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Updated: Jun 13, 2026

Venous Thrombosis Assay in a Mouse Model of Cancer
Published on: January 5, 2024
Development and validation of a risk assessment model for hospital-acquired venous thrombosis in medical in-patients
Karlyn A Martin1, Katherine Wilkinson2, Andrew Sparks3
1Department of Medicine, Larner College of Medicine at the University of Vermont, Burlington, Vermont, USA.
A new risk assessment model (RAM) for hospital-acquired venous thromboembolism (VTE) in cancer patients was developed. This cancer-specific RAM outperforms existing models, aiding individualized risk assessment for medical inpatients with cancer.
Area of Science:
- Oncology
- Internal Medicine
- Epidemiology
Background:
- Existing risk assessment models (RAM) for hospital-acquired venous thromboembolism (VTE) show underperformance in cancer patients.
- There is a need for validated RAMs tailored to the unique risk factors of cancer patients admitted to medical services.
Purpose of the Study:
- To develop and validate a novel RAM for predicting hospital-acquired VTE in adult patients with active cancer admitted to medical services.
- To compare the performance of the new model against a refitted existing RAM.
Main Methods:
- A retrospective cohort study included over 120,000 medical admissions across 6 academic health systems (2016-2022).
- A de-novo RAM was developed using Bayesian LASSO logistic regression, incorporating cancer-specific predictors.
- The model was internally validated to assess its predictive accuracy and calibration.
Main Results:
- The de-novo RAM identified key predictors including prior VTE, malnutrition, specific cancer types (hematologic, lung), inflammatory comorbidities, age, and anticoagulation status.
- The developed RAM achieved an AUC of 0.68 in the development cohort and 0.64 in the validation cohort.
- The cancer-specific RAM demonstrated superior performance compared to a refitted non-cancer specific model (AUC 0.60).
Conclusions:
- The newly developed and validated RAM for hospital-acquired VTE in cancer patients offers improved risk prediction.
- This tool facilitates personalized risk assessment for medical inpatients with cancer, potentially improving VTE prevention strategies.
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