Key result
Unsupervised machine learning cluster analysis found that a PVC configuration of V1S plus V2S ≤ 9.25 predicted a left ventricular outflow tract origin with 95.4% sensitivity and 97.5% specificity.
Why the study?
Current inferences about the site of origin of premature ventricular complexes from the surface ECG have not been subjected to newer data analytic techniques that identify signals not recognized by visual inspection.
Can an unsupervised machine learning approach applied to 12-lead ECG configurations accurately identify the site of origin of premature ventricular complexes?
Observational (n=338)
No
Can an unsupervised machine learning approach applied to 12-lead ECG configurations accurately identify the site of origin of premature ventricular complexes?
Unsupervised machine learning analysis of 12-lead ECGs identified a simple, highly accurate rule (V1S + V2S ≤ 9.25) to localize PVC origin to the left ventricular outflow tract.
BACKGROUND: Current inferences about the site of origin (SOO) of premature ventricular complexes (PVC) from the surface ECG have not been subjected to newer data analytic techniques that identify signals that are not recognized by visual inspection. AIMS: The objective of this study was to apply data analytics to PVC characteristics. METHODS: PVCs from 12-lead ECGs of a consecutive series of 338 individuals were examined by unsupervised machine learning cluster analysis, and indexes were compared to a composite criterion for SOO. RESULTS: Data analytics found that V1S plus V2S ≤ 9.25 of the PVC had a LVOT origin (sensitivity 95.4%; specificity 97.5%). V1R + V2R + V3R > 15.0 (a RBBB configuration) likely had a LVOT origin. PVCs with V1S plus V2S > 12.75 (LBBB configuration) likely had a RVOT origin. PVC with V1S plus V2S > 14.25 (LBBB configuration) and all inferior leads positive likely had a RVOT origin. CONCLUSION: Newer data analytic techniques provide a non-invasive approach to identifying PVC SOO, which should be useful for the clinician evaluating a 12-lead ECG.
No takes yet. Share an insight, caveat, or question.
Bajaj et al. (2023) conducted an observational in Premature ventricular complexes (PVC) (n=338). Machine learning cluster analysis of 12-lead ECG parameters vs. Composite of three established clinical ECG algorithms was evaluated on Prediction of Left Ventricular Outflow Tract (LVOT) vs Right Ventricular Outflow Tract (RVOT) site of origin (sensitivity 95.4%; specificity 97.5%). Unsupervised machine learning cluster analysis found that a PVC configuration of V1S plus V2S ≤ 9.25 predicted a left ventricular outflow tract origin with 95.4% sensitivity and 97.5% specificity.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: