Enhancing Response in the Cardiac Resynchronization Therapy Patient

Virtual pacing of a patient's digital twin to predict left ventricular reverse remodelling after cardiac resynchronization therapy

AIMS

Identifying heart failure (HF) patients who will benefit from cardiac resynchronization therapy (CRT) remains challenging. We evaluated whether virtual pacing in a digital twin (DT) of the patient's heart could be used to predict the degree of left ventricular (LV) reverse remodelling post-CRT.

METHODS AND RESULTS

Forty-five HF patients with wide QRS complex (≥130 ms) and reduced LV ejection fraction (≤35%) receiving CRT were retrospectively enrolled. Echocardiography was performed before (baseline) and 6 months after CRT implantation to obtain LV volumes and 18-segment longitudinal strain. A previously developed algorithm was used to generate 45 DTs by personalizing the CircAdapt model to each patient's baseline measurements. From each DT, baseline septal-to-lateral myocardial work difference (MWLW-S,DT) and maximum rate of LV systolic pressure rise (dP/dtmax,DT) were derived. Biventricular pacing was then simulated using patient-specific atrioventricular delay and lead location. Virtual pacing-induced changes ΔMWLW-S,DT and ΔdP/dtmax,DT were correlated with real-world LV end-systolic volume change at 6-month follow-up (ΔLVESV). The DT's baseline MWLW-S,DT and virtual pacing-induced ΔMWLW-S,DT were both significantly associated with the real patient's reverse remodelling ΔLVESV (r = -0.60, P < 0.001 and r = 0.62, P < 0.001, respectively), while correlation between ΔdP/dtmax,DT and ΔLVESV was considerably weaker (r = -0.34, P = 0.02).

CONCLUSION

Our results suggest that the reduction of septal-to-lateral work imbalance by virtual pacing in the DT can predict real-world post-CRT LV reverse remodelling. This DT approach could prove to be an additional tool in selecting HF patients for CRT and has the potential to provide valuable insights in optimization of CRT delivery.

Treatment of cardiac resynchronization therapy non-responders: current approaches and new frontiers

INTRODUCTION

Cardiac resynchronization therapy (CRT) has developed into a very effective technology for patients with decreased systolic function and has substantially improved patients' clinical course. However, non-responsiveness to CRT, described as lack of reverse cardiac chamber remodeling, leading to lack to improve symptoms, heart failure hospitalizations or mortality, is common, rather unpredictable, and not fully understood.

AREAS COVERED

This article aims to discuss key factors that are impacting CRT response; from patient selection to LV lead position, to structured follow-up in CRT clinics. Secondly, common causes and interventions for CRT non-responsiveness are discussed. Next, insight is given into technologies representing new and feasible interventions as well as pacing strategies in this group of patients that remain challenging to treat. Finally, an outlook is given into future scientific development.

EXPERT OPINION

Despite the progress that has been made, CRT non-response remains a significant and complex problem. Patient management in interdisciplinary teams including heart failure, imaging, and cardiac arrhythmia experts appears critical as complexity is increasing and CRT non-response often is a multifactorial problem. This will allow optimization of medical therapy, the use of new integrated sensor technologies and telemedicine to ultimately optimize outcomes for all patients in need of CRT.

Status and Update on Cardiac Resynchronization Therapy Trials

After decades of clinical use, cardiac resynchronization therapy (CRT) can be considered an established therapy. However, there are multiple open questions to be addressed that shall further improve the proportion of patients responding to CRT. Progress in better understanding the relationship between electrical and mechanical disorder in patients with heart failure with ventricular conduction abnormalities is important. This article presents and discusses ongoing studies in different areas of CRT research, including patient selection by novel diagnostic tools, extension of clinical criteria, left ventricular lead positioning and pacing site selection, optimization of CRT delivery and programming, and selection of device type.

Machine Learning Prediction of Cardiac Resynchronisation Therapy Response From Combination of Clinical and Model-Driven Data

Background: Up to 30–50% of chronic heart failure patients who underwent cardiac resynchronization therapy (CRT) do not respond to the treatment. Therefore, patient stratification for CRT and optimization of CRT device settings remain a challenge.

Objective: The main goal of our study is to develop a predictive model of CRT outcome using a combination of clinical data recorded in patients before CRT and simulations of the response to biventricular (BiV) pacing in personalized computational models of the cardiac electrophysiology.

Materials and Methods: Retrospective data from 57 patients who underwent CRT device implantation was utilized. Positive response to CRT was defined by a 10% increase in the left ventricular ejection fraction in a year after implantation. For each patient, an anatomical model of the heart and torso was reconstructed from MRI and CT images and tailored to ECG recorded in the participant. The models were used to compute ventricular activation time, ECG duration and electrical dyssynchrony indices during intrinsic rhythm and BiV pacing from the sites of implanted leads. For building a predictive model of CRT response, we used clinical data recorded before CRT device implantation together with model-derived biomarkers of ventricular excitation in the left bundle branch block mode of activation and under BiV stimulation. Several Machine Learning (ML) classifiers and feature selection algorithms were tested on the hybrid dataset, and the quality of predictors was assessed using the area under receiver operating curve (ROC AUC). The classifiers on the hybrid data were compared with ML models built on clinical data only.

Results: The best ML classifier utilizing a hybrid set of clinical and model-driven data demonstrated ROC AUC of 0.82, an accuracy of 0.82, sensitivity of 0.85, and specificity of 0.78, improving quality over that of ML predictors built on clinical data from much larger datasets by more than 0.1. Distance from the LV pacing site to the post-infarction zone and ventricular activation characteristics under BiV pacing were shown as the most relevant model-driven features for CRT response classification.

Conclusion: Our results suggest that combination of clinical and model-driven data increases the accuracy of classification models for CRT outcomes.

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

BACKGROUND

Despite all having systolic heart failure and broad QRS intervals, patients screened for cardiac resynchronization therapy (CRT) are highly heterogeneous, and it remains extremely challenging to predict the impact of CRT devices on left ventricular function and outcomes. The aim of this study was to evaluate the relative impact of clinical, electrocardiographic, and echocardiographic data on the left ventricular remodeling and prognosis of CRT candidates by the application of machine learning approaches.

METHODS

One hundred ninety-three patients with systolic heart failure receiving CRT according to current recommendations were prospectively included in this multicenter study. A combination of the Boruta algorithm and random forest methods was used to identify features predicting both CRT volumetric response and prognosis. Model performance was tested using the area under the receiver operating characteristic curve. The k-medoid method was also applied to identify clusters of phenotypically similar patients.

RESULTS

From 28 clinical, electrocardiographic, and echocardiographic variables, 16 features were predictive of CRT response, and 11 features were predictive of prognosis. Among the predictors of CRT response, eight variables (50%) pertained to right ventricular size or function. Tricuspid annular plane systolic excursion was the main feature associated with prognosis. The selected features were associated with particularly good prediction of both CRT response (area under the curve, 0.81; 95% CI, 0.74-0.87) and outcomes (area under the curve, 0.84; 95% CI, 0.75-0.93). An unsupervised machine learning approach allowed the identification of two phenogroups of patients who differed significantly in clinical variables and parameters of biventricular size and right ventricular function. The two phenogroups had significantly different prognosis (hazard ratio, 4.70; 95% CI, 2.1-10.0; P < .0001; log-rank P < .0001).

CONCLUSIONS

Machine learning can reliably identify clinical and echocardiographic features associated with CRT response and prognosis. The evaluation of both right ventricular size and functional parameters has pivotal importance for the risk stratification of CRT candidates and should be systematically performed in patients undergoing CRT.

Computational models in cardiology

The treatment of individual patients in cardiology practice increasingly relies on advanced imaging, genetic screening and devices. As the amount of imaging and other diagnostic data increases, paralleled by the greater capacity to personalize treatment, the difficulty of using the full array of measurements of a patient to determine an optimal treatment seems also to be paradoxically increasing. Computational models are progressively addressing this issue by providing a common framework for integrating multiple data sets from individual patients. These models, which are based on physiology and physics rather than on population statistics, enable computational simulations to reveal diagnostic information that would have otherwise remained concealed and to predict treatment outcomes for individual patients. The inherent need for patient-specific models in cardiology is clear and is driving the rapid development of tools and techniques for creating personalized methods to guide pharmaceutical therapy, deployment of devices and surgical interventions.

Changes in QRS Area and QRS Duration After Cardiac Resynchronization Therapy Predict Cardiac Mortality, Heart Failure Hospitalizations, and Ventricular Arrhythmias

Background

Predicting clinical outcomes after cardiac resynchronization therapy (CRT) and its optimization remain a challenge. We sought to determine whether pre‐ and postimplantation QRS area (QRS_area) predict clinical outcomes after CRT.

Methods and Results

In this retrospective study, QRS_area, derived from pre‐ and postimplantation vectorcardiography, were assessed in relation to the primary end point of cardiac mortality after CRT with or without defibrillation. Other end points included total mortality, total mortality or heart failure (HF) hospitalization, total mortality or major adverse cardiac events, and the arrhythmic end point of sudden cardiac death or ventricular arrhythmias with or without a shock. In patients (n=380, age 72.0±12.4 years, 68.7% male) undergoing CRT over 7.7 years (median follow‐up: 3.8 years [interquartile range 2.3–5.3]), preimplantation QRS_area ≥102 μVs predicted cardiac mortality (HR: 0.36; P<0.001), independent of QRS duration (QRSd) and morphology (P<0.001). A QRS_area reduction ≥45 μVs after CRT predicted cardiac mortality (HR: 0.19), total mortality (HR: 0.50), total mortality or heart failure hospitalization (HR: 0.44), total mortality or major adverse cardiac events (HR: 0.43) (all P<0.001) and the arrhythmic end point (HR: 0.26; P<0.001). A concomitant reduction in QRS_area and QRSd was associated with the lowest risk of cardiac mortality and the arrhythmic end point (both HR: 0.12, P<0.001).

Conclusions

Pre‐implantation QRS_area, derived from vectorcardiography, was superior to QRSd and QRS morphology in predicting cardiac mortality after CRT. A postimplant reduction in both QRS_area and QRSd was associated with the best outcomes, including the arrhythmic end point.

Long-Term Effect of Different Optimizing Methods for Cardiac Resynchronization Therapy in Patients with Heart Failure: A Randomized and Controlled Pilot Study

AIM

During cardiac resynchronization therapy (CRT), optimized programming of the atrioventricular (AV) delay and ventricular-to-ventricular (VV) interval can lead to improved hemodynamics, symptomatic response, and left ventricular systolic function. Currently, however, there is no recommendation for the best optimization method. This study aimed to compare the long-term clinical outcomes of 4 different CRT optimization methods.

METHODS

One hundred and twenty-four consecutive CRT patients with severe heart failure and left bundle-branch block configuration were randomly assigned into four groups to undergo AV/VV delay optimization through echocardiogram (ECHO; n = 30), electrocardiogram (ECG; n = 32), QuickOpt algorithm (n = 28), and nominal AV/VV (n = 36) groups. Patients were followed up and underwent examinations, including New York Heart Association (NYHA) cardiac functional classification, 6-min walking distance (6MWD), and echocardiography, at 6, 12, 24, 36, and 48 months, respectively. The patients' survival and clinical outcomes were compared among the four groups.

RESULTS

Kaplan-Meier survival analyses showed that the median survival was the same in the 4 groups: ECHO, 43 months; ECG, 44 months; QuickOpt, 44 months, and nominal, 41 months. At the 6-month follow-up, the reduction in left ventricular end diastolic diameter (LVEDD) was significantly less in the nominal group (-1.91 ± 2.58 mm) than that in the other three groups (ECHO: -3.70 ± 2.78 mm, p = 0.012; ECG: -3.53 ± 3.14 mm, p = 0.020; QuickOpt: -3.46 ± 2.65 mm, p = 0.032); 6MWD was significantly shorter in the nominal group (87.88 ± 34.76 m) than that in the other three groups (ECHO: 120.63 ± 56.93 m, p = 0.006; ECG: 114.97 ± 54.95 m, p = 0.020; QuickOpt: 114.57 ± 35.41 m, p = 0.027). Left ventricular ejection fraction (LVEF) significantly increased in ECHO (7.23 ± 2.76%, p = 0.010), ECG (8.50 ± 3.17%, p < 0.001), and QuickOpt (8.39 ± 2.90%, p < 0.001) compared with the nominal group (5.35 ± 2.59%). There were no significant differences among the groups in the aforementioned parameters at 24, 36, and 48 months, respectively.

CONCLUSION

While LVEDD, LVEF, 6MWD, and NYHA were significantly improved in ECHO, ECG, and QuickOpt at 6 months, there were no significant improvements in any of the groups at 12, 24, and 48 months. These findings suggested that the long-term effect of the four CRT methods for heart failure was not significantly different.

Left Ventricular Response to Cardiac Resynchronization Therapy: Insights From Hemodynamic Forces Computed by Speckle Tracking

Aims: Despite continuous efforts in improving the selection process, the rate of non-responders to cardiac resynchronization therapy (CRT) remains high. Recent studies on intraventricular blood flow suggested that the alignment of hemodynamic forces (HDFs) may be a reproducible biomarker of mechanical dyssynchrony. We aimed to explore the relationship between pacing-induced realignment of HDFs and positive response to CRT. Methods and results: We retrospectively analyzed 38 patients from the CRT database of our institution fulfilling the inclusion criteria for HDFs-related echocardiographic assessment early pre and post CRT implantation, with available mid-term follow-up (≥ 6 months) evaluation. Standard echocardiographic and deformation parameters early pre and post CRT implantation were integrated with the measurement of HFDs through novel methods based on speckle-tracking analysis. At midterm follow-up 71% of patients were classified as responders (reduction of Left Ventricular Systolic Volume Indexed ≥ 15%). Patients did not display significant changes between close evaluations pre and post-implant in terms of ejection fraction and strain metrics. A significant reduction of the ratio between the amplitudes of transversal and longitudinal force components was found. The variation of this ratio strongly correlates (R² =0.60) with Left Ventricular (LV) end-systolic volume variation at mid-term follow up. Conclusion: Pacing-induced realignment of HDFs is associated with CRT efficacy at follow up. These preliminary results claim for dedicated prospective clinical studies testing the potential impact of HDFs study for patient selection and pacing optimization in CRT.

[Multipoint pacing-more CRT or a waste of battery power?]

Cardiac resynchronization therapy (CRT) reduces morbidity and mortality in patients with broad QRS complex ≥130 ms and heart failure with reduced ejection fraction despite optimal guideline-directed medical therapy. However, approximately 30% of the patients implanted with a CRT system do not show clinical benefit. Reasons for nonresponse are complex and some aspects can be addressed during follow-up. Based on quadripolar lead technology, multipoint pacing (MPP) allows left ventricular stimulation at two different sites along the lead. In particular, in scarred and fibrotic ventricular myocardium stimulation at two different sites may overcome conduction barriers and lead to homogeneous ventricular depolarization. Especially for patients that do not respond to conventional CRT, activation of MPP may present an option to increase clinical response. On the other hand, MPP may significantly decrease battery longevity.This review offers an overview of the current knowledge and data on MPP balancing the potential clinical benefit and the possible disadvantages of this therapy.