Deep-Learning Models for the Echocardiographic Assessment of Diastolic Dysfunction
Deep-Learning Models for the Echocardiographic Assessment of Diastolic Dysfunction
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DOI：
10.1016/j.jcmg.2021.04.010
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发表时间：
2021-10-04
影响因子：
14
通讯作者：
Sengupta, Partho P.
中科院分区：
文献类型：
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作者：
Pandey, Ambarish;Kagiyama, Nobuyuki;Sengupta, Partho P.
作者探索了一种深度神经网络（DeepNN）模型，该模型整合了多维超声心动图数据，以识别射血分数保留的心力衰竭（HFpEF）的不同患者亚组。背景用于HFpEF舒张功能障碍严重程度表型的临床算法仍然不精确。方法作者开发了一种DeepNN模型，以预测衍生队列中的高风险和低风险表型组。（n = 1 242）。首先在2个外部队列中验证模型性能，以确定左心室充盈压升高（n = 84），并评估其在不同程度收缩和舒张功能障碍患者中的预后价值（n = 219）。在3个国家心脏、肺和血液研究所资助的HFpEF试验中，通过评估表型组与不良临床结局的关系，进一步验证了模型的临床意义（TOPCAT [醛固酮拮抗剂治疗成人心力衰竭和保留的心功能]试验，n = 518），心脏生物标志物，和运动参数（NEAT-HFpEF [硝酸盐对射血分数保留的心力衰竭患者活动耐量的影响]和RELAX-HF [评估西地那非改善舒张性心力衰竭患者健康结局和运动能力的有效性]结果DeepNN模型显示出比2016年美国超声心动图学会指南分级更高的受试者工作特征曲线下面积，用于预测左心室充盈压升高（0.88 vs. 0.67; p = 0.01）。高风险（相对于低风险）表型组显示心力衰竭住院和/或死亡的发生率更高，即使在调整了整体左心室和心房纵向应变后也是如此（风险比[HR]：3.96; 95%置信区间[CI]：1.24至12.67; p = 0.021）。同样，在TOPCAT队列中，高风险（与低风险）表型组显示心力衰竭住院或心源性死亡的发生率较高（HR：1.92; 95% CI：1.16至3.22; p = 0.01），螺内酯治疗的无事件生存率较高（HR：0.65; 95% CI：0.46至0.90; p = 0.01）。在合并的RELAX-HF/NEAT-HFpEF队列中，（与低风险）表型组的慢性心肌损伤负担更高（p < 0.001），神经激素激活（p < 0.001），和较低的运动能力（p = 0.001）结论：这种公开可用的DeepNN分类器可以表征舒张功能障碍的严重程度，并识别患有HFpEF的特定亚组患者，升高的左心室充盈压、心肌损伤和应激的生物标志物、不良事件以及更可能对螺内酯有反应的患者。(C)2021年由美国心脏病学会基金会。
OBJECTIVES The authors explored a deep neural network (DeepNN) model that integrates multidimensional echo cardiographic data to identify distinct patient subgroups with heart failure with preserved ejection fraction (HFpEF).BACKGROUND The clinical algorithms for phenotyping the severity of diastolic dysfunction in HFpEF remain imprecise.METHODS The authors developed a DeepNN model to predict high-and low-risk phenogroups in a derivation cohort (n =1,242). Model performance was first validated in 2 external cohorts to identify elevated left ventricular filling pressure (n = 84) and assess its prognostic value (n = 219) in patients with varying degrees of systolic and diastolic dysfunction. In 3 National Heart, Lung, and Blood Institute-funded HFpEF trials, the clinical significance of the model was further validated by assessing the relationships of the phenogroups with adverse clinical outcomes (TOPCAT [Aldosterone Antagonist Therapy for Adults With Heart Failure and Preserved Systolic Function] trial, n = 518), cardiac biomarkers, and exercise parameters (NEAT-HFpEF [Nitrate's Effect on Activity Tolerance in Heart Failure With Preserved Ejection Fraction] and RELAX-HF [Evaluating the Effectiveness of Sildenafil at Improving Health Outcomes and Exercise Ability in People With Diastolic Heart Failure] pooled cohort, n = 346).RESULTS The DeepNN model showed higher area under the receiver-operating characteristic curve than 2016 American Society of Echocardiography guideline grades for predicting elevated left ventricular filling pressure (0.88 vs. 0.67; p = 0.01). The high-risk (vs. low-risk) phenogroup showed higher rates of heart failure hospitalization and/or death, even after adjusting for global left ventricular and atrial longitudinal strain (hazard ratio [HR]: 3.96; 95% confidence interval [CI]: 1.24 to 12.67; p = 0.021). Similarly, in the TOPCAT cohort, the high-risk (vs. low-risk) phenogroup showed higher rates of heart failure hospitalization or cardiac death (HR: 1.92; 95% CI: 1.16 to 3.22; p = 0.01) and higher event-free survival with spironolactone therapy (HR: 0.65; 95% CI: 0.46 to 0.90; p = 0.01). In the pooled RELAX-HF/NEAT-HFpEF cohort, the high-risk (vs. low-risk) phenogroup had a higher burden of chronic myocardial injury (p < 0.001), neurohormonal activation (p < 0.001), and lower exercise capacity (p = 0.001).CONCLUSIONS This publicly available DeepNN classifier can characterize the severity of diastolic dysfunction and identify a specific subgroup of patients with HFpEF who have elevated left ventricular filling pressures, biomarkers of myocardial injury and stress, and adverse events and those who are more likely to respond to spironolactone. (C) 2021 by the American College of Cardiology Foundation.