Background Left ventricular (LV) subclinical remodeling is associated with adverse outcomes and indicates mechanisms of disease development. Standard metrics such as LV mass and volumes may not capture the full range of remodeling. Purpose To quantify the relationship between LV three-dimensional shape at MRI and incident cardiovascular events over 10 years. Materials and Methods In this retrospective study, 5098 participants from the Multi-Ethnic Study of Atherosclerosis who were free of clinical cardiovascular disease underwent cardiac MRI from 2000 to 2002. LV shape models were automatically generated using a machine learning workflow. Event-specific remodeling signatures were computed using partial least squares regression, and random survival forests were used to determine which features were most associated with incident heart failure (HF), coronary heart disease (CHD), and cardiovascular disease (CVD) events over a 10-year follow-up period. The discrimination improvement of adding LV shape to traditional cardiovascular risk factors, coronary artery calcium scores, and N-terminal pro-brain natriuretic peptide levels was assessed using the index of prediction accuracy and time-dependent area under the receiver operating characteristic curve (AUC). Kaplan-Meier survival curves were used to illustrate the ability of remodeling signatures to predict the end points. Results Overall, 4618 participants had sufficient three-dimensional MRI information to generate patient-specific LV models (mean age, 60.6 years ± 9.9 [SD]; 2540 women). Among these participants, 147 had HF, 317 had CHD, and 455 had CVD events. The addition of LV remodeling signatures to traditional cardiovascular risk factors improved the mean AUC for 10-year survival prediction and achieved better performance than LV mass and volumes; HF (AUC, 0.83 ± 0.01 and 0.81 ± 0.01, respectively; P < .05), CHD (AUC, 0.77 ± 0.01 and 0.75 ± 0.01, respectively; P < .05), and CVD (AUC, 0.78 ± 0.0 and 0.76 ± 0.0, respectively; P < .05). Kaplan-Meier analysis demonstrated that participants with high-risk HF remodeling signatures had a 10-year survival rate of 56% compared with 95% for those with low-risk scores. Conclusion Left ventricular event-specific remodeling signatures were more predictive of heart failure, coronary heart disease, and cardiovascular disease events over 10 years than standard mass and volume measures and enable an automatic personalized medicine approach to tracking remodeling. © RSNA, 2022 Online supplemental material is available for this article.

中文翻译：

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动脉粥样硬化的多种族研究：心脏 MRI 左心室形状与 10 年结果之间的关系。

背景 左心室 (LV) 亚临床重塑与不良后果相关，并指示疾病发展的机制。LV 质量和体积等标准指标可能无法捕捉到重塑的全部范围。目的 量化 MRI 上 LV 三维形状与 10 年心血管事件事件之间的关系。材料和方法 在这项回顾性研究中，5098 名来自动脉粥样硬化多种族研究且没有临床心血管疾病的参与者在 2000 年至 2002 年期间接受了心脏 MRI 检查。LV 形状模型是使用机器学习工作流程自动生成的。使用偏最小二乘回归计算事件特定的重塑特征，并使用随机生存森林来确定哪些特征与事件心力衰竭 (HF) 最相关，10 年随访期间的冠心病 (CHD) 和心血管疾病 (CVD) 事件。将 LV 形状添加到传统心血管危险因素、冠状动脉钙评分和 N 末端前脑利钠肽水平的辨别力改进使用预测准确性和接受者操作特征曲线下的时间依赖性面积 (AUC) 指标进行评估. Kaplan-Meier 生存曲线用于说明重塑签名预测终点的能力。结果 总体而言，4618 名参与者拥有足够的三维 MRI 信息来生成患者特定的 LV 模型（平均年龄，60.6 岁 ± 9.9 [SD]；2540 名女性）。在这些参与者中，147 人患有 HF，317 人患有 CHD，455 人患有 CVD 事件。将 LV 重塑特征添加到传统的心血管危险因素中，改善了 10 年生存预测的平均 AUC，并取得了比 LV 质量和体积更好的性能；HF（AUC，分别为 0.83 ± 0.01 和 0.81 ± 0.01；P < .05）、CHD（AUC，分别为 0.77 ± 0.01 和 0.75 ± 0.01；P < .05）和 CVD（AUC，0.78 ± 0.0 和 0.76分别为 ± 0.0；P < .05)。Kaplan-Meier 分析表明，具有高风险 HF 重塑特征的参与者的 10 年生存率为 56%，而具有低风险分数的参与者为 95%。结论 左心室事件特异性重塑特征更能预测心力衰竭、冠心病、和心血管疾病事件超过 10 年，而不是标准的质量和体积测量，并启用自动个性化医学方法来跟踪重塑。© RSNA, 2022 本文提供在线补充材料。