Unsupervised machine learning of QRS waveforms identified a subgroup with lower risk of death, LVAD, or transplant compared to Group 2 (HR 0.44; 95% CI 0.38-0.53; P<0.001).
Cohort (n=946)
Does unsupervised machine learning of 12-lead QRS waveforms identify subgroups with differential outcomes in CRT patients with conduction delay?
Unsupervised machine learning of 12-lead QRS waveforms can identify CRT patient subgroups with differential clinical and echocardiographic outcomes, potentially improving patient selection beyond traditional QRS duration and LBBB criteria.
Effect estimate: HR 0.44 (95% CI 0.38-0.53)
p-value: p=<0.001
Background: Cardiac resynchronization therapy (CRT) improves heart failure outcomes but has significant nonresponse rates, highlighting limitations in ECG selection criteria: QRS duration (QRSd) ≥150 ms and subjective labeling of left bundle branch block (LBBB). We explored unsupervised machine learning of ECG waveforms to identify CRT subgroups that may differentiate outcomes beyond QRSd and LBBB. Methods: We retrospectively analyzed 946 CRT patients with conduction delay. Principal component analysis (PCA) dimensionality reduction obtained a 2-dimensional representation of preCRT 12-lead QRS waveforms. k -means clustering of the 2-dimensional PCA representation of 12-lead QRS waveforms identified 2 patient subgroups (QRS PCA groups). Vectorcardiographic QRS area was also calculated. We examined following 2 primary outcomes: (1) composite end point of death, left ventricular assist device, or heart transplant, and (2) degree of echocardiographic left ventricular ejection fraction (LVEF) change after CRT. Results: Compared with QRS PCA Group 2 ( n =425), Group 1 ( n =521) had lower risk for reaching the composite end point (HR, 0.44 95% CI, 0.38–0.53; P <0.001) and experienced greater mean LVEF improvement (11.1±11.7% versus 4.8±9.7%; P <0.001), even among patients with LBBB with QRSd ≥150 ms (HR, 0.42 95% CI, 0.30–0.57; P <0.001; mean LVEF change 12.5±11.8% versus 7.3±8.1%; P =0.001). QRS area also stratified outcomes but had significant differences from QRS PCA groups. A stratification scheme combining QRS area and QRS PCA group identified patients with LBBB with similar outcomes to non-LBBB patients (HR, 1.32 95% CI, 0.93–1.62; difference in mean LVEF change: 0.8% 95% CI, −2.1% to 3.7%). The stratification scheme also identified patients with LBBB with QRSd <150 ms with comparable outcomes to patients with LBBB with QRSd ≥150 ms (HR, 0.93 95% CI, 0.67–1.29; difference in mean LVEF change: −0.2% 95% CI, −2.7% to 3.0%). Conclusions: Unsupervised machine learning of ECG waveforms identified CRT subgroups with relevance beyond LBBB and QRSd. This method may assist in objective classification of bundle branch block morphology in CRT.
Feeny et al. (Mon,) conducted a cohort in Heart failure requiring cardiac resynchronization therapy (n=946). QRS PCA Group 1 vs. QRS PCA Group 2 was evaluated on Composite of death, left ventricular assist device, or heart transplant (HR 0.44, 95% CI 0.38-0.53, p=<0.001). Unsupervised machine learning of QRS waveforms identified a subgroup with lower risk of death, LVAD, or transplant compared to Group 2 (HR 0.44; 95% CI 0.38-0.53; P<0.001).