Evidence map›Paper›PMID 37499868›Full record

ArticleJournal of vascular surgery. Venous and lymphatic disorders2023

Prediction of bleeding in patients being considered for venous thromboembolism prophylaxis.

Shalini Sahoo, Hilary Hayssen, Brian Englum, Minerva Mayorga-Carlin, Tariq Siddiqui, Phuong Nguyen, Aman Kankaria, Yelena Yesha, John D Sorkin, Brajesh K Lal

Open access · bronzeAbstract read
In one paragraph

Article in Journal of vascular surgery. Venous and lymphatic disorders, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.6field-weighted citation impact, top 32% 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

2 citing papers in PubMed, 2 citations in OpenAlex.

  1. Article
  2. Review
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

10 authors at 3 institutions in 1 country.

Shalini SahooDepartment of Surgery, University of Maryland, Baltimore, MD; Surgery Service, Veterans Affairs Medical Center, Baltimore, MD.
Hilary HayssenDepartment of Surgery, University of Maryland, Baltimore, MD; Surgery Service, Veterans Affairs Medical Center, Baltimore, MD.
Brian EnglumDepartment of Surgery, University of Maryland, Baltimore, MD.
Minerva Mayorga-CarlinDepartment of Surgery, University of Maryland, Baltimore, MD; Surgery Service, Veterans Affairs Medical Center, Baltimore, MD.
Tariq SiddiquiSurgery Service, Veterans Affairs Medical Center, Baltimore, MD.
Phuong NguyenDepartment of Computer Science, University of Miami, Miami, FL.
Aman KankariaDepartment of Surgery, University of Maryland, Baltimore, MD; Surgery Service, Veterans Affairs Medical Center, Baltimore, MD.
Yelena YeshaDepartment of Computer Science, University of Miami, Miami, FL.
John D SorkinDepartment of Medicine, University of Maryland School of Medicine, Baltimore, MD; Geriatric Research, Education, and Clinical Center, Veterans Affairs Medical Center, Baltimore, MD.
Brajesh K LalDepartment of Surgery, University of Maryland, Baltimore, MD; Surgery Service, Veterans Affairs Medical Center, Baltimore, MD. Electronic address: blal@som.umaryland.edu.
University of Maryland, Baltimore · USUniversity of Miami · USVeterans Health Administration · US

Funding

QAQC Johns Hopkins Institute for Clinical and Translational ResearchUL1TR003098 · NCATS · JOHNS HOPKINS UNIVERSITY · PI FORD, DANIEL ERNEST · 2019 to 2023
$57.7M
CREST-2 Clinical Coordinating CenterU01NS080168 · NINDS · MAYO CLINIC JACKSONVILLE · PI Thomas G Brott, JAMES F MESCHIA · 2014 to 2026
$56.3M
University of Maryland Claude D. Pepper Older Americans Independence Center (UM-OAIC)P30AG028747 · NIA · UNIVERSITY OF MARYLAND BALTIMORE · PI ALICE S. RYAN · 2006 to 2026
$28.9M
RESEARCH TRAINING IN THE EPIDEMIOLOGY OF AGINGT32AG000262 · NIA · UNIVERSITY OF MARYLAND BALTIMORE · PI ANN L GRUBER-BALDINI, DENISE L ORWIG · 1998 to 2026
$8.2M
Healthy Aging Neighborhoods of Diversity Across LifespanZ01AG000513 · NIA · NATIONAL INSTITUTE ON AGING · PI EVANS, MICHELE K · 2001 to 2008
$6.8M
Carotid Revascularization and Medical Management for Asymptomatic CarotidStenosis Trial - Hemodynamics (CREST-H)R01NS097876 · NINDS · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI RONALD M LAZAR, DAVID SIGMUND LIEBESKIND · 2017 to 2026
$6.4M
CSRD VA I01 CX001621CSRD VA I01 CX002747Intramural NIH HHS Z01 AG000513NCATS NIH HHS UL1 TR003098NIA NIH HHS P30 AG028747NIA NIH HHS T32 AG000262NINDS NIH HHS R01 NS097876NINDS NIH HHS U01 NS080168RRD VA I01 RX000995
6 · The paper itself

Abstract

backgroundVenous thromboembolism (pulmonary embolism and deep vein thrombosis) is an important preventable cause of in-hospital death. Prophylaxis with low doses of anticoagulants reduces the incidence of venous thromboembolism but can also cause bleeding. It is, therefore, important to stratify the risk of bleeding for hospitalized patients when considering pharmacologic prophylaxis. The IMPROVE (international medical prevention registry on venous thromboembolism) and Consensus risk assessment models (RAMs) are the two tools available for such patients. Few studies have evaluated their ability to predict bleeding in a large, unselected cohort of patients. We assessed the ability of the IMPROVE and Consensus bleeding RAMs to predict bleeding within 90 days of hospitalization in a comprehensive analysis encompassing all hospitalized patients, regardless of surgical vs nonsurgical status.

methodsWe analyzed consecutive first hospital admissions of 1,228,448 unique surgical and nonsurgical patients to 1298 Veterans Affairs facilities nationwide between January 2016 and December 2021. IMPROVE and Consensus scores were generated using data from a repository of their common electronic medical records. We assessed the ability of the two RAMs to predict bleeding within 90 days of admission. We used area under the receiver operating characteristic curves to determine the prediction of bleeding by each RAM.

resultsOf 1,228,448 hospitalized patients, 324,959 (26.5%) were surgical and 903,489 (73.5%) were nonsurgical. Of these patients, 68,372 (5.6%) had a bleeding event within 90 days of admission. The Consensus RAM scores ranged from -5.60 to -1.21 (median, -4.93; interquartile range, -5.60 to -4.93). The IMPROVE RAM scores ranged from 0 to 22 (median, 3.5; interquartile range, 2.5-5). Both showed good calibration, with higher scores associated with higher bleeding rates. The ability of both RAMs to predict 90-day bleeding was low (area under the receiver operating characteristic curve 0.61 for the IMPROVE RAM and 0.59 for the Consensus RAM). The predictive ability was also low at 30 and 60 days for surgical and nonsurgical patients, patients receiving prophylactic, therapeutic, or no anticoagulation, and patients hospitalized for ≥72 hours. Prediction was also low across different bleeding outcomes (ie, any bleeding, gastrointestinal bleeding, nongastrointestinal bleeding, and bleeding or death).

conclusionsIn this large, unselected, nationwide cohort of surgical and nonsurgical hospital admissions, increasing IMPROVE and Consensus bleeding RAM scores were associated with increasing bleeding rates. However, both RAMs had low ability to predict bleeding at 0 to 90 days after admission. Thus, the currently available RAMs require modification and rigorous reevaluation before they can be applied universally.

Indexed as

Venous ThromboembolismAnticoagulantsHemorrhageHospital MortalityHumansRisk AssessmentRisk FactorsAnticoagulantsBleedingPredictionRisk factorsVenous thromboembolism prophylaxis

Identifiers

PMID37499868
PMCPMC11017967
OpenAlexW4385242416

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.