SynthesisBMJ open2021
Risk assessment models for venous thromboembolism in hospitalised adult patients: a systematic review.
Synthesis in BMJ open, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 67 papers, 6 of them syntheses that pooled it.
What it found
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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.
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.
Who cites it
67 citing papers in PubMed, 6 syntheses or guidelines pooled it, 108 citations in OpenAlex.
- Artificial intelligence for venous thromboembolism risk stratification in surgical patients: a systematic review.Journal of thrombosis and thrombolysis · 2026Pooled it
- Identifying risk factors for venous thromboembolism in medical inpatients: a systematic review and meta-analysis.Research and practice in thrombosis and haemostasis · 2026Pooled it
- Evaluation of venous thromboembolism risk assessment models for hospital inpatients: the VTEAM evidence synthesis.Health technology assessment (Winchester, England) · 2024Pooled it
- Venous thromboembolism after arthroscopic shoulder surgery: a systematic review.Journal of orthopaedic surgery and research · 2023Pooled it
- An update on the global use of risk assessment models and thromboprophylaxis in hospitalized patients with medical illnesses from the World Thrombosis Day steering committee: Systematic review and meta-analysis.Journal of thrombosis and haemostasis : JTH · 2022Pooled it
- Risk Prediction Models for Deep Vein Thrombosis in Patients With Spontaneous Intracerebral Hemorrhage: A Systematic Review and Meta-Analysis.Clinical and applied thrombosis/hemostasis : official journal of the International Academy of Clinical and Applied Thrombosis/HemostasisPooled it
- Interpretable machine learning model for early prediction of deep vein thrombosis in elderly ICU patients: multicenter development and external validation.BMC medical informatics and decision making · 2026Article
- Prediction of venous thromboembolism after spontaneous intracerebral hemorrhage based on machine learning.Journal of thrombosis and thrombolysis · 2026Article
- Estimated impact of model-guided venous thromboembolism prophylaxis versus real-world practice.Journal of hospital medicine · 2026Article
- Review
- Accuracy of nurses' Caprini risk assessment for venous thromboembolism: a simulation-based observational study in China.BMC nursing · 2026Article
- Recommendations from guidelines for the prevention of venous thromboembolism in pregnant women with inherited thrombophilia.Archives of gynecology and obstetrics · 2026Review
- Development of Venous Thromboembolism Risk Prediction Models Based on Whole Blood Gene Expression Profiling Using 20 Machine Learning Algorithms: Comprehensive Analysis Study.JMIR medical informatics · 2026Article
- Current Trends in Venous Thromboprophylaxis for Inpatient Care.Journal of personalized medicine · 2026Review
- Development and validation of an explainable machine learning model for predicting postoperative venous thromboembolism in esophageal cancer.International journal of surgery (London, England) · 2026Article
- Article
- Derivation of a novel risk assessment model of venous thromboembolism in hospitalized patients.PeerJ · 2026Article
- Implementation of the Maynard-Based Risk Assessment Model for Venous Thromboembolism Inpatient Prophylaxis: A Before-and-After Study.Healthcare (Basel, Switzerland) · 2025Article
- Development, validation, and clinical utility of risk prediction models for cancer-associated venous thromboembolism: A retrospective and prospective cohort study.Asia-Pacific journal of oncology nursing · 2025Article
- Preferences Among Expert Physicians in Areas of Uncertainty in Venous Thromboembolism Management: Results from a Multiple-Choice Questionnaire.Journal of clinical medicine · 2025Article
7 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors at 6 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionHospital-acquired thrombosis accounts for a large proportion of all venous thromboembolism (VTE), with significant morbidity and mortality. This subset of VTE can be reduced through accurate risk assessment and tailored pharmacological thromboprophylaxis. This systematic review aimed to determine the comparative accuracy of risk assessment models (RAMs) for predicting VTE in patients admitted to hospital.
methodsA systematic search was performed across five electronic databases (including MEDLINE, EMBASE and the Cochrane Library) from inception to February 2021. All primary validation studies were eligible if they examined the accuracy of a multivariable RAM (or scoring system) for predicting the risk of developing VTE in hospitalised inpatients. Two or more reviewers independently undertook study selection, data extraction and risk of bias assessments using the PROBAST (Prediction model Risk Of Bias ASsessment Tool) tool. We used narrative synthesis to summarise the findings.
resultsAmong 6355 records, we included 51 studies, comprising 24 unique validated RAMs. The majority of studies included hospital inpatients who required medical care (21 studies), were undergoing surgery (15 studies) or receiving care for trauma (4 studies). The most widely evaluated RAMs were the Caprini RAM (22 studies), Padua prediction score (16 studies), IMPROVE models (8 studies), the Geneva risk score (4 studies) and the Kucher score (4 studies). C-statistics varied markedly between studies and between models, with no one RAM performing obviously better than other models. Across all models, C-statistics were often weak (<0.7), sometimes good (0.7-0.8) and a few were excellent (>0.8). Similarly, estimates for sensitivity and specificity were highly variable. Sensitivity estimates ranged from 12.0% to 100% and specificity estimates ranged from 7.2% to 100%.
conclusionAvailable data suggest that RAMs have generally weak predictive accuracy for VTE. There is insufficient evidence and too much heterogeneity to recommend the use of any particular RAM. PROSPERO REGISTRATION NUMBER: Steve Goodacre, Abdullah Pandor, Katie Sworn, Daniel Horner, Mark Clowes. A systematic review of venous thromboembolism RAMs for hospital inpatients. PROSPERO 2020 CRD42020165778. Available from https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=165778https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=165778.
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Registered trials
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.