一级预防植入式心律转复除颤器受术者单形性与非单形性室性心律失常的竞争风险:全球电异质性和临床结果(GEHCO)研究

Larisa G Tereshchenko, Jonathan W Waks, Christine Tompkins, Albert J Rogers, Ashkan Ehdaie, Charles A Henrikson, Khidir Dalouk, Merritt Raitt, Shivangi Kewalramani, Michael W Kattan, Pasquale Santangeli, Bruce W Wilkoff, Samir R Kapadia, Sanjiv M Narayan, Sumeet S Chugh
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Cox models were adjusted for demographic characteristics, heart failure severity and treatment, device programming, and ECG metrics. Global electrical heterogeneity was measured by spatial QRS-T angle (QRSTa), spatial ventricular gradient elevation (SVGel), azimuth, magnitude (SVGmag), and sum absolute QRST integral (SAIQRST). We compared the out-of-sample performance of the lasso and elastic net for Cox proportional hazards and the Fine-Gray competing risk model. Results During a median follow-up of 4 years, 359 patients experienced their first sustained MMVT with appropriate ICD therapy, and 129 patients had their first PVT/VF with appropriate ICD shock. The risk of MMVT was associated with wider QRSTa (HR 1.16; 95%CI 1.01-1.34), larger SVGel (HR 1.17; 95%CI 1.05-1.30), and smaller SVGmag (HR 0.74; 95%CI 0.63-0.86) and SAIQRST (HR 0.84; 95%CI 0.71-0.99). The best-performing 3-year competing risk Fine-Gray model for MMVT (ROC(t)AUC 0.728; 95%CI 0.668-0.788) identified high-risk (> 50%) patients with 75% sensitivity, 65% specificity, and PVT/VF prediction model had ROC(t)AUC 0.915 (95%CI 0.868-0.962), both satisfactory calibration. 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引用次数: 0

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背景和目的 单形室性心动过速(MMVT)消融术已被证明可降低休克频率并提高存活率。我们旨在比较 MMVT 和多形性室速(PVT)/室颤(VF)的特异性病因风险因素,并建立预测模型。方法 该多中心回顾性队列研究纳入了 2,668 名患者(年龄为 63.1±13.0 岁;23% 为女性;78% 为白人;43% 为非缺血性心肌病,左室射血分数为 28.2±11.1%)。Cox模型对人口统计学特征、心衰严重程度和治疗、设备编程和心电图指标进行了调整。全局电异质性通过空间 QRS-T 角 (QRSTa)、空间心室阶差抬高 (SVGel)、方位角、幅度 (SVGmag) 和绝对 QRST 积分总和 (SAIQRST) 进行测量。我们比较了套索和弹性网在 Cox 比例危险模型和 Fine-Gray 竞争风险模型中的样本外性能。结果 在中位随访 4 年期间,359 名患者在接受适当的 ICD 治疗后首次出现持续 MMVT,129 名患者在接受适当的 ICD 电击后首次出现 PVT/VF。发生 MMVT 的风险与较宽的 QRSTa(HR 1.16;95%CI 1.01-1.34)、较大的 SVGel(HR 1.17;95%CI 1.05-1.30)、较小的 SVGmag(HR 0.74;95%CI 0.63-0.86)和 SAIQRST(HR 0.84;95%CI 0.71-0.99)相关。表现最佳的 MMVT 3 年竞争风险 Fine-Gray 模型(ROC(t)AUC 0.728;95%CI 0.668-0.788)可识别高风险(> 50%)患者,灵敏度为 75%,特异度为 65%;PVT/VF 预测模型的 ROC(t)AUC 为 0.915(95%CI 0.868-0.962),校准结果均令人满意。结论 我们开发并验证了预测 MMVT 或 PVT/VF 竞争风险的模型,可为程序规划和未来预防性 VT 消融的 RCT 提供参考。
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Competing Risks for Monomorphic versus Non-Monomorphic Ventricular Arrhythmias in Primary Prevention Implantable Cardioverter Defibrillator Recipients: Global Electrical Heterogeneity and Clinical Outcomes (GEHCO) Study
Background and Aims Ablation of monomorphic ventricular tachycardia (MMVT) has been shown to reduce shock frequency and improve survival. We aimed to compare cause-specific risk factors of MMVT and polymorphic ventricular tachycardia (PVT)/ventricular fibrillation (VF) and to develop predictive models. Methods The multicenter retrospective cohort study included 2,668 patients (age 63.1±13.0 y; 23% female; 78% white; 43% nonischemic cardiomyopathy, left ventricular ejection fraction 28.2±11.1%). Cox models were adjusted for demographic characteristics, heart failure severity and treatment, device programming, and ECG metrics. Global electrical heterogeneity was measured by spatial QRS-T angle (QRSTa), spatial ventricular gradient elevation (SVGel), azimuth, magnitude (SVGmag), and sum absolute QRST integral (SAIQRST). We compared the out-of-sample performance of the lasso and elastic net for Cox proportional hazards and the Fine-Gray competing risk model. Results During a median follow-up of 4 years, 359 patients experienced their first sustained MMVT with appropriate ICD therapy, and 129 patients had their first PVT/VF with appropriate ICD shock. The risk of MMVT was associated with wider QRSTa (HR 1.16; 95%CI 1.01-1.34), larger SVGel (HR 1.17; 95%CI 1.05-1.30), and smaller SVGmag (HR 0.74; 95%CI 0.63-0.86) and SAIQRST (HR 0.84; 95%CI 0.71-0.99). The best-performing 3-year competing risk Fine-Gray model for MMVT (ROC(t)AUC 0.728; 95%CI 0.668-0.788) identified high-risk (> 50%) patients with 75% sensitivity, 65% specificity, and PVT/VF prediction model had ROC(t)AUC 0.915 (95%CI 0.868-0.962), both satisfactory calibration. Conclusion We developed and validated models to predict the competing risks of MMVT or PVT/VF that could inform procedural planning and future RCTs of prophylactic VT ablation.
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