Lillehei Heart Institute & Cardiovascular Division, Department of Medicine, University of Minnesota Medical School, Minneapolis' MN (L.Y.C.).
Centro de Telessaúde, Hospital das Clínicas, & Departamento de Clínica Médica, Faculdade de Medicina, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil (A.L.P.R.).
Circ Arrhythm Electrophysiol. 2022 Apr;15(4):e010435. doi: 10.1161/CIRCEP.121.010435. Epub 2022 Mar 25.
Atrial cardiomyopathy, characterized by abnormalities in atrial structure and function, is associated with increased risk of adverse cardiovascular and neurocognitive outcomes, independent of atrial fibrillation. There exists a critical unmet need for a clinical tool that is cost-effective, easy to use, and that can diagnose atrial cardiomyopathy. P wave parameters (PWPs) reflect underlying atrial structure, size, and electrical activation; alterations in these factors manifest as abnormalities in PWPs that can be readily ascertained from a standard 12-lead ECG and potentially be used to aid clinical decision-making. PWPs include P wave duration, interatrial block, P wave terminal force in V, P wave axis, P wave voltage, P wave area, and P wave dispersion. PWPs can be combined to yield an index (P wave index), such as the morphology-voltage-P-wave duration ECG risk score. Abnormal PWPs have been shown in population-based cohort studies to be independently associated with higher risks of atrial fibrillation, ischemic stroke, sudden cardiac death, and dementia. Additionally, PWPs, either individually or in combination (as a P wave index), have been reported to enhance prediction of atrial fibrillation or ischemic stroke. To facilitate translation of PWPs to routine clinical practice, additional work is needed to standardize measurement of PWPs (eg, via semiautomated or automated measurement), confirm their reliability and predictive value, leverage novel approaches (eg, wavelet analysis of P waves and machine learning algorithms), and finally, define the risk-benefit ratio of specific interventions in high-risk individuals. Our ultimate goal is to repurpose the ubiquitous 12-lead ECG to advance the study, diagnosis, and treatment of atrial cardiomyopathy, thus overcoming critical challenges in prevention of cardiovascular disease and dementia.
心房心肌病以心房结构和功能异常为特征，与不良心血管和神经认知结局风险增加相关，且独立于心房颤动。目前迫切需要一种经济有效的临床工具，该工具易于使用，能够诊断心房心肌病。P 波参数（PWPs）反映了心房的结构、大小和电激活；这些因素的改变表现为 PWPs 的异常，这些异常可以从标准的 12 导联心电图中轻易确定，并可能用于辅助临床决策。PWPs 包括 P 波持续时间、房间传导阻滞、V 波的 P 波终端力、P 波轴、P 波电压、P 波面积和 P 波离散度。PWPs 可以组合起来得到一个指数（P 波指数），例如形态-电压-P 波持续时间心电图风险评分。基于人群的队列研究表明，异常的 PWPs 与心房颤动、缺血性卒中和心源性猝死以及痴呆的风险增加独立相关。此外，PWPs 无论是单独存在还是组合存在（如 P 波指数），都已被报道可以增强对心房颤动或缺血性卒中的预测。为了将 PWPs 转化为常规临床实践，需要开展更多工作来标准化 PWPs 的测量（例如，通过半自动或自动测量），确认其可靠性和预测价值，利用新方法（例如，P 波的小波分析和机器学习算法），并最终定义特定干预措施在高危人群中的风险效益比。我们的最终目标是重新利用无处不在的 12 导联心电图来推进心房心肌病的研究、诊断和治疗，从而克服预防心血管疾病和痴呆的关键挑战。
