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

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Heart. 2021 Oct 29:heartjnl-2021-319846. doi: 10.1136/heartjnl-2021-319846. Online forward of print.
ABSTRACT
BACKGROUND: Diagnosis of cardiac amyloidosis (CA) requires superior imaging methods. Typical floor ECG patterns have been described, however their diagnostic skills are restricted.
OBJECTIVE: The purpose was to carry out a radical electrophysiological characterisation of sufferers with CA and derive an easy-to-use software for analysis.
METHODS: We utilized electrocardiographic imaging (ECGI) to amass electroanatomical maps in sufferers with CA and controls. A machine studying method was then used to decipher the complicated knowledge units obtained and generate a floor ECG-based diagnostic software.
FINDINGS: Areas of low voltage have been localised in the basal inferior areas of each ventricles and the remaining proper ventricular segments in CA. The earliest epicardial breakthrough of myocardial activation was visualised on the proper ventricle. Potential maps revealed an accelerated and diffuse propagation sample. We correlated the outcomes from ECGI with 12-lead ECG recordings. Ventricular activation correlated greatest with R-peak timing in leads V1-V3. Epicardial voltage confirmed a robust optimistic correlation with R-peak amplitude in the inferior leads II, III and aVF. Respective floor ECG leads confirmed two attribute patterns. Ten blinded cardiologists have been requested to determine sufferers with CA by analysing 12-lead ECGs earlier than and after coaching on the outlined ECG patterns. Training led to important enhancements in the detection charge of CA, with an space below the curve of 0.69 earlier than and 0.97 after coaching.
INTERPRETATION: Using a machine studying method, an ECG-based software was developed from detailed electroanatomical mapping of sufferers with CA. The ECG algorithm is easy and has confirmed useful to suspect CA with out the assist of superior imaging modalities.
PMID:34716183 | DOI:10.1136/heartjnl-2021-319846

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