External validation of a prediction model for bleeding events in anticoagulated cancer patients with venous thromboembolism (PredictAI)

Abstract

The objective of this study was to validate the PredictAI models for predicting major bleeding (MB) in patients with active cancer and venous thromboembolism (VTE) with anticoagulant (ACO) therapy, within 6 months after primary VTE, using an independent cohort of patients from the TESEO database. This study conducted an external validation of the PredictAI models using the international, prospective TESEO registry from July 2018 until October 2021. Data from 40 Spanish and Portuguese hospitals recruiting consecutive cases of cancer-associated thrombosis under anticoagulant treatment and without missing values regarding the model outcome or predictors were used. Patients with baseline MB or unknown MB status during follow-up were excluded for the validation analysis. Logistic regression (LR), decision tree (DT), and random forest (RF) approaches were used to validate the models. Included patients from the TESEO cohort (2179 patients) had similar key demographics and clinical characteristics to the PredictAI cohort (21,227 patients). During the 6-month follow-up period, 10.9% (n = 2314) and 5.9% (n = 129) of patients experienced at least one MB event in the PredictAI and TESEO cohorts, respectively. Hemoglobin, metastasis, age, platelets, leukocytes, and serum creatinine were described as predictors for MB in PredictAI; the external validation results in TESEO showed statistical significance by LR and RF approaches, with ROC-AUC values of 0.59 and 0.56, respectively (both p < 0.05). PredictAI models for predicting MB in anticoagulant-treated cancer patients within the first 6 months following VTE diagnosis have been externally validated. These models may be considered as a tool to guide objective decisions regarding the indication or extension of anticoagulant therapy in this population. The online version contains supplementary material available at 10.1007/s12094-025-03890-5.