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New Machine Learning Models Improve Prediction of Thrombosis and Bleeding in Cancer – JTH

JTH/LinkedIn

Jul 10, 2026, 20:47

New Machine Learning Models Improve Prediction of Thrombosis and Bleeding in Cancer – JTH

Journal of Thrombosis and Haemostasis (JTH) shared on LinkedIn about a recent article by Danielle Carole Roy et al, adding:

”Development of models for predicting the 7-month risk of venous thromboembolism and clinically relevant bleeding in ambulatory patients with cancer (AVERT trial analysis)

Using data from 514 ambulatory cancer patients in the AVERT trial, machine-learning models combining biomarkers and genetics predicted both venous thromboembolism (AUC 0.92) and clinically relevant bleeding (AUC 0.90).

VTE risk tracked with factor V Leiden and certain cancers, while bleeding risk tracked with cardiac and inflammatory markers.

The sample stayed modest.

Title: Development of models for predicting the 7-month risk of venous thromboembolism and clinically relevant bleeding in ambulatory patients with cancer: analysis from the apixaban for the prevention of venous thromboembolism in high-risk ambulatory cancer patients trial

Authors: Danielle Carole Roy, Tzu-Fei Wang, Philip Wells, Ranjeeta Mallick, Dylan Burger, Marc Carrier, Steven Hawken

New Machine Learning Models Improve Prediction of Thrombosis and Bleeding in Cancer - JTH