Validation of an electrocardiographic algorithm for the detection of cardiac amyloidosis. (3rd October 2022) - Schrutka, L; Seirer, B; Dusik, F; Rettl, R; Duca, F; Dalos, D; Dachs, T M; Binder, C; Badr-Eslam, R; Kastner, J; Hengstenberg, C; Stix, G; et al.
Abstract: Background: Despite new therapies, diagnosis of cardiac amyloidosis (CA) is often delayed. We recently developed a simple electrocardiographic (ECG) algorithm to suspect CA without the aid of advanced imaging modalities (Figure). Methods: The aim of this study was to validate the algorithms' usefulness in clinical practice. ECG readings from patients with CA, heart failure with preserved ejection fraction (HFpEF), and hypertrophic cardiomyopathy (HCMP) were analyzed in a blinded fashion. Results: 884 patients were included. Patients with pacemakers were excluded, leaving 827 ECGs (237 CA, 407 HFpEF, 183 HCMP) for final analysis. A characteristic pattern defined by the algorithm was visually perceptible in 165 ECGs (69.6%) of the amyloidosis patients vs. 114 (28%) of HFpEF vs. 22 (12.0%) of HCMP patients (p<0.001). The area under the curve (AUC) for the detection CA was 0.75 with a sensitivity of 69.6% and a specificity of 76.9% (Figure). Binary logistic regression analysis revealed that the presence of a distinctive pattern increased the probability of CA with an odds ratio of 7.66 (CI: 5.47–10.72; p<0.001). Conclusion: This easy-to-use ECG algorithm has proven helpful to suspect CA. Our tool may significantly improve the treatment of heart failure patients by identifying those with amyloidosis-related disease. Funding Acknowledgement: Type of funding sources: None.