Evidence map›Paper›PMID 37838307›Full record

ArticleJournal of vascular surgery. Venous and lymphatic disorders2024

Ability of Caprini and Padua risk-assessment models to predict venous thromboembolism in a nationwide Veterans Affairs study.

Hilary Hayssen, Shalini Sahoo, Phuong Nguyen, Minerva Mayorga-Carlin, Tariq Siddiqui, Brian Englum, Julia F Slejko, C Daniel Mullins, Yelena Yesha, John D Sorkin and 1 more

Open access · hybridAbstract read
In one paragraph

Article in Journal of vascular surgery. Venous and lymphatic disorders, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed, 1 pooled it
4.4field-weighted citation impact, top 4% 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

18 citing papers in PubMed, 1 synthesis or guideline pooled it, 16 citations in OpenAlex.

  1. Pooled it
  2. Article
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  5. Article
  6. Comparative Analysis of Padua and Caprini Scores in Predicting Venous Thromboembolism Risk Among Nonagenarians: A Cross-Sectional Study From Rugao.Annals of noninvasive electrocardiology : the official journal of the International Society for Holter and Noninvasive Electrocardiology, Inc · 2025
    Article
  7. Article
  8. A composite risk assessment model for venous thromboembolism.Journal of vascular surgery. Venous and lymphatic disorders · 2025
    Article
  9. Article
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  14. Article
  15. Venous thromboembolism risk models may be ready for a makeover.Journal of vascular surgery. Venous and lymphatic disorders · 2024
    Article
  16. External Validation of the ICU-Venous Thromboembolism Risk Assessment Model in Adult Critically Ill Patients.Clinical and applied thrombosis/hemostasis : official journal of the International Academy of Clinical and Applied Thrombosis/Hemostasis
    Article
  17. Evaluating and Improving Knowledge of DVT Prophylaxis Among Healthcare Professionals at a Tertiary Care Hospital in Karachi, Pakistan: A Quality Improvement Study.Clinical and applied thrombosis/hemostasis : official journal of the International Academy of Clinical and Applied Thrombosis/Hemostasis
    Article
  18. CatBoost Machine Learning Model for Thrombosis Risk Prediction in Critically Ill Cancer Patients: A MIMIC-IV Database Study.Clinical and applied thrombosis/hemostasis : official journal of the International Academy of Clinical and Applied Thrombosis/Hemostasis
    Observational
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

11 authors at 3 institutions in 1 country.

Hilary HayssenDepartment of Surgery, University of Maryland, Baltimore, MD; Surgery Service, Veterans Affairs Medical Center, Baltimore, MD.
Shalini SahooDepartment of Surgery, University of Maryland, Baltimore, MD; Surgery Service, Veterans Affairs Medical Center, Baltimore, MD.
Phuong NguyenDepartment of Computer Science, University of Miami, Miami, FL.
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.
Brian EnglumDepartment of Surgery, University of Maryland, Baltimore, MD.
Julia F SlejkoDepartment of Health Services Research, University of Maryland, Baltimore, MD.
C Daniel MullinsDepartment of Health Services Research, University of Maryland, 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 Stephen L. Seliger · 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
Role of a Novel Exercise Program to Prevent Post-thrombotic SyndromeI01RX000995 · VA · BALTIMORE VA MEDICAL CENTER · PI LAL, BRAJESH K · 2015 to 2020
–
Asymptomatic Carotid Stenosis: Cognitive Function and Plaque Correlates - 2I01CX001621 · VA · BALTIMORE VA MEDICAL CENTER · PI LAL, BRAJESH K · 2017 to 2021
–
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

objectiveVenous thromboembolism (VTE) is a preventable complication of hospitalization. Risk-stratification is the cornerstone of prevention. The Caprini and Padua are two of the most commonly used risk-assessment models (RAMs) to quantify VTE risk. Both models perform well in select, high-risk cohorts. Although VTE RAMs were designed for use in all hospital admissions, they are mostly tested in select, high-risk cohorts. We aim to evaluate the two RAMs in a large, unselected cohort of patients.

methodsWe analyzed consecutive first hospital admissions of 1,252,460 unique surgical and non-surgical patients to 1298 Veterans Affairs facilities nationwide between January 2016 and December 2021. Caprini and Padua scores were generated using the Veterans Affairs' national data repository. We determined the ability of the two RAMs to predict VTE within 90 days of admission. In secondary analyses, we evaluated prediction at 30 and 60 days, in surgical vs non-surgical patients, after excluding patients with upper extremity deep vein thrombosis, in patients hospitalized ≥72 hours, after including all-cause mortality in a composite outcome, and after accounting for prophylaxis in the predictive model. We used area under the receiver operating characteristic curves (AUCs) as the metric of prediction.

resultsA total of 330,388 (26.4%) surgical and 922,072 (73.6%) non-surgical consecutively hospitalized patients (total N = 1,252,460) were analyzed. Caprini scores ranged from 0 to 28 (median, 4; interquartile range [IQR], 3-6); Padua scores ranged from 0-13 (median, 1; IQR, 1-3). The RAMs showed good calibration and higher scores were associated with higher VTE rates. VTE developed in 35,557 patients (2.8%) within 90 days of admission. The ability of both models to predict 90-day VTE was low (AUCs: Caprini, 0.56; 95% confidence interval [CI], 0.56-0.56; Padua, 0.59; 95% CI, 0.58-0.59). Prediction remained low for surgical (Caprini, 0.54; 95% CI, 0.53-0.54; Padua, 0.56; 95% CI, 0.56-0.57) and non-surgical patients (Caprini, 0.59; 95% CI, 0.58-0.59; Padua, 0.59; 95% CI, 0.59-0.60). There was no clinically meaningful change in predictive performance in any of the sensitivity analyses.

conclusionsCaprini and Padua RAM scores have low ability to predict VTE events in a cohort of unselected consecutive hospitalizations. Improved VTE RAMs must be developed before they can be applied to a general hospital population.

Indexed as

Venous ThromboembolismVeteransHumansRetrospective StudiesRisk AssessmentRisk FactorsDeep vein thrombosisPreventionPulmonary embolismRisk assessmentVenous thromboembolism

Identifiers

PMID37838307
PMCPMC10922503
OpenAlexW4387580782

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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.