Machine learning-derived electrocardiographic algorithm for the detection of cardiac amyloidosis
| Autor: | Christian Hengstenberg, Benjamin Seirer, Johannes Kastner, Daniel Dalos, Philip Anner, Diana Bonderman, R Badr-Eslam, Theresa-Marie Dachs, Günther Laufer, Christian Loewe, René Rettl, Christina Binder, Franz Duca, Guenter Stix, Asan Agibetov, Fabian Dusik, Georg Dorffner, Dietrich Beitzke, Lore Schrutka |
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| Rok vydání: | 2021 |
| Předmět: |
Heart Ventricles
Diastole Machine learning computer.software_genre Positive correlation Both ventricles Machine Learning Electrocardiography medicine Humans medicine.diagnostic_test business.industry Amyloidosis medicine.disease Ventricular activation medicine.anatomical_structure Cardiac amyloidosis Ventricle Heart failure Artificial intelligence Cardiology and Cardiovascular Medicine business Algorithm computer Algorithms |
| Zdroj: | Heart. 108:1137-1147 |
| ISSN: | 1468-201X 1355-6037 |
| Popis: | BackgroundDiagnosis of cardiac amyloidosis (CA) requires advanced imaging techniques. Typical surface ECG patterns have been described, but their diagnostic abilities are limited.ObjectiveThe aim was to perform a thorough electrophysiological characterisation of patients with CA and derive an easy-to-use tool for diagnosis.MethodsWe 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.FindingsAreas 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.InterpretationUsing 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. |
| Databáze: | OpenAIRE |
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