Machine Learning of 12-Lead QRS Waveforms to Identify Cardiac Resynchronization Therapy Patients With Differential Outcomes. (July 2020)

• Record Type: Journal Article
• Authors: Feeny, Albert K.; Rickard, John; Trulock, Kevin M.; Patel, Divyang; Toro, Saleem; Moennich, Laurie Ann; Varma, Niraj; Niebauer, Mark J.; Gorodeski, Eiran Z.; Grimm, Richard A.; Barnard, John; Madabhushi, Anant; Chung, Mina K.
• Is Part Of: Circulation. Volume 13:Number 7(2020)
• Publication Date: 2020-07
• Subjects: bundle branch block -- cardiac resynchronization therapy -- electrocardiography -- heart failure -- machine learning; Arrhythmia -- Periodicals; Heart -- Electric properties -- Periodicals; 616.128
• DOI: 10.1161/CIRCEP.119.008210
• Languages: English
• ISSNs: 1941-3149

Abstract

Abstract : 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. Abstract : Supplemental Digital Content is available in the text.