19 min
Circulation: Arrhythmia and Electrophysiology July 2020 Issue Circulation: Arrhythmia and Electrophysiology On the Beat
-
- Natural Sciences
Paul J. Wang:
Welcome to the monthly podcast, On the BEAT, for Circulation: Arrhythmia and Electrophysiology. I'm Dr. Paul Wang, Editor in Chief, with some of the key highlights from this month's issue.
Albert Feeny and Associates used unsupervised machine learning of electrocardiogram [ECG] waveforms to identify cardiac resynchronization therapy [CRT] subgroups to differentiate outcomes beyond QRS duration and left bundle branch block. They retrospectively analyzed 946 CRT patients with conduction delay. Principal component analysis [PCA] dimensionality reduction obtained a 2-dimensional representation of pre-CRT 12-lead QRS waveforms. K-means clustering of the 2-dimensional PCA representation of 12-lead QRS waveforms identified two patient subgroups [QRS PCA groups]. Vectorcardiographic QRS area was also calculated. They examined two primary outcomes: (1) composite endpoint of death, left ventricular assist device, or heart transplant, and (2) degree of echocardiographic left ventricular ejection fraction [LVEF] change after CRT. Compared to QRS PCA group 2 (n = 425), Group 1 (n=521) had a lower risk for achieving the composite endpoint (hazard ratio of 0.44, P In our next paper, Julie Shade, Rheeda Ali and Associates combined machine learning [ML] and personalized computational modeling to predict, prior to pulmonary vein isolation [PVI], which patients are most likely to experience atrial fibrillation [AF] recurrence after PVI. The single center retrospective proof of concept study included 32 patients with documented paroxysmal AF who underwent PVI and had pre-procedural late gadolinium enhanced magnetic resonance imaging [LGE MRI]. For each patient, a personalized computational model of the left atrium simulated AF induction via rapid pacing features were derived from pre-PVI LG MRI images and from results of simulations [SIM] AF. The most predictive features used to input to a quadratic discrimination analysis ML classifier, which was trained, optimized, and evaluated with a 10-fold nested cross validation to predict the probability of AF recurrence post PVI. In the cohort, the ML classifier predicted probability of AF recurrence with an average validation, sensitivity, and specificity of 82% and 89% respectively, and a validation AUC of 0.82. Dissecting the relative contributions of simulations SIM AF and raw images to the predictive capability of the ML classifier, they found that only when features from simulation SIM AF were used to train the ML classifier, its performance retained similar (validation AUC equals 0.81). However, when only features classified from raw images were used for training, the validation AUC significantly decreased (0.47).
In our next paper, Sarah Vermij and Associates examined sodium channel NaV 1.5 localization and function mutations in the gene and coding the sodium channel NaV 1.5 caused various cardiac arrhythmias. The authors use novel single-molecule localization [S-M-L-M] and computational modeling to define nanoscale features of NaV 1.5 localization and distribution at the lateral membrane [L-M], the LM groove, and T-tubules in cardiomyocytes from wild-type (N=3), dystrophin-deficient (mdx; N=3) mice, and mice expressing C-terminally truncated NaV 1.5 (ΔSIV; N=3). The authors assessed T-tubules sodium current by recording whole-cell sodium currents in control (N=5) in detubulated (N=5) wild-type cardiomyocytes. The authors found that NaV 1.5 organizes as distinct clusters in the groove and T-tubules which density, distribution, and organization partially depend on SIV and dystrophin. They found that overall reduction in NaV 1.5 expression expressed in mdx and ΔSIV cells result in a non-uniform distribution with NaV 1.5 being specifically reduced at the groove ΔSIV and increased in T-tubules of mdx cardiomyocytes. A T-tubules sodium current could, however, not be demonstrated. The authors concluded that NaV 1.5 mutations may site-specifically affect NaV 1.5 localization and d
Paul J. Wang:
Welcome to the monthly podcast, On the BEAT, for Circulation: Arrhythmia and Electrophysiology. I'm Dr. Paul Wang, Editor in Chief, with some of the key highlights from this month's issue.
Albert Feeny and Associates used unsupervised machine learning of electrocardiogram [ECG] waveforms to identify cardiac resynchronization therapy [CRT] subgroups to differentiate outcomes beyond QRS duration and left bundle branch block. They retrospectively analyzed 946 CRT patients with conduction delay. Principal component analysis [PCA] dimensionality reduction obtained a 2-dimensional representation of pre-CRT 12-lead QRS waveforms. K-means clustering of the 2-dimensional PCA representation of 12-lead QRS waveforms identified two patient subgroups [QRS PCA groups]. Vectorcardiographic QRS area was also calculated. They examined two primary outcomes: (1) composite endpoint of death, left ventricular assist device, or heart transplant, and (2) degree of echocardiographic left ventricular ejection fraction [LVEF] change after CRT. Compared to QRS PCA group 2 (n = 425), Group 1 (n=521) had a lower risk for achieving the composite endpoint (hazard ratio of 0.44, P In our next paper, Julie Shade, Rheeda Ali and Associates combined machine learning [ML] and personalized computational modeling to predict, prior to pulmonary vein isolation [PVI], which patients are most likely to experience atrial fibrillation [AF] recurrence after PVI. The single center retrospective proof of concept study included 32 patients with documented paroxysmal AF who underwent PVI and had pre-procedural late gadolinium enhanced magnetic resonance imaging [LGE MRI]. For each patient, a personalized computational model of the left atrium simulated AF induction via rapid pacing features were derived from pre-PVI LG MRI images and from results of simulations [SIM] AF. The most predictive features used to input to a quadratic discrimination analysis ML classifier, which was trained, optimized, and evaluated with a 10-fold nested cross validation to predict the probability of AF recurrence post PVI. In the cohort, the ML classifier predicted probability of AF recurrence with an average validation, sensitivity, and specificity of 82% and 89% respectively, and a validation AUC of 0.82. Dissecting the relative contributions of simulations SIM AF and raw images to the predictive capability of the ML classifier, they found that only when features from simulation SIM AF were used to train the ML classifier, its performance retained similar (validation AUC equals 0.81). However, when only features classified from raw images were used for training, the validation AUC significantly decreased (0.47).
In our next paper, Sarah Vermij and Associates examined sodium channel NaV 1.5 localization and function mutations in the gene and coding the sodium channel NaV 1.5 caused various cardiac arrhythmias. The authors use novel single-molecule localization [S-M-L-M] and computational modeling to define nanoscale features of NaV 1.5 localization and distribution at the lateral membrane [L-M], the LM groove, and T-tubules in cardiomyocytes from wild-type (N=3), dystrophin-deficient (mdx; N=3) mice, and mice expressing C-terminally truncated NaV 1.5 (ΔSIV; N=3). The authors assessed T-tubules sodium current by recording whole-cell sodium currents in control (N=5) in detubulated (N=5) wild-type cardiomyocytes. The authors found that NaV 1.5 organizes as distinct clusters in the groove and T-tubules which density, distribution, and organization partially depend on SIV and dystrophin. They found that overall reduction in NaV 1.5 expression expressed in mdx and ΔSIV cells result in a non-uniform distribution with NaV 1.5 being specifically reduced at the groove ΔSIV and increased in T-tubules of mdx cardiomyocytes. A T-tubules sodium current could, however, not be demonstrated. The authors concluded that NaV 1.5 mutations may site-specifically affect NaV 1.5 localization and d
19 min