ArticleESC heart failure2025
Novel three-dimensional ECG algorithm for reliable screening for cardiac amyloidosis.
Article in ESC heart failure, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Diagnostic Performance Evaluation of Clinical and Artificial Intelligence Risk Models in Patients Referred for Cardiac Amyloidosis Testing.Journal of the American Society of Echocardiography : official publication of the American Society of Echocardiography · 2026Article
- Early timepointEJNMMI research · 2026Article
- Novel three-dimensional ECG algorithm for reliable screening for cardiac amyloidosis.ESC heart failure · 2025Article
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
aimsCurrently, 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). METHODS AND
resultsWe 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.
conclusionsWe here describe a novel screening tool, which allows for reliable detection of cardiac amyloidosis.
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