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.

BACKGROUND

Patients with cancer are at elevated risk of venous thromboembolism (VTE). While primary thromboprophylaxis reduces VTE incidence, it also increases bleeding risk, necessitating accurate risk stratification. Existing tools (eg, the Khorana score) have modest predictive value, and bleeding risk models have not been validated in ambulatory patients with cancer.

OBJECTIVES

To develop and internally validate prediction models for VTE and clinically relevant bleeding in ambulatory patients with cancer initiating chemotherapy.

METHODS

We used data from 514 participants in the apixaban for the prevention of venous thromboembolism in high-risk ambulatory cancer patients randomized trial. Outcomes included objectively confirmed VTE and clinically relevant bleeding over 7 months. For each outcome, we trained 3 models: logistic regression with L1 regularization and 2 extreme gradient boosting models (1 using all variables, including biomarkers and genetic data; 1 using only routinely collected variables). Performance was evaluated using optimism-adjusted area under the curve (AUC) values, precision-recall AUC values, calibration, and diagnostic statistics.

RESULTS

VTE and clinically relevant bleeding occurred in 8.4% and 9.0% of patients, respectively. The full-variable extreme gradient boosting model performed best (VTE AUC: 0.92; bleeding AUC: 0.90). Key VTE predictors included factor V Leiden, topoisomerase inhibitor use, apixaban, stomach or pancreatic cancer, hemoglobin and high-sensitivity troponin T whereas top bleeding predictors included extracellular vesicles procoagulant activity, N-terminal pro-B-type natriuretic peptide, C-reactive protein, age, body mass index, and blood counts.

CONCLUSION

In this proof-of-concept study, prediction models incorporating biomarker and genetic variables accurately predicted VTE and clinically relevant bleeding in patients with cancer, though caution is warranted due to the modest sample size.