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

TL;DR

A new 3D ECG algorithm was developed and validated to screen for cardiac amyloidosis, a condition often diagnosed late, with high diagnostic accuracy.

Contribution

A novel non-invasive 3D ECG-based AI algorithm for reliable screening of cardiac amyloidosis is introduced and validated.

Findings

The AI algorithm achieved 85% sensitivity and 89% specificity in the derivation cohort.

In the validation cohort, the algorithm showed 79% sensitivity and 82% specificity for detecting cardiac amyloidosis.

Abstract

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.

The present manuscript describes the derivation and validation of an algorithm for screening of cardiac amyloidosis using 3‐dimensional ECG. The algorithm is based on a ECG vector loop, acquired over the duration of 15 seconds using 4 electrodes. With this easy to perform method, we describe a high diagnostic accuracy for the detection of cardiac amyloidosis in a cohort of patients with severe heart failure.

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