Background

The role of CT angiography–derived fractional flow reserve (CT-FFR) in pre–transcatheter aortic valve replacement (TAVR) assessment is uncertain.

Purpose

To evaluate the predictive value of on-site machine learning–based CT-FFR for adverse clinical outcomes in candidates for TAVR.

Materials and Methods

This observational retrospective study included patients with severe aortic stenosis referred to TAVR after coronary CT angiography (CCTA) between September 2014 and December 2019. Clinical end points comprised major adverse cardiac events (MACE) (nonfatal myocardial infarction, unstable angina, cardiac death, or heart failure admission) and all-cause mortality. CT-FFR was obtained semiautomatically using an on-site machine learning algorithm. The ability of CT-FFR (abnormal if ≤0.75) to predict outcomes and improve the predictive value of the current noninvasive work-up was assessed. Survival analysis was performed, and the C-index was used to assess the performance of each predictive model. To compare nested models, the likelihood ratio χ² test was performed.

Results

A total of 196 patients (mean age ± standard deviation, 75 years ± 11; 110 women [56%]) were included; the median time of follow-up was 18 months. MACE occurred in 16% (31 of 196 patients) and all-cause mortality in 19% (38 of 196 patients). Univariable analysis revealed CT-FFR was predictive of MACE (hazard ratio [HR], 4.1; 95% CI: 1.6, 10.8; P = .01) but not all-cause mortality (HR, 1.2; 95% CI: 0.6, 2.2; P = .63). CT-FFR was independently associated with MACE (HR, 4.0; 95% CI: 1.5, 10.5; P = .01) when adjusting for potential confounders. Adding CT-FFR as a predictor to models that include CCTA and clinical data improved their predictive value for MACE (P = .002) but not all-cause mortality (P = .67), and it showed good discriminative ability for MACE (C-index, 0.71).

Conclusion

CT angiography–derived fractional flow reserve was associated with major adverse cardiac events in candidates for transcatheter aortic valve replacement and improved the predictive value of coronary CT angiography assessment.

© RSNA, 2021

Online supplemental material is available for this article.

See also the editorial by Choe in this issue.

中文翻译：

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经导管主动脉瓣置换术前的冠状动脉 CT 血流储备分数：临床结果

背景

CT 血管造影衍生的血流储备分数 (CT-FFR) 在经导管主动脉瓣置换术 (TAVR) 前评估中的作用尚不确定。

目的

评估基于现场机器学习的 CT-FFR 对 TAVR 候选者不良临床结果的预测价值。

材料和方法

这项观察性回顾性研究纳入了 2014 年 9 月至 2019 年 12 月期间在冠状动脉 CT 血管造影 (CCTA) 后转诊至 TAVR 的严重主动脉瓣狭窄患者。临床终点包括主要不良心脏事件 (MACE)（非致死性心肌梗死、不稳定型心绞痛、心源性死亡或心力衰竭入院）和全因死亡率。CT-FFR 是使用现场机器学习算法半自动获得的。评估了 CT-FFR（如果 ≤0.75 则异常）预测结果和提高当前无创检查的预测值的能力。进行了生存分析，并使用 C 指数来评估每个预测模型的性能。为了比较嵌套模型，进行似然比χ ²检验。

结果

共纳入 196 名患者（平均年龄 ± 标准差，75 岁 ± 11；110 名女性 [56%]）；中位随访时间为 18 个月。MACE 发生率为 16%（196 名患者中的 31 名），全因死亡率为 19%（196 名患者中的 38 名）。单变量分析显示 CT-FFR 可预测 MACE（风险比 [HR]，4.1；95% CI：1.6，10.8；P = .01），但不能预测全因死亡率（HR，1.2；95% CI：0.6，2.2 ; P = .63)。在调整潜在混杂因素时，CT-FFR 与 MACE 独立相关（HR，4.0；95% CI：1.5, 10.5；P = .01）。将 CT-FFR 作为预测因子添加到包括 CCTA 和临床数据的模型中提高了它们对 MACE 的预测值（P = .002），但不是全因死亡率（P = .67), 并且对 MACE 表现出良好的判别能力 (C-index, 0.71)。

结论

CT 血管造影得出的血流储备分数与经导管主动脉瓣置换术候选者的主要不良心脏事件相关，并提高了冠状动脉 CT 血管造影评估的预测价值。

© 北美放射学会，2021

本文提供在线补充材料。

另请参阅本期 Choe 的社论。