Cardiovascular Risk–Prediction Models in Cancer Survivors: A Meta-Analysis Publisher Pubmed



Kazemian S ; Vakilpour A ; Scherrer Crosbie M
Source: European Heart Journal Published:2026

Abstract

This systematic review and meta-analysis included 34 studies encompassing 983 438 adult cancer survivors and evaluated the performance of both general-population and cancer-specific cardiovascular risk–prediction models across vascular events, cancer therapy-related cardiac dysfunction or heart failure, arrhythmias, and composite cardiovascular event outcomes. In total, 27 unique risk scores were assessed. Approximately one-third of model–outcome evaluations demonstrated acceptable-to-good discrimination, defined as an area under the curve of ≥0.70. Among general-population models, four risk scores demonstrated acceptable-to-good discrimination for vascular events, three models were acceptable for CTRCD/heart failure, and two models for arrhythmias. The New Zealand CVD score was the only composite outcome model with an AUC ≥0.70. Cancer-specific scores, such as HFA-ICOS and the Ezaz score, showed high specificity for selected outcomes and may support rule-in decisions, although their lower sensitivity limits use for ruling out risk of CTRCD/heart failure. In our subgroup analysis, model performance varied by cancer type, with more consistent discrimination in breast cancer cohorts and substantially poorer performance in survivors of haematologic malignancies, indicating limited transportability across cancer populations. In summary, these findings provide a comprehensive overview of available cardiovascular risk–prediction tools and may assist clinicians in selecting appropriate models for primary cardiovascular prevention in cancer survivors while highlighting the need for exposure-aware, externally validated models tailored to specific cancer populations. © The Author(s) 2026. Published by Oxford University Press on behalf of the European Society of Cardiology. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.