RT Journal Article
SR Electronic
T1 Machine learning-derived electrocardiographic algorithm for the detection of cardiac amyloidosis
JF Heart
JO Heart
FD BMJ Publishing Group Ltd and British Cardiovascular Society
SP 1137
OP 1147
DO 10.1136/heartjnl-2021-319846
VO 108
IS 14
A1 Lore Schrutka
A1 Philip Anner
A1 Asan Agibetov
A1 Benjamin Seirer
A1 Fabian Dusik
A1 René Rettl
A1 Franz Duca
A1 Daniel Dalos
A1 Theresa-Marie Dachs
A1 Christina Binder
A1 Roza Badr-Eslam
A1 Johannes Kastner
A1 Dietrich Beitzke
A1 Christian Loewe
A1 Christian Hengstenberg
A1 Günther Laufer
A1 Guenter Stix
A1 Georg Dorffner
A1 Diana Bonderman
YR 2022
UL http://heart.bmj.com/content/108/14/1137.abstract
AB Background Diagnosis of cardiac amyloidosis (CA) requires advanced imaging techniques. Typical surface ECG patterns have been described, but their diagnostic abilities are limited.Objective The aim was to perform a thorough electrophysiological characterisation of patients with CA and derive an easy-to-use tool for diagnosis.Methods 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.Findings 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.Interpretation 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.All data relevant to the study are included in the article or uploaded as supplemental information.