Right ventricular pacing for hypertrophic obstructive cardiomyopathy: meta-analysis and meta-regression of clinical trials

Discriminating electrocardiographic responses to His-bundle pacing using machine learning

BACKGROUND

His-bundle pacing (HBP) has emerged as an alternative to conventional ventricular pacing because of its ability to deliver physiological ventricular activation. Pacing at the His bundle produces different electrocardiographic (ECG) responses: selective His-bundle pacing (S-HBP), non-selective His bundle pacing (NS-HBP), and myocardium-only capture (MOC). These 3 capture types must be distinguished from each other, which can be challenging and time-consuming even for experts.

OBJECTIVE

The purpose of this study was to use artificial intelligence (AI) in the form of supervised machine learning using a convolutional neural network (CNN) to automate HBP ECG interpretation.

METHODS

We identified patients who had undergone HBP and extracted raw 12-lead ECG data during S-HBP, NS-HBP, and MOC. A CNN was trained, using 3-fold cross-validation, on 75% of the segmented QRS complexes labeled with their capture type. The remaining 25% was kept aside as a testing dataset.

RESULTS

The CNN was trained with 1297 QRS complexes from 59 patients. Cohen kappa for the neural network's performance on the 17-patient testing set was 0.59 (95% confidence interval 0.30 to 0.88; P <.0001), with an overall accuracy of 75%. The CNN's accuracy in the 17-patient testing set was 67% for S-HBP, 71% for NS-HBP, and 84% for MOC.

CONCLUSION

We demonstrated proof of concept that a neural network can be trained to automate discrimination between HBP ECG responses. When a larger dataset is trained to higher accuracy, automated AI ECG analysis could facilitate HBP implantation and follow-up and prevent complications resulting from incorrect HBP ECG analysis.

Targeted Medical Therapies for Hypertrophic Cardiomyopathy

PURPOSE OF REVIEW

The management of hypertrophic cardiomyopathy (HCM) has changed considerably over the years, although molecular therapies targeting core mechanisms of the disease are still lacking. This review provides an overview of the contemporary medical approach to patients with HCM, and of promising novel developments hopefully soon to enter the clinical arena.

RECENT FINDINGS

Our perception of therapeutic targets for medical therapy in HCM is rapidly evolving. Novel approaches include myocardial metabolic modulation, late sodium current inhibition, and allosteric myosin inhibition, actively pursued to reduce and hopefully prevent the development of severe HCM phenotypes, improve symptom control, and preserve patients from disease-related complications. Clinical management of patients with HCM should be guided by in-depth knowledge of the complex mechanisms at the energetic, metabolic, and electrophysiologic level. Until new experimental therapies become available, tailored management of modifiable disease manifestations should be pursued, including lifestyle counseling and prevention of comorbidities.