Affordable Access

deepdyve-link
Publisher Website

Machine learning-derived electrocardiographic algorithm for the detection of cardiac amyloidosis.

Authors
  • Schrutka, Lore1
  • Anner, Philip1, 2
  • Agibetov, Asan2
  • Seirer, Benjamin1
  • Dusik, Fabian1
  • Rettl, René1
  • Duca, Franz1
  • Dalos, Daniel1
  • Dachs, Theresa-Marie1
  • Binder, Christina1
  • Badr-Eslam, Roza1
  • Kastner, Johannes1
  • Beitzke, Dietrich3
  • Loewe, Christian3
  • Hengstenberg, Christian1
  • Laufer, Günther4
  • Stix, Guenter1
  • Dorffner, Georg2
  • Bonderman, Diana5, 6
  • 1 Department of Internal Medicine II, Division of Cardiology, Medical University of Vienna, Vienna, Austria. , (Austria)
  • 2 Center for Medical Statistics, Informatics and Intelligent Systems, Institute of Artificial Intelligence and Decision Support, Medical University of Vienna, Vienna, Austria. , (Austria)
  • 3 Department of Biomedical Imaging and Image-guided Therapy, Division of Cardiovascular and Interventional Radiology, Medical University of Vienna, Vienna, Austria. , (Austria)
  • 4 Department of Cardiac Surgery, Medical University of Vienna, Vienna, Austria. , (Austria)
  • 5 Department of Internal Medicine II, Division of Cardiology, Medical University of Vienna, Vienna, Austria [email protected] , (Austria)
  • 6 Department of Internal Medicine V, Division of Cardiology, Clinic Favoriten, Vienna, Austria. , (Austria)
Type
Published Article
Journal
Heart
Publisher
BMJ
Publication Date
Jun 24, 2022
Volume
108
Issue
14
Pages
1137–1147
Identifiers
DOI: 10.1136/heartjnl-2021-319846
PMID: 34716183
Source
Medline
Keywords
Language
English
License
Unknown

Abstract

Diagnosis of cardiac amyloidosis (CA) requires advanced imaging techniques. Typical surface ECG patterns have been described, but their diagnostic abilities are limited. The aim was to perform a thorough electrophysiological characterisation of patients with CA and derive an easy-to-use tool for diagnosis. We applied electrocardiographic imaging (ECGI) to acquire electroanatomical maps in patients with CA and controls. A machine learning approach was then used to decipher the complex data sets obtained and generate a surface ECG-based diagnostic tool. Areas of low voltage were localised in the basal inferior regions of both ventricles and the remaining right ventricular segments in CA. The earliest epicardial breakthrough of myocardial activation was visualised on the right ventricle. Potential maps revealed an accelerated and diffuse propagation pattern. We correlated the results from ECGI with 12-lead ECG recordings. Ventricular activation correlated best with R-peak timing in leads V1-V3. Epicardial voltage showed a strong positive correlation with R-peak amplitude in the inferior leads II, III and aVF. Respective surface ECG leads showed two characteristic patterns. Ten blinded cardiologists were asked to identify patients with CA by analysing 12-lead ECGs before and after training on the defined ECG patterns. Training led to significant improvements in the detection rate of CA, with an area under the curve of 0.69 before and 0.97 after training. Using a machine learning approach, an ECG-based tool was developed from detailed electroanatomical mapping of patients with CA. The ECG algorithm is simple and has proven helpful to suspect CA without the aid of advanced imaging modalities. © Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.

Report this publication

Statistics

Seen <100 times