Patient-specific heart simulation can identify non-responders to cardiac resynchronization therapy

Personalized computational electro-mechanics simulations to optimize cardiac resynchronization therapy

In this study, we present a computational framework designed to evaluate virtual scenarios of cardiac resynchronization therapy (CRT) and compare their effectiveness based on relevant clinical biomarkers. Our approach involves electro-mechanical numerical simulations personalized, for patients with left bundle branch block, by means of a calibration obtained using data from Electro-Anatomical Mapping System (EAMS) measures acquired by cardiologists during the CRT procedure, as well as ventricular pressures and volumes, both obtained pre-implantation. We validate the calibration by using EAMS data coming from right pacing conditions. Three patients with fibrosis and three without are considered to explore various conditions. Our virtual scenarios consist of personalized numerical experiments, incorporating different positions of the left electrode along reconstructed epicardial veins; different locations of the right electrode; different ventriculo-ventricular delays. The aim is to offer a comprehensive tool capable of optimizing CRT efficiency for individual patients. We provide preliminary answers on optimal electrode placement and delay, by computing some relevant biomarkers such as d P / d t max , ejection fraction, stroke work. From our numerical experiments, we found that the latest activated segment during sinus rhythm is an effective choice for the non-fibrotic cases for the location of the left electrode. Also, our results showed that the activation of the right electrode before the left one seems to improve the CRT performance for the non-fibrotic cases. Last, we found that the CRT performance seems to improve by positioning the right electrode halfway between the base and the apex. This work is on the line of computational works for the study of CRT and introduces new features in the field, such as the presence of the epicardial veins and the movement of the right electrode. All these studies from the different research groups can in future synergistically flow together in the development of a tool which clinicians could use during the procedure to have quantitative information about the patient's propagation in different scenarios.

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.

Combination of personalized computational modeling and machine learning for optimization of left ventricular pacing site in cardiac resynchronization therapy

Introduction: The 30-50% non-response rate to cardiac resynchronization therapy (CRT) calls for improved patient selection and optimized pacing lead placement. The study aimed to develop a novel technique using patient-specific cardiac models and machine learning (ML) to predict an optimal left ventricular (LV) pacing site (ML-PS) that maximizes the likelihood of LV ejection fraction (LVEF) improvement in a given CRT candidate. To validate the approach, we evaluated whether the distance D_PS between the clinical LV pacing site (ref-PS) and ML-PS is associated with improved response rate and magnitude. Materials and methods: We reviewed retrospective data for 57 CRT recipients. A positive response was defined as a more than 10% LVEF improvement. Personalized models of ventricular activation and ECG were created from MRI and CT images. The characteristics of ventricular activation during intrinsic rhythm and biventricular (BiV) pacing with ref-PS were derived from the models and used in combination with clinical data to train supervised ML classifiers. The best logistic regression model classified CRT responders with a high accuracy of 0.77 (ROC AUC = 0.84). The LR classifier, model simulations and Bayesian optimization with Gaussian process regression were combined to identify an optimal ML-PS that maximizes the ML-score of CRT response over the LV surface in each patient. Results: The optimal ML-PS improved the ML-score by 17 ± 14% over the ref-PS. Twenty percent of the non-responders were reclassified as positive at ML-PS. Selection of positive patients with a max ML-score >0.5 demonstrated an improved clinical response rate. The distance D_PS was shorter in the responders. The max ML-score and D_PS were found to be strong predictors of CRT response (ROC AUC = 0.85). In the group with max ML-score > 0.5 and D_PS< 30 mm, the response rate was 83% compared to 14% in the rest of the cohort. LVEF improvement in this group was higher than in the other patients (16 ± 8% vs. 7 ± 8%). Conclusion: A new technique combining clinical data, personalized heart modelling and supervised ML demonstrates the potential for use in clinical practice to assist in optimizing patient selection and predicting optimal LV pacing lead position in HF candidates for CRT.

In silico screening method for non-responders to cardiac resynchronization therapy in patients with heart failure: a pilot study

Background

Cardiac resynchronization therapy (CRT) is an effective treatment option for patients with heart failure (HF) and left ventricular (LV) dyssynchrony. However, the problem of some patients not responding to CRT remains unresolved. This study aimed to propose a novel in silico method for CRT simulation.

Methods

Three-dimensional heart geometry was constructed from computed tomography images. The finite element method was used to elucidate the electric wave propagation in the heart. The electric excitation and mechanical contraction were coupled with vascular hemodynamics by the lumped parameter model. The model parameters for three-dimensional (3D) heart and vascular mechanics were estimated by matching computed variables with measured physiological parameters. CRT effects were simulated in a patient with HF and left bundle branch block (LBBB). LV end-diastolic (LVEDV) and end-systolic volumes (LVESV), LV ejection fraction (LVEF), and CRT responsiveness measured from the in silico simulation model were compared with those from clinical observation. A CRT responder was defined as absolute increase in LVEF ≥ 5% or relative increase in LVEF ≥ 15%.

Results

A 68-year-old female with nonischemic HF and LBBB was retrospectively included. The in silico CRT simulation modeling revealed that changes in LVEDV, LVESV, and LVEF by CRT were from 174 to 173 mL, 116 to 104 mL, and 33 to 40%, respectively. Absolute and relative ΔLVEF were 7% and 18%, respectively, signifying a CRT responder. In clinical observation, echocardiography showed that changes in LVEDV, LVESV, and LVEF by CRT were from 162 to 119 mL, 114 to 69 mL, and 29 to 42%, respectively. Absolute and relative ΔLVESV were 13% and 31%, respectively, also signifying a CRT responder. CRT responsiveness from the in silico CRT simulation model was concordant with that in the clinical observation.

Conclusion

This in silico CRT simulation method is a feasible technique to screen for CRT non-responders in patients with HF and LBBB.

Ventricular Arrhythmias in Seniors with Heart Failure: Present Dilemmas and Therapeutic Considerations: A Systematic Review

BACKGROUND

Heart Failure (HF) is a global public health problem, which affects over 23 million people worldwide. The prevalence of HF is higher among seniors in the USA and other developed countries. Ventricular Arrhythmias (VAs) account for 50% of deaths among patients with HF. We aim to elucidate the factors associated with VAs among seniors with HF, as well as therapies that may improve the outcomes.

METHODS

PubMed, Web of Science, Scopus, Cochrane Library databases, Science Direct, and Google Scholar were searched using specific keywords. The reference lists of relevant articles were searched for additional studies related to HF and VAs among seniors as well as associated outcomes.

RESULTS

The prevalence of VAs increases with worsening HF. A 24-hour Holter electrocardiogram may be useful in risk stratifying patients for device therapy if they do not meet the criterion of low ventricular ejection fraction. Implantable Cardiac Defibrillators (ICDs) are superior to anti-arrhythmic drugs in reducing mortality in patients with HF. Guideline-Directed Medical Therapy (GDMT) together with device therapy may be required to reduce symptoms. In general, the proportion of seniors on GDMT is low. A combination of ICDs and cardiac resynchronization therapy may improve outcomes in selected patients.

CONCLUSION

Seniors with HF and VAs have high mortality even with the use of device therapy and GDMT. The holistic effect of device therapy on outcomes among seniors with HF is equivocal. More studies focused on seniors with advanced HF as well as therapeutic options are, therefore, required.

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.

UT-Heart: A Finite Element Model Designed for the Multiscale and Multiphysics Integration of our Knowledge on the Human Heart

To fully understand the health and pathology of the heart, it is necessary to integrate knowledge accumulated at molecular, cellular, tissue, and organ levels. However, it is difficult to comprehend the complex interactions occurring among the building blocks of biological systems across these scales. Recent advances in computational science supported by innovative high-performance computer hardware make it possible to develop a multiscale multiphysics model simulating the heart, in which the behavior of each cell model is controlled by molecular mechanisms and the cell models themselves are arranged to reproduce elaborate tissue structures. Such a simulator could be used as a tool not only in basic science but also in clinical settings. Here, we describe a multiscale multiphysics heart simulator, UT-Heart, which uses unique technologies to realize the abovementioned features. As examples of its applications, models for cardiac resynchronization therapy and surgery for congenital heart disease will be also shown.

An application of a patient-specific cardiac simulator for the prediction of outcomes after mitral valve replacement: a pilot study

Despite advancements in preoperative prediction of patient outcomes, determination of the most appropriate surgical treatments for patients with severely impaired cardiac function remains a challenge. "UT-Heart" is a multi-scale, multi-physics heart simulator, which can be used to assess the effects of treatment without imposing any burden on the patients. This retrospective study aimed to assess whether UT-Heart can function as a tool that aids decision making for performing mitral valve replacements (MVR) in patients with severe mitral regurgitation (MR) and impaired left ventricular (LV) function. We used preoperative clinical data to create a patient-specific heart model using UT-Heart for a patient who had dilated cardiomyopathy with severe MR. After confirming that this heart model reproduced the preoperative state of the patient, we performed an in silico MVR operation without changing any parameters, such as the end-diastolic volume of the left ventricle, systemic vascular resistance, and the number of myocardiocytes. Among the functional changes introduced by in silico surgery, we found two indices, forward flow and the mechanical efficiency of the work done to the systemic circulation, which may relate positively to the favorable outcome observed in the real world. Thus, multi-scale, multi-physics heart simulators can reproduce the pathophysiology of MR with impaired LV function. By performing in silico MVR and examining the resultant functional changes, we identified two indices, whose usefulness should be tested in future studies.