Enhancing venous thromboembolism risk prediction after immunotherapy for lung cancer.
Model's high-risk group had more than double the venous thromboembolism rate of low-risk group
Enhancing venous thromboembolism risk prediction after immunotherapy for lung cancer.
Assessing venous thromboembolism (venous thromboembolism) risk after immunotherapy remains important for lung cancer management.
We analyzed 2,300 patients receiving first-line immunotherapy from two centers, randomly assigned to training (70%), validation (15%), and internal test (15%) sets, and included 491 patients from an independent external center for external validation.
high-risk patients had over double the clot rate of low-risk patients
Five feature-selection methods and five machine-learning algorithms were compared to develop a 6-month venous thromboembolism prediction model.
The Lasso-logistic model showed the best performance, with areas under the curve of 0.692 and 0.728 in the internal and external test sets, respectively, outperforming Khorana, Padua, PROTECHT, ONKOTEV, and COMPASS-CAT scores (all p < 0.05).
Shapley additive explanations (SHAP) analysis identified D-dimer and Eastern Cooperative Oncology Group (Eastern Cooperative Oncology Group) performance status as the most influential predictors, supporting individualized thromboprophylaxis decisions.