Predicting cardiovascular toxicity in anti-PD-1/PD-L1 therapy: a risk factor analysis and model development

This study aimed to investigate risk factors for cardiovascular toxicity following anti-PD-1/PD-L1 therapy and develop a predictive model.

We retrospectively collected data from 2,665 patients with solid tumors treated with anti-PD-1/PD-L1 therapy at two-center between October 2018 and October 2023.We performed univariate and multivariate logistic regression to identify predictors of cardiovascular toxicity and developed a nomogram. Internal evaluation and internal validation were performed using receiver operating characteristic (ROC), decision curve analysis (DCA), calibration curve (CC) for internal evaluation and internal validation.

Univariate logistic regression identified the Systemic Inflammatory Response Index (SIRI;OR 2.26, 95% CI 1.19-4.27, p = 0.012), Eastern Cooperative Oncology Group performance status (ECOG;OR 9.67, 95% CI 3.04-30.69, p < 0.001), hypertension (OR 3.50, 95% CI 1.78-6.88, p < 0.001), diabetes (OR 2.52, 95% CI 1.13-5.66, p = 0.025), tumor metastasis (OR 0.17, 95% CI 0.08-0.39, p < 0.001), tumor stage (OR 0.40, 95% CI 0.21-0.76, p = 0.006), and sex (male vs. female)(OR 0.43, 95% CI 0.19-0.96, p = 0.040) as significant predictors. Multivariate analysis confirmed ECOG (OR 9.81, 95% CI 2.73-35.25, p < 0.001) and tumor metastasis (OR 0.26, 95% CI 0.10-0.71, p = 0.008) as independent predictors. Seven variables (p < 0.05 in univariate analysis) were included in a nomogram, which showed good accuracy and discrimination (AUC 0.77, 95% CI 0.70-0.85).

SIRI, ECOG, hypertension, diabetes, tumor metastasis, tumor stage, and sex were significant predictors of cardiovascular toxicity. ECOG was an independent risk factor, while tumor metastasis was an independent protective factor, after adjusting for other covariates. The nomogram showed good accuracy and discrimination, with clinical utility for predicting cardiovascular toxicity risk in patients receiving anti-PD-1/PD-L1 therapy.