PDF(Pubmed)
Abstract：
UNASSIGNED: The association of electrocardiographic (ECG) markers of atrial cardiomyopathy with heart failure (HF) and its subtypes is unclear.
UNASSIGNED: This analysis included 6,754 participants free of clinical cardiovascular disease (CVD), including atrial fibrillation (AF), from the Multi-Ethnic Study of Atherosclerosis. Five ECG markers of atrial cardiomyopathy (P-wave terminal force in V1 [PTFV1], deep-terminal negativity in V1 [DTNV1], P-wave duration [PWD], P-wave axis [PWA], advanced intra-atrial block [aIAB]) were derived from digitally recorded electrocardiograms. Incident HF events through 2018 were centrally adjudicated. An ejection fraction (EF) of 50% at the time of HF was used to classify HF as HF with reduced EF (HFrEF), HF with preserved EF (HFpEF), or unclassified HF. Cox proportional hazard models were used to examine the associations of markers of atrial cardiomyopathy with HF. The Lunn-McNeil method was used to compare the associations in HFrEF vs. HFpEF.
UNASSIGNED: 413 HF events occurred over a median follow-up of 16 years. In adjusted models, abnormal PTFV1 (HR (95%CI): 1.56(1.15-2.13), abnormal PWA (HR (95%CI):1.60(1.16-2.22), aIAB (HR (95%CI):2.62(1.47-4.69), DTNPV1 (HR (95%CI): 2.99(1.63-7.33), and abnormal PWD (HR (95%CI): 1.33(1.02-1.73), were associated with increased HF risk. These associations persisted after further adjustments for intercurrent AF events. No significant differences in the strength of association of each ECG predictor with HFrEF and HFpEF were noted.
UNASSIGNED: Atrial cardiomyopathy defined by ECG markers is associated with HF, with no differences in the strength of association between HFrEF and HFpEF. Markers of atrial Cardiomyopathy may help identify individuals at risk of developing HF.
UNASSIGNED: This analysis included 6,754 participants free of clinical cardiovascular disease (CVD), including atrial fibrillation (AF), from the Multi-Ethnic Study of Atherosclerosis. Five ECG markers of atrial cardiomyopathy (P-wave terminal force in V1 [PTFV1], deep-terminal negativity in V1 [DTNV1], P-wave duration [PWD], P-wave axis [PWA], advanced intra-atrial block [aIAB]) were derived from digitally recorded electrocardiograms. Incident HF events through 2018 were centrally adjudicated. An ejection fraction (EF) of 50% at the time of HF was used to classify HF as HF with reduced EF (HFrEF), HF with preserved EF (HFpEF), or unclassified HF. Cox proportional hazard models were used to examine the associations of markers of atrial cardiomyopathy with HF. The Lunn-McNeil method was used to compare the associations in HFrEF vs. HFpEF.
UNASSIGNED: 413 HF events occurred over a median follow-up of 16 years. In adjusted models, abnormal PTFV1 (HR (95%CI): 1.56(1.15-2.13), abnormal PWA (HR (95%CI):1.60(1.16-2.22), aIAB (HR (95%CI):2.62(1.47-4.69), DTNPV1 (HR (95%CI): 2.99(1.63-7.33), and abnormal PWD (HR (95%CI): 1.33(1.02-1.73), were associated with increased HF risk. These associations persisted after further adjustments for intercurrent AF events. No significant differences in the strength of association of each ECG predictor with HFrEF and HFpEF were noted.
UNASSIGNED: Atrial cardiomyopathy defined by ECG markers is associated with HF, with no differences in the strength of association between HFrEF and HFpEF. Markers of atrial Cardiomyopathy may help identify individuals at risk of developing HF.
摘要：
■心房心肌病的心电图（ECG）标志物与心力衰竭（HF）及其亚型之间的关系尚不清楚。
■这项分析包括6,754名没有临床心血管疾病（CVD）的参与者，包括心房颤动(AF),动脉粥样硬化的多民族研究。心房心肌病的五个ECG标志物（V1[PTFV1]中的P波终末力，V1[DTNV1]中的深端消极性，P波持续时间[PWD],P波轴[PWA]，晚期心房内阻滞[aIAB]）来自数字记录的心电图。对2018年的HF事件进行了集中裁决。使用HF时50％的射血分数（EF）将HF分类为EF降低的HF（HFrEF）。HF与保存的EF（HFpEF），或未分类的HF。Cox比例风险模型用于检查心房心肌病标志物与HF的关联。Lunn-McNeil方法用于比较HFrEF与HFpEF.
■在16年的中位随访中发生了413例HF事件。在调整后的模型中，异常PTFV1（HR（95CI）：1.56（1.15-2.13），异常PWA（HR（95CI）：1.60（1.16-2.22），aIAB(HR(95CI):2.62(1.47-4.69),DTNPV1（HR（95CI）：2.99（1.63-7.33），和异常PWD（HR（95CI）：1.33（1.02-1.73），与HF风险增加有关。这些关联在进一步调整并发AF事件后仍然存在。每个ECG预测因子与HFrEF和HFpEF的关联强度没有显着差异。
■心电图标记物定义的心房心肌病与HF相关，HFrEF和HFpEF之间的关联强度没有差异。心房心肌病的标志物可能有助于识别有发生HF风险的个体。
■这项分析包括6,754名没有临床心血管疾病（CVD）的参与者，包括心房颤动(AF),动脉粥样硬化的多民族研究。心房心肌病的五个ECG标志物（V1[PTFV1]中的P波终末力，V1[DTNV1]中的深端消极性，P波持续时间[PWD],P波轴[PWA]，晚期心房内阻滞[aIAB]）来自数字记录的心电图。对2018年的HF事件进行了集中裁决。使用HF时50％的射血分数（EF）将HF分类为EF降低的HF（HFrEF）。HF与保存的EF（HFpEF），或未分类的HF。Cox比例风险模型用于检查心房心肌病标志物与HF的关联。Lunn-McNeil方法用于比较HFrEF与HFpEF.
■在16年的中位随访中发生了413例HF事件。在调整后的模型中，异常PTFV1（HR（95CI）：1.56（1.15-2.13），异常PWA（HR（95CI）：1.60（1.16-2.22），aIAB(HR(95CI):2.62(1.47-4.69),DTNPV1（HR（95CI）：2.99（1.63-7.33），和异常PWD（HR（95CI）：1.33（1.02-1.73），与HF风险增加有关。这些关联在进一步调整并发AF事件后仍然存在。每个ECG预测因子与HFrEF和HFpEF的关联强度没有显着差异。
■心电图标记物定义的心房心肌病与HF相关，HFrEF和HFpEF之间的关联强度没有差异。心房心肌病的标志物可能有助于识别有发生HF风险的个体。
