对放大后的P波进行分析能够识别窦性心律期间的房颤患者。

Analysis of the amplified p-wave enables identification of patients with atrial fibrillation during sinus rhythm.

作者信息

Huang Taiyuan, Schurr Patrick, Muller-Edenborn Bjoern, Pilia Nicolas, Mayer Louisa, Eichenlaub Martin, Allgeier Juergen, Heidenreich Marie, Ahlgrim Christoph, Bohnen Marius, Lehrmann Heiko, Trenk Dietmar, Neumann Franz-Josef, Westermann Dirk, Arentz Thomas, Jadidi Amir

机构信息

Arrhythmia Division, Clinic for Cardiology and Angiology, University Heart Center Freiburg-Bad Krozingen, Faculty of Medicine, University of Freiburg, Freiburg im Breisgau, Germany.

出版信息

Front Cardiovasc Med. 2023 Feb 22;10:1095931. doi: 10.3389/fcvm.2023.1095931. eCollection 2023.

AIM

This study sought to develop and validate diagnostic models to identify individuals with atrial fibrillation (AF) using amplified sinus-p-wave analysis.

METHODS

A total of 1,492 patients (491 healthy controls, 499 with paroxysmal AF and 502 with persistent AF) underwent digital 12-lead-ECG recording during sinus rhythm. The patient cohort was divided into training and validation set in a 3:2 ratio. P-wave indices (PWI) including duration of standard p-wave (standard PWD; scale at 10 mm/mV, sweep speed at 25 mm/s) and amplified sinus-p-wave (APWD, scale at 60-120 mm/mV, sweep speed at 100 mm/s) and advanced inter-atrial block (aIAB) along with other clinical parameters were used to develop diagnostic models using logistic regression. Each model was developed from the training set and further tested in both training and validation sets for its diagnostic performance in identifying individuals with AF.

RESULTS

Compared to standard PWD (Reference model), which achieved an AUC of 0.637 and 0.632, for training and validation set, respectively, APWD (Basic model) importantly improved the accuracy to identify individuals with AF (AUC = 0.86 and 0.866). The PWI-based model combining APWD, aIAB and body surface area (BSA) further improved the diagnostic performance for AF (AUC = 0.892 and 0.885). The integrated model, which further combined left atrial diameter (LAD) with parameters of the PWI-based model, achieved optimal diagnostic performance (AUC = 0.916 and 0.902).

CONCLUSION

Analysis of amplified p-wave during sinus rhythm allows identification of individuals with atrial fibrillation.

目的

本研究旨在开发并验证使用放大窦性P波分析来识别房颤（AF）患者的诊断模型。

方法

共有1492例患者（491例健康对照、499例阵发性AF患者和502例持续性AF患者）在窦性心律期间接受了数字化12导联心电图记录。患者队列以3:2的比例分为训练集和验证集。使用包括标准P波持续时间（标准PWD；10mm/mV标度，25mm/s扫描速度）、放大窦性P波（APWD，60 - 120mm/mV标度，100mm/s扫描速度）和高级房内阻滞（aIAB）以及其他临床参数的P波指数（PWI），通过逻辑回归开发诊断模型。每个模型均从训练集开发，并在训练集和验证集中进一步测试其识别AF患者的诊断性能。

结果

与标准PWD（参考模型）相比，其在训练集和验证集中的AUC分别为0.637和0.632，APWD（基础模型）显著提高了识别AF患者的准确性（AUC = 0.86和0.866）。基于PWI的模型结合APWD、aIAB和体表面积（BSA）进一步提高了AF的诊断性能（AUC = 0.892和0.885）。进一步将左心房直径（LAD）与基于PWI的模型参数相结合而得到的综合模型实现了最佳诊断性能（AUC = 0.916和0.902）。