Cardiovascular risk-prediction models in cancer survivors: a meta-analysis.

In total, 27 unique risk scores were assessed.

总共评估了27种独特的风险评分。

Approximately one-third of model-outcome evaluations demonstrated acceptable-to-good discrimination, defined as an area under the curve of ≥0.70.

大约三分之一的模型结果评估显示出可接受到良好的区分度，定义为曲线下面积≥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.

在一般人群模型中，有四种风险评分对血管事件显示出可接受到良好的区分度，三种模型对癌症治疗相关的心脏功能障碍/心力衰竭可接受，两种模型对心律失常可接受。

The New Zealand CVD score was the only composite outcome model with an AUC ≥0.70.

新西兰心血管疾病评分是唯一一个曲线下面积≥0.70的复合结果模型。

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.

癌症特异性评分，如HFA-ICOS和Ezaz评分，对于选定的结果显示出高特异性，并可能支持纳入决策，尽管它们较低的敏感性限制了用于排除CTRCD/心力衰竭风险的使用。

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.

在我们的亚组分析中，模型性能因癌症类型而异，在乳腺癌队列中区分度更为一致，在血液恶性肿瘤幸存者中性能显著较差，表明在不同癌症人群中模型的适用性有限。

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.

总之，这些发现为现有的心血管风险预测工具提供了全面的概述，并可能帮助临床医生为癌症幸存者选择适当的一级心血管预防模型，同时强调了需要针对特定癌症人群的暴露意识、外部验证的模型。