Patient-Tailored Cardiac Resynchronization Therapy: Imaging and Exercise Strategies

Abstract

Better characterisation of the electromechanical substrate (part I) may promote more appropriate patient selection for cardiac resynchronization therapy (CRT) implantation. By developing an and-to-end automated and explainable deep learning-based approach, we were able to identify a patient cluster with the predicted combination of non-response and poor outcome after CRT (chapter 2). This approach allows for objective risk-stratification that outperforms contemporary electrocardiogram characteristics and QRSAREA, and thereby reliable excludes the presence of a CRT-amendable substrate. Chapter 3 illustrates how incorporating the mechanical substrate may extend the accuracy of such prediction models, which in turn is illustrated in the form of a combined assessment of the electrical substrate and mechanical dysfunction in chapter 4. Here, a simple four-variable model was shown to be associated with a sustained volumetric response, which indicates stable disease remission.

Optimizing left ventricular (LV) lead position (part II) may improve outcome after CRT, whereas poor placement may preclude response despite an underlying amendable substrate or favourable patient characteristics. Hence, chapter 5 discusses the importance of optimizing LV-lead placement. It is shown how the optimal pacing site is highly variable, patient-specific, difficult to predict during implantation, and especially critical in the presence of atypical LV activation or myocardial scar. In these patients, image-guidance, rather than electrical-guidance, is likely the most important approach for achieving the optimal transvenous lead position. Hence, chapter 6 demonstrates that real-time navigation of the LV-lead is feasible in a multicentre setting. Not only were leads adequately positioned in 76% of patients, echocardiographic response was achieved in 86% of patients. Chapter 7 then discusses the protocol of the multicentre randomised controlled ‘Advanced Image Supported Lead Placement in Cardiac Resynchronization Therapy’ (ADVISE) trial, which will further investigate the clinical efficacy of this approach.

Lastly, part III explores the potential diagnostic (chapter 8), prognostic (chapter 9), and therapeutic (chapter 10) implications of exercise in heart failure. In chapter 8, the feasibility of strain-based discoordination-imaging during stress echocardiography was demonstrated. Stress echocardiography could unmask dynamic changes in mechanical dyscoordination. Although feasible, many challenges remain to overcome. Chapter 9 demonstrates that submaximal oxygen uptake (VO2) kinetics are associated with volumetric response to CRT. While at least equally effective as measuring VO2 Peak, submaximal VO2 kinetics may provide a more relevant characterisation of exercise tolerance, which is also more reliable and patient-friendly. Finally, in chapter 10, we illustrate how differences in cardiac output and VO2 kinetics during submaximal exercise distinguish the severity of functional impairment in chronic heart failure, rather than differences in resting echocardiography. Moreover, we demonstrate that both high-intensity interval training and CRT improve submaximal exercise tolerance. By contrast, only CRT induces cardiac reverse remodelling, whereas only an exercise training program improved exercising haemodynamics. Hence, combining both therapies may further improve functional recovery through differential effects.