POLE: Development and Validation of a Pulmonary Embolism Prediction Model in Lung Cancer

Background Prediction models for cancer-associated pulmonary embolism (PE) in lung cancer patients are scarce. This study aimed to develop and validate a novel model to accurately predict PE risk in this population. Methods A retrospective cohort (n = 476) was used to identify PE-related risk factors and construct a predictive nomogram using Cox regression. Validation was performed in a prospective cohort (n = 140). The model's performance was compared with the Khorana score. Results The newly developed nomogram termed P ulmonary emb O lism in L ung canc E r ( POLE ) included seven variables: activated partial thromboplastin time, D-dimer, serum phosphorus, carbohydrate antigen 19-9, history of lung cancer surgery, targeted therapy, and age. It demonstrated good predictive performance in the retrospective study (area under the curve [AUC]: 0.776, 95% confidence interval [CI] 0.720-0.833, P < 0.001). The AUC values at 1, 3, 6, and 9 months after lung cancer diagnosis were 0.840, 0.839, 0.799, and 0.801, respectively. In the prospective study, the AUC values for the POLE model at 1, 3, 6, and 9 months after lung cancer diagnosis were 0.859, 0.806, 0.754, and 0.746, respectively. Moreover, in both derivation and validation cohorts, there were significant differences in the probability of PE occurrence among lung cancer patients being stratified into different risk strata (all P < 0.001). The POLE model (AUC 0.762) outperformed the Khorana score (AUC 0.560). Conclusions The POLE model, based on seven clinical parameters, was developed and validated for predicting cancer-associated PE in lung cancer patients, demonstrating high accuracy, calibration, and stability.