Evidence map›Paper›PMID 39874351›Full record

ArticlePloS one2025

Racial disparities in extended venous thromboembolism prophylaxis after hysterectomy.

Wenbo Wu, Sherry Wu, Sim Berlene Mariano, Richard E Burney, Jonathan P Kuriakose

Abstract read
In one paragraph

Article in PloS one, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Wenbo WuDepartments of Population Health and Medicine, New York University Grossman School of Medicine, New York, NY, United States of America.ORCID 0000-0002-7642-9773
Sherry WuDepartment of Biostatistics, New York University School of Global Public Health, New York, NY, United States of America.ORCID 0009-0004-9965-8730
Sim Berlene MarianoRobert Wood Johnson Medical School, Rutgers University, New Brunswick, NJ, United States of America.
Richard E BurneyMichigan Surgical Quality Collaborative, Institute of Health Policy & Innovation, Ann Arbor, MI, United States of America.
Jonathan P KuriakoseRobert Wood Johnson Medical School, Rutgers University, New Brunswick, NJ, United States of America.

Funding

Scaling Community-Clinical Linkage Models to Address Diabetes and Hypertension Disparities in the Southeastern U.S.U54MD000538 · NIMHD · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · PI TRINH-SHEVRIN, CHAU · 2017 to 2022
$11.3M
NIMHD NIH HHS U54 MD000538
6 · The paper itself

Abstract

backgroundVenous thromboembolism (VTE) is a significant preventable cause of postoperative morbidity and mortality after major abdominopelvic surgery that calls for extended VTE prophylaxis (eVTEp). Literature suggests that significant racial disparities may exist in post-operative care.

objectiveThe study sought to examine if racial disparities exist in the administration of eVTEp after hysterectomy in a statewide collaborative.

methodsWe conducted a retrospective cohort study of post-hysterectomy patients across 69 hospitals in the Michigan Surgical Quality Collaborative from January 2016 to February 2020. The variable of interest was race (Black/African or White American). The primary outcome was administration or absence of eVTEp. Descriptive statistics and mixed effects logistic regression were performed for risk adjustment with covariates such as age, cancer occurrence, inflammatory bowel disease, American Society of Anesthesiologists physical status classification, perioperative VTE prophylaxis, postoperative VTE prophylaxis, surgical approach, and surgical duration, among other variables.

resultsIn total, 24,513 patients underwent hysterectomy. Of these patients, 1,107 (4.45%) received eVTEp, 153 (13.24%) of which were Black and 954 (82.53%) of which were White. Mixed effects logistic regression analysis suggested that Black patients were significantly less likely to receive eVTEp than White patients (odds ratio = 0.776; 95% CI: 0.615-0.979; P = 0.039). Additionally, tobacco use, coronary artery disease, bleeding disorder, cancer occurrence, functional status, perioperative VTE prophylaxis, surgical duration, length of stay, and surgical approach were associated with a higher likelihood of receiving eVTEp.

conclusioneVTEp is recommended for the prevention of post-discharge VTE in select patients after hysterectomy. Regression analysis showed that, compared to their White counterparts, Black females were significantly less likely to receive eVTEp. The underlying reasons for this disparity require further investigation into possible socioeconomic influences and inherent biases.

Indexed as

Healthcare DisparitiesHysterectomyPostoperative ComplicationsVenous ThromboembolismAdultAgedBlack or African AmericanFemaleHumansMichiganMiddle AgedRetrospective StudiesWhite

Identifiers

PMID39874351
PMCPMC11774358

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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.