Background: Diagnosis of cardiac amyloidosis (CA) requires advanced imaging techniques. Typical surface ECG patterns have been described, but their diagnostic abilities are limited. Objectives: The aim was to perform a thorough electrophysiological characterization in CA patients and derive an easy-to-use tool for diagnosis. Methods: We applied electrocardiographic imaging (ECGI) to acquire electroanatomical maps in CA patients and controls. A machine learning approach was then utilized to decipher the complex data sets obtained and generate a surface ECG-based diagnostic tool. Findings: Areas of low-voltage were localized in the basal inferior regions of both ventricles and the remaining right ventricular segments in CA. The earliest epicardial breakthrough of myocardial activation was visualized 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 to V3. Epicardial voltage showed a strong positive correlation with R-peak amplitude in the inferior leads II, III, aVF. Respective surface ECG leads showed two characteristic patterns. Ten blinded cardiologists were asked to identify CA patients by analyzing 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 AUC 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 CA patients. The ECG algorithm is simple and has proven helpful to suspect CA without the aid of advanced imaging modalities.. Funding Information: This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Declaration of Interests: None to declare. Ethics Approval Statement: The study was part of a registry approved by the local ethics committee (EK #796/2010) and conducted according to good clinical practice as outlined in the declaration of Helsinki. All patients gave written informed consent.
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