Wroclaw Medical University, Wroclaw, Poland.
Baker Heart and Diabetes Institute, Melbourne, Australia; Menzies Institute for Medical Research, University of Tasmania, Hobart, Australia.
JACC Cardiovasc Imaging. 2021 Jan;14(1):131-144. doi: 10.1016/j.jcmg.2020.07.040.
This study sought to identify the factors associated with incident atrial fibrillation (AF) in a well-characterized heart failure with preserved ejection fraction (HFpEF) population, with special focus on left atrial (LA) strain.
AF is associated with HFpEF, with adverse consequences. Effective risk evaluation might allow the initiation of protective strategies.
Clinical evaluation and echocardiography, including measurements of peak atrial longitudinal strain (PALS), peak atrial contraction strain (PACS), and LA volume index (LAVI), were obtained in 170 patients with symptomatic HFpEF (mean age, 65 ± 8 years), free of baseline AF. AF was identified by standard 12-lead electrocardiogram, review of relevant medical records (including Holter documentation), and surveillance with a portable single-lead electrocardiogram device over 2 weeks. Results were validated in the 103 patients with HFpEF from the Karolinska-Rennes (KaRen) study.
Over a median follow-up of 49 months, incident AF was identified in 39 patients (23%). Patients who developed AF were older; had higher clinical risk scores, brain natriuretic peptide, creatinine, LAVI, and LV mass; lower LA strain and exercise capacity; and more impaired LV diastolic function. PACS, PALS, and LAVI were the most predictive parameters for AF (area under receiver-operating characteristic curve: 0.76 for PACS, 0.71 for PALS, and 0.72 for LAVI). Nested Cox regression models showed that the predictive value of PACS and PALS was independent from and incremental to clinical data, LAVI, and E/e' ratio. Classification and regression trees analysis identified PACS ≤12.7%, PALS ≤29.4%, and LAVI >34.3 ml/m as discriminatory nodes for AF, with a 33-fold greater hazard of AF (p < 0.001) in patients categorized as high risk. The classification and regression trees algorithm discriminated high and low AF risk in the validation cohort.
PACS and PALS provide incremental predictive information about incident AF in HFpEF. The inclusion of these LA strain components to the diagnostic algorithm may help guide screening and further monitoring for AF risk in this population.
本研究旨在确定特征明确的心衰射血分数保留型（HFpEF）人群中与新发心房颤动（AF）相关的因素，尤其关注左心房（LA）应变。
AF 与 HFpEF 相关，并伴有不良后果。有效的风险评估可能有助于启动保护策略。
对 170 例有症状的 HFpEF 患者（平均年龄 65±8 岁）进行临床评估和超声心动图检查，包括测量峰值心房纵向应变（PALS）、峰值心房收缩应变（PACS）和左心房容积指数（LAVI）。这些患者在基线时无 AF。通过标准 12 导联心电图、相关病历回顾（包括动态心电图记录）和使用便携式单导联心电图设备监测 2 周来确定 AF。研究结果在 Karolinska-Rennes（KaRen）研究中的 103 例 HFpEF 患者中得到了验证。
中位随访 49 个月期间，39 例（23%）患者新发 AF。发生 AF 的患者年龄更大；临床风险评分、脑利钠肽、肌酐、LAVI 和 LV 质量更高；LA 应变和运动能力更低；LV 舒张功能受损更严重。PACS、PALS 和 LAVI 是 AF 最具预测性的参数（接受者操作特征曲线下面积：PACS 为 0.76，PALS 为 0.71，LAVI 为 0.72）。嵌套 Cox 回归模型显示，PACS 和 PALS 的预测价值独立于临床数据、LAVI 和 E/e' 比值，并可进一步预测。分类回归树分析确定 PACS≤12.7%、PALS≤29.4%和 LAVI>34.3ml/m 为 AF 的判别节点，高危患者发生 AF 的风险增加 33 倍（p<0.001）。分类回归树算法在验证队列中区分了 AF 的高风险和低风险。
PACS 和 PALS 为 HFpEF 患者新发 AF 提供了额外的预测信息。将这些 LA 应变成分纳入诊断算法可能有助于指导该人群的 AF 风险筛查和进一步监测。
