Importance of Systematic Right Ventricular Assessment in Cardiac Resynchronization Therapy Candidates: A Machine Learning Approach

Artificial intelligence models in prediction of response to cardiac resynchronization therapy: a systematic review

The aim of the presented review is to summarize the literature data on the accuracy and clinical applicability of artificial intelligence (AI) models as a valuable alternative to the current guidelines in predicting cardiac resynchronization therapy (CRT) response and phenotyping of patients eligible for CRT implantation. This systematic review was performed according to the PRISMA guidelines. After a search of Scopus, PubMed, Cochrane Library, and Embase databases, 675 records were identified. Twenty supervised (prediction of CRT response) and 9 unsupervised (clustering and phenotyping) AI models were analyzed qualitatively (22 studies, 14,258 patients). Fifty-five percent of AI models were based on retrospective studies. Unsupervised AI models were able to identify clusters of patients with significantly different rates of primary outcome events (death, heart failure event). In comparison to the guideline-based CRT response prediction accuracy of 70%, supervised AI models trained on cohorts with > 100 patients achieved up to 85% accuracy and an AUC of 0.86 in their prediction of response to CRT for echocardiographic and clinical outcomes, respectively. AI models seem to be an accurate and clinically applicable tool in phenotyping of patients eligible for CRT implantation and predicting potential responders. In the future, AI may help to increase CRT response rates to over 80% and improve clinical decision-making and prognosis of the patients, including reduction of mortality rates. However, these findings must be validated in randomized controlled trials.

Current role and future perspectives of artificial intelligence in echocardiography

Echocardiography is an essential tool in diagnostic cardiology and is fundamental to clinical care. Artificial intelligence (AI) can help health care providers serving as a valuable diagnostic tool for physicians in the field of echocardiography specially on the automation of measurements and interpretation of results. In addition, it can help expand the capabilities of research and discover alternative pathways in medical management specially on prognostication. In this review article, we describe the current role and future perspectives of AI in echocardiography.

The saga of dyssynchrony imaging: Are we getting to the point

Cardiac resynchronisation therapy (CRT) has an established role in the management of patients with heart failure, reduced left ventricular ejection fraction (LVEF < 35%) and widened QRS (>130 msec). Despite the complex pathophysiology of left ventricular (LV) dyssynchrony and the increasing evidence supporting the identification of specific electromechanical substrates that are associated with a higher probability of CRT response, the assessment of LVEF is the only imaging-derived parameter used for the selection of CRT candidates.

This review aims to (1) provide an overview of the evolution of cardiac imaging for the assessment of LV dyssynchrony and its role in the selection of patients undergoing CRT; (2) highlight the main pitfalls and advantages of the application of cardiac imaging for the assessment of LV dyssynchrony; (3) provide some perspectives for clinical application and future research in this field.

Conclusion

the road for a more individualized approach to resynchronization therapy delivery is open and imaging might provide important input beyond the assessment of LVEF.

Multi-modality imaging to guide the implantation of cardiac electronic devices in heart failure: is the sum greater than the individual components?

Heart failure is a clinical syndrome with an increasing prevalence and incidence worldwide that impacts patients' quality of life, morbidity, and mortality. Implantable cardioverter-defibrillator and cardiac resynchronization therapy are pillars of managing patients with HF and reduced left ventricular ejection fraction. Despite the advances in cardiac imaging, the assessment of patients needing cardiac implantable electronic devices relies essentially on the measure of left ventricular ejection fraction. However, multi-modality imaging can provide important information concerning the aetiology of heart failure, the extent and localization of myocardial scar, and the pathophysiological mechanisms of left ventricular conduction delay. This paper aims to highlight the main novelties and progress in the field of multi-modality imaging to identify patients who will benefit from cardiac resynchronization therapy and/or implantable cardioverter-defibrillator. We also want to underscore the boundaries that prevent the application of imaging-derived parameters to patients who will benefit from cardiac implantable electronic devices and orient the choice of the device. Finally, we aim at providing some reflections for future research in this field.

Reference values and the Z-score values of tricuspid annular plane systolic excursion in Chinese children

To establish age-specific and body surface area (BSA)-specific reference values of Tricuspid Annular Plane Systolic Excursion (TAPSE) for children under 15 years old in China. A retrospective study was conducted in Children's Hospital Attached to the Capital Institute of Pediatrics. A total of 702 cases were included in this research to establish reference values of TAPSE in Chinese children. SPSS 25.0 (IBM) was used for data analysis. Lambda-mu-sigma method was used to calculate and construct the age-specific and BSA-specific percentiles and Z-score curves of TAPSE. The mean value of TAPSE increased with age and BSA from 0 to 15 years in a nonlinear way and reached the adult threshold (17 mm) until 1 year old. There was no difference between genders. TAPSE values increased with age and BSA in Chinese children aged between 0 and 15 years and there was no difference between boys and girls. A prospective, multicenter cohort study from different parts of China is supposed to be conducted in the future to reflect the whole spectrum of TAPSE in Chinese children.

Contemporary Applications of Machine Learning for Device Therapy in Heart Failure

Despite a better understanding of the underlying pathogenesis of heart failure (HF), pharmacotherapy, surgical, and percutaneous interventions do not prevent disease progression in all patients, and a significant proportion of patients end up requiring advanced therapies. Machine learning (ML) is gaining wider acceptance in cardiovascular medicine because of its ability to incorporate large, complex, and multidimensional data and to potentially facilitate the creation of predictive models not constrained by many of the limitations of traditional statistical approaches. With the coexistence of "big data" and novel advanced analytic techniques using ML, there is ever-increasing research into applying ML in the context of HF with the goal of improving patient outcomes. Through this review, the authors describe the basics of ML and summarize the existing published reports regarding contemporary applications of ML in device therapy for HF while highlighting the limitations to widespread implementation and its future promises.

Prediction of response after cardiac resynchronization therapy with machine learning

AIMS

Nearly one third of patients receiving cardiac resynchronization therapy (CRT) suffer non-response. We intend to develop predictive models using machine learning (ML) approaches and easily attainable features before CRT implantation.

METHODS AND RESULTS

The baseline characteristics of 752 CRT recipients from two hospitals were retrospectively collected. Nine ML predictive models were established, including logistic regression (LR), elastic network (EN), lasso regression (Lasso), ridge regression (Ridge), neural network (NN), support vector machine (SVM), random forest (RF), XGBoost and k-nearest neighbour (k-NN). Sensitivity, specificity, precision, accuracy, F1, log-loss, area under the receiver operating characteristic (AU-ROC), and average precision (AP) of each model were evaluated. AU-ROC was compared between models and the latest guidelines. Six models had an AU-ROC value above 0.75. The LR, EN and Ridge models showed the highest overall predictive power compared with other models with AU-ROC at 0.77. The XGBoost model reached the highest sensitivity at 0.72, while the highest specificity was achieved by Ridge model at 0.92. All ML models achieved higher AU-ROCs that those derived from the latest guidelines (all P < 0.05). The effect size analysis identified left bundle branch block, left ventricular end-systolic diameter, and history of percutaneous coronary intervention as the most crucial predictors of CRT response. An online tool to facilitate the prediction of CRT response is freely available at http://www.crt-response.com/.

CONCLUSIONS

ML algorithms produced efficient predictive models for evaluation of CRT response with features before implantation. Tools developed accordingly could improve the selection of CRT candidates and reduce the incidence of non-response.