Cesarean Delivery Admission.

Ann M Bruno, Jerome J Federspiel, Paula McGee, Luis D Pacheco, George R Saade, Samuel Parry, Monica Longo, Alan T N Tita, Cynthia Gyamfi-Bannerman, Suneet P Chauhan and 7 more

Open access · goldAbstract readMulticenter StudyRandomized Controlled Trial
In one paragraph

Trial report in American journal of perinatology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.6field-weighted citation impact, top 24% 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

3 citing papers in PubMed, 1 citations in OpenAlex.

  1. Article
  2. Article
  3. 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

17 authors at 14 institutions in 1 country.

Ann M BrunoDepartment of Obstetrics and Gynecology, University of Utah Health Sciences Center, Salt Lake City, Utah.
Jerome J FederspielDivision of Maternal-Fetal Medicine, Department of Obstetrics and Gynecology, Duke University School of Medicine, Durham, North Carolina.ORCID 0000-0003-0321-6280
Paula McGeeDivision of Maternal-Fetal Medicine, Department of Obstetrics and Gynecology, The George Washington University Biostatistics Center, Washington, District of Columbia.
Luis D PachecoDivision of Maternal-Fetal Medicine, Department of Obstetrics and Gynecology, University of Texas Medical Branch, Galveston, Texas.
George R SaadeDivision of Maternal-Fetal Medicine, Department of Obstetrics and Gynecology, University of Texas Medical Branch, Galveston, Texas.
Samuel ParryDivision of Maternal-Fetal Medicine, Department of Obstetrics and Gynecology, University of Pennsylvania, Philadelphia, Pennsylvania.
Monica LongoDivision of Maternal-Fetal Medicine, Department of Obstetrics and Gynecology, Eunice Kennedy Shriver National Institute of Child Health and Human Development, Bethesda, Maryland.
Alan T N TitaDivision of Maternal-Fetal Medicine, Department of Obstetrics and Gynecology, University of Alabama at Birmingham, Birmingham, Alabama.
Cynthia Gyamfi-BannermanDivision of Maternal-Fetal Medicine, Department of Obstetrics and Gynecology, Columbia University, New York, New York.
Suneet P ChauhanDivision of Maternal-Fetal Medicine, Department of Obstetrics and Gynecology, University of Texas Health Science Center at Houston, Children's Memorial Hermann Hospital, Houston, Texas.
Brett D EinersonDepartment of Obstetrics and Gynecology, University of Utah Health Sciences Center, Salt Lake City, Utah.
Kara RoodDivision of Maternal-Fetal Medicine, Department of Obstetrics and Gynecology, The Ohio State University, Columbus, Ohio.
Dwight J RouseDivision of Maternal-Fetal Medicine, Department of Obstetrics and Gynecology, Brown University, Providence, Rhode Island.
Jennifer BailitDivision of Maternal-Fetal Medicine, Department of Obstetrics and Gynecology, The MetroHealth Medical System, Case Western Reserve University, Cleveland, Ohio.
William A GrobmanDivision of Maternal-Fetal Medicine, Department of Obstetrics and Gynecology, Northwestern University, Chicago, Illinois.
Hyagriv N SimhanDivision of Maternal-Fetal Medicine, Department of Obstetrics and Gynecology, University of Pittsburgh, Pittsburgh, Pennsylvania.
Eunice Kennedy Shriver National Institute of Child Health Human Development Maternal-Fetal Medicine Units Network
The University of Texas Medical Branch at Galveston · USUniversity of Utah · USBrown University · USColumbia University · USDuke University · USEunice Kennedy Shriver National Institute of Child Health and Human Development · USGeorge Washington University · USMemorial Hermann · USMetroHealth Medical Center · USNorthwestern University · USThe Ohio State University · USUniversity of Alabama at Birmingham · USUniversity of Pennsylvania · USUniversity of Pittsburgh · US

Funding

SARS-CoV-2 Vaccine Pregnancy RegistryU24HD036801 · NICHD · GEORGE WASHINGTON UNIVERSITY · PI Rebecca Gersnoviez Clifton · 2021 to 2026
$39.2M
NICHD Maternal Fetal Medicine Units NetworkU10HD034208 · NICHD · UNIVERSITY OF UTAH · PI VARNER, MICHAEL W. · 1996 to 2015
$16.6M
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
Utah Women's Reproductive Health Research Career Development ProgramK12HD085816 · NICHD · UNIVERSITY OF UTAH · PI SILVER, ROBERT M. · 2015 to 2024
$3.2M
NICHD NIH HHS K12 HD085816NICHD NIH HHS K12 HD103083NICHD NIH HHS U10 HD034208NICHD 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

objectivePrediction of blood transfusion during delivery admission allows for clinical preparedness and risk mitigation. Although prediction models have been developed and adopted into practice, their external validation is limited. We aimed to evaluate the performance of three blood transfusion prediction models in a U.S. cohort of individuals undergoing cesarean delivery. STUDY

designThis was a secondary analysis of a multicenter randomized trial of tranexamic acid for prevention of hemorrhage at time of cesarean delivery. Three models were considered: a categorical risk tool (California Maternal Quality Care Collaborative [CMQCC]) and two regression models (Ahmadzia et al and Albright et al). The primary outcome was intrapartum or postpartum red blood cell transfusion. The CMQCC algorithm was applied to the cohort with frequency of risk category (low, medium, high) and associated transfusion rates reported. For the regression models, the area under the receiver-operating curve (AUC) was calculated and a calibration curve plotted to evaluate each model's capacity to predict receipt of transfusion. The regression model outputs were statistically compared.

resultsOf 10,785 analyzed individuals, 3.9% received a red blood cell transfusion during delivery admission. The CMQCC risk tool categorized 1,970 (18.3%) individuals as low risk, 5,259 (48.8%) as medium risk, and 3,556 (33.0%) as high risk with corresponding transfusion rates of 2.1% (95% confidence interval [CI]: 1.5-2.9%), 2.2% (95% CI: 1.8-2.6%), and 7.5% (95% CI: 6.6-8.4%), respectively. The AUC for prediction of blood transfusion using the Ahmadzia and Albright models was 0.78 (95% CI: 0.76-0.81) and 0.79 (95% CI: 0.77-0.82), respectively (

conclusionThree models were externally validated for prediction of blood transfusion during cesarean delivery admission in this U.S. COHORT: Overall, performance was moderate; model selection should be based on ease of application until a specific model with superior predictive ability is developed. KEY POINTS: · A total of 3.9% of individuals received a blood transfusion during cesarean delivery admission.. · Three models used in clinical practice are externally valid for blood transfusion prediction.. · Institutional model selection should be based on ease of application until further research identifies the optimal approach..

Indexed as

Blood TransfusionCesarean SectionAdultAlgorithmsAntifibrinolytic AgentsArea Under CurveErythrocyte TransfusionFemaleHumansPostpartum HemorrhagePregnancyRisk AssessmentROC CurveTranexamic AcidUnited StatesAntifibrinolytic AgentsTranexamic Acid

Identifiers

PMID38134939
PMCPMC11153014
OpenAlexW4390099580

What OpenQuestion 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 OpenQuestion graph.