Background Diagnosis of cardiac amyloidosis (CA) requires advanced imaging techniques. Typical surface ECG patterns have been described, but their diagnostic abilities are limited. Objective The aim was to perform a thorough electrophysiological characterisation of patients with CA and derive an easy-to-use tool for diagnosis. Methods We applied electrocardiographic imaging (ECGI) to acquire electroanatomical maps in patients with CA and controls. A machine learning approach was then used to decipher the complex data sets obtained and generate a surface ECG-based diagnostic tool. Findings Areas of low voltage were localised in the basal inferior regions of both ventricles and the remaining right ventricular segments in CA. The earliest epicardial breakthrough of myocardial activation was visualised on the right ventricle. Potential maps revealed an accelerated and diffuse propagation pattern. We correlated the results from ECGI with 12-lead ECG recordings. Ventricular activation correlated best with R-peak timing in leads V1–V3. Epicardial voltage showed a strong positive correlation with R-peak amplitude in the inferior leads II, III and aVF. Respective surface ECG leads showed two characteristic patterns. Ten blinded cardiologists were asked to identify patients with CA by analysing 12-lead ECGs before and after training on the defined ECG patterns. Training led to significant improvements in the detection rate of CA, with an area under the curve of 0.69 before and 0.97 after training. Interpretation Using a machine learning approach, an ECG-based tool was developed from detailed electroanatomical mapping of patients with CA. The ECG algorithm is simple and has proven helpful to suspect CA without the aid of advanced imaging modalities. All data relevant to the study are included in the article or uploaded as supplemental information.

中文翻译：

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机器学习衍生的心电图算法检测心脏淀粉样变性

背景 心脏淀粉样变性 (CA) 的诊断需要先进的成像技术。已经描述了典型的表面心电图模式，但它们的诊断能力有限。目的 目的是对 CA 患者进行全面的电生理表征，并获得一种易于使用的诊断工具。方法 我们应用心电图成像 (ECGI) 来获取 CA 患者和对照组的电解剖图。然后使用机器学习方法来破译获得的复杂数据集并生成基于表面 ECG 的诊断工具。结果 低电压区域位于 CA 中两个心室的基底下部区域和剩余的右心室段。心肌激活的最早的心外膜突破在右心室可视化。潜在地图显示了一种加速和扩散的传播模式。我们将 ECGI 的结果与 12 导联 ECG 记录相关联。心室激活与 V1-V3 导联的 R 峰时间相关性最好。心外膜电压与下壁导联 II、III 和 aVF 的 R 峰幅度呈强正相关。相应的表面心电图导联显示出两种特征模式。十位失明的心脏病专家被要求通过在定义的心电图模式训练前后分析 12 导联心电图来识别 CA 患者。训练导致CA的检测率显着提高，曲线下面积在训练前为0.69，训练后为0.97。解释 使用机器学习方法，根据 CA 患者的详细电解剖图开发了一种基于 ECG 的工具。ECG 算法很简单，并且已被证明有助于在没有高级成像模式的帮助下怀疑 CA。所有与研究相关的数据都包含在文章中或作为补充信息上传。