预测与低活动水平相关的快速心室率的心房颤动发作。

Predicting atrial fibrillation episodes with rapid ventricular rates associated with low levels of activity.

机构信息

Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA.

Department of Internal Medicine, Division of Cardiovascular Medicine, Cardiac Electrophysiology Services, 1500 East Medical Center Drive, 48109-5853, Ann Arbor, Michigan, USA.

出版信息

BMC Med Inform Decis Mak. 2021 Dec 28;21(1):364. doi: 10.1186/s12911-021-01723-3.

DOI:10.1186/s12911-021-01723-3

PMID:34963444

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8714444/

Abstract

BACKGROUND

Rapid and irregular ventricular rates (RVR) are an important consequence of atrial fibrillation (AF). Raw accelerometry data in combination with electrocardiogram (ECG) data have the potential to distinguish inappropriate from appropriate tachycardia in AF. This can allow for the development of a just-in-time intervention for clinical treatments of AF events. The objective of this study is to develop a machine learning algorithm that can distinguish episodes of AF with RVR that are associated with low levels of activity.

METHODS

This study involves 45 patients with persistent or paroxysmal AF. The ECG and accelerometer data were recorded continuously for up to 3 weeks. The prediction of AF episodes with RVR and low activity was achieved using a deterministic probabilistic finite-state automata (DPFA)-based approach. Rapid and irregular ventricular rate (RVR) is defined as having heart rates (HR) greater than 110 beats per minute (BPM) and high activity is defined as greater than 0.75 quantile of the activity level. The AF events were annotated using the FDA-cleared BeatLogic algorithm. Various time intervals prior to the events were used to determine the longest prediction intervals for predicting AF with RVR episodes associated with low levels of activity.

RESULTS

Among the 961 annotated AF events, 292 met the criterion for RVR episode. There were 176 and 116 episodes with low and high activity levels respectively. Out of the 961 AF episodes, 770 (80.1%) were used in the training data set and the remaining 191 intervals were held out for testing. The model was able to predict AF with RVR and low activity up to 4.5 min before the events. The mean prediction performance gradually decreased as the time to events increased. The overall Area under the ROC Curve (AUC) for the model lies within the range of 0.67-0.78.

CONCLUSION

The DPFA algorithm can predict AF with RVR associated with low levels of activity up to 4.5 min before the onset of the event. This would enable the development of just-in-time interventions that could reduce the morbidity and mortality associated with AF and other similar arrhythmias.

摘要

背景

快速且不规则的心室率（RVR）是心房颤动（AF）的重要后果。原始加速度计数据与心电图（ECG）数据相结合，有可能区分 AF 中的不适当与适当的心动过速。这可以为 AF 事件的临床治疗开发即时干预措施。本研究的目的是开发一种机器学习算法，能够区分与低活动水平相关的 RVR 相关 AF 发作。

方法

本研究涉及 45 名持续性或阵发性 AF 患者。连续记录 ECG 和加速度计数据长达 3 周。使用基于确定性概率有限状态自动机（DPFA）的方法预测 RVR 和低活动的 AF 发作。快速且不规则的心室率（RVR）定义为心率（HR）大于 110 次/分钟（BPM），高活动定义为大于活动水平的 0.75 分位数。使用 FDA 批准的 BeatLogic 算法对 AF 事件进行注释。使用各种时间间隔来确定最长的预测间隔，以预测与低活动水平相关的 RVR 发作的 AF。

结果

在 961 个注释的 AF 事件中，有 292 个符合 RVR 发作标准。低活动和高活动水平的事件分别为 176 和 116 个。在 961 个 AF 事件中，770 个（80.1%）用于训练数据集，其余 191 个间隔保留用于测试。该模型能够在事件发生前 4.5 分钟预测 RVR 和低活动水平的 AF。随着时间的推移，预测性能逐渐下降。该模型的整体 ROC 曲线下面积（AUC）在 0.67-0.78 之间。