Novel three-dimensional ECG algorithm for reliable screening for cardiac amyloidosis

Currently, there is no established screening tool for cardiac amyloidosis, leading to a delay in diagnosis in the majority of patients. We aimed to develop and validate a non-invasive and easy to use tool that allows for screening of cardiac amyloidosis based on structured evaluation of three-dimensional electrocardiograms (ECGs).

We included patients with confirmed cardiac AL or ATTR amyloidosis and controls of patients with other cardiovascular diseases but without amyloidosis into two independent cohorts: a derivation and validation cohort. All patients received three-dimensional ECGs and vector loops were categorized based on predefined 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, 99 patients with amyloidosis and 156 controls were included (mean age: 69 ± 15 years, 79% 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.

We here describe a novel screening tool, which allows for reliable detection of cardiac amyloidosis.