Abstract 4369967: Performance of Cardiovascular Risk Prediction Models in Cancer Survivors: A Systematic Review and Meta-Analysis

Background: Cancer survivors have a substantial risk of cardiovascular diseases (CVD) due to shared risk factors and cancer treatment-induced cardiac toxicity. However, the predictive power of cardiovascular risk prediction models in this population remains unclear. Research Question: This systematic review and meta-analysis aims to synthesize available data on the performance of various CVD risk scores in adult cancer survivors. Methods: PubMed, Embase, Scopus, and Web of Science databases were systematically searched from inception until May 2025 for studies that validated the performance of CVD risk prediction models in cancer survivors. Risk scores were evaluated across 4 outcome categories: vascular events (myocardial infarction, peripheral arterial disease, and stroke), cancer therapy-related cardiac dysfunction (CTRCD) or heart failure, arrhythmias, and composite CV events. For each score, discrimination metrics (area under the curve [AUC] and C-index) were pooled using random-effects models. Calibration metrics (sensitivity, specificity, and accuracy) were pooled using a bivariate random-effects model. Results: A total of 31 observational studies involving 901,664 patients (pooled mean age 61.1±14.4 years; 50.8% female) diagnosed with cancer were included. The most common cancer types were breast cancer (18.1%) and colon cancer (11.6%). In these studies, 27 unique risk scores (20 targeted to the general population and 7 to patients with cancer) were assessed. Good discriminatory power (AUC ≥0.70) was observed in 3 out of 8 risk scores predicting vascular events, 3 out of 9 for CTRCD or heart failure, 3 out of 10 for arrhythmia, and 5 out of 8 for composite cardiovascular events. Among all risk scores, the CORE (AUC: 0.74) and SCORE (5 studies; pooled AUC: 0.74, sensitivity: 52%, specificity: 85%, accuracy: 65%) risk scores exhibited the highest predictive performance for vascular events. For predicting CTRCD or heart failure outcome, the ARIC-HF and FRESCO risk scores showed the highest predictive performance, with both achieving an AUC of 0.76. Conclusion: Available cardiovascular risk scores developed in both general and cardio-oncology populations demonstrate moderate to good predictive power in cancer survivors. However, their calibration and generalizability remain limited. Future studies are needed to recalibrate and optimize scores in cardio-oncology settings to identify individuals at higher risk of developing CVD.