ArticleResearch and practice in thrombosis and haemostasis2024
A real-time prognostic model for venous thromboembolic events among hospitalized adults.
Article in Research and practice in thrombosis and haemostasis, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06939803 (AI-driven Clinical Decision Support to Reduce Hospital-Acquired Venous Thromboembolism), which is not on this map. Cited by 3 papers.
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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.
AI-driven Clinical Decision Support to Reduce Hospital-Acquired Venous Thromboembolism
Who cites it
3 citing papers in PubMed.
- Explainable Machine Learning for Predicting Deep Vein Thrombosis in Critically Ill Patients with COPD: Development and Multicenter External Validation.International journal of chronic obstructive pulmonary disease · 2026Article
- AI-Driven Clinical Decision Support to Reduce Hospital-Acquired Venous Thromboembolism: A Trial Protocol.JAMA network open · 2025Article
- Predicting venous thromboembolism among hospitalized adults: a protocol for development and validation of an implementable real-time prognostic model.Diagnostic and prognostic research · 2025Review
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Hospital-acquired venous thromboembolism (HA-VTE) is a leading cause of morbidity and mortality among hospitalized adults. Guidelines recommend use of a risk-prediction model to estimate HA-VTE risk for individual patients. Extant models do not perform well for broad patient populations and are not conducive to automation in clinical practice. Objectives: To develop an automated, real-time prognostic model for venous thromboembolism during hospitalization among all adult inpatients using readily available data from the electronic health record. Methods: The derivation cohort included inpatient hospitalizations ("encounters") for patients ≥16 years old at Vanderbilt University Medical Center between 2018 and 2020 ( Results: There were 1187 HA-VTEs in the derivation cohort (9.0 per 1000 encounters) and 864 in the validation cohort (13.8 per 1000 encounters). The prognostic model included 25 variables, with placement of a central line among the most important predictors. Prediction performance of the model was excellent ( Conclusion: This fully automated prognostic model uses readily available data from the electronic health record, exhibits superior prediction performance compared with existing models, and generates granular risk stratification in the form of a predicted probability of HA-VTE for each patient.
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Registered trials
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