Novel 3D-ECG algorithms for reliable screening of cardiac amyloidosis

Abstract Background Cardiac amyloidosis is associated with high morbidity and mortality. Early detection is crucial to allow for specific therapy and improvement of patient’s prognosis. However, there is no established screening tool for cardiac amyloidosis, leading to a delay in diagnosis in the majority of patients. Aims We aimed to develop and validate a screening tool for cardiac amyloidosis based on structured evaluation of 3-dimensional electrocardiograms (ECGs). Methods We included patients from a vascular center with confirmed cardiac AL- or ATTR-amyloidosis as well as controls of patients with other cardiovascular diseases but without amyloidosis into two independent cohorts, a derivation and validation cohort. All patients received 3-dimensional ECGs and vector loops were categorized based on pre-defined patterns by two independent cardiologists. Consecutively, an AI algorithm was trained in the derivation cohort (n=66 amyloidosis cases, n=89 controls). This algorithm was then applied to the validation cohort (n=33 amyloidosis cases, n=67 controls). Overall accuracy of the algorithm for detection of amyloidosis was evaluated in both cohorts. Results Overall, 99 patients with amyloidosis and 156 controls were included (mean age: 75±24 years, 82% male). In the derivation cohort, the AI algorithm reached a sensitivity of 85%, a specificity of 89%, a positive predictive value of 91%, and a negative predictive value of 87%. Applying the algorithm on the independent validation cohort, a sensitivity of 79%, specificity of 82%, a positive predictive value of 61%, and a negative predictive value of 92% was reached. The overall accuracy of the algorithm for detection of cardiac amyloidosis was 85%. Conclusion We here describe a novel screening tool, which allows for reliable detection of cardiac amyloidosis.