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

Abstract

To identify non-responders to cardiac resynchronization therapy (CRT), various biomarkers have been proposed, but these attempts have not been successful to date. We tested the clinical applicability of computer simulation of CRT for the identification of non-responders. We used the multi-scale heart simulator "UT-Heart," which can reproduce the electrophysiology and mechanics of the heart based on a molecular model of the excitation-contraction mechanism. Patient-specific heart models were created for eight heart failure patients who were treated with CRT, based on the clinical data recorded before treatment. Using these heart models, bi-ventricular pacing simulations were performed at multiple pacing sites adopted in clinical practice. Improvement in pumping function measured by the relative change of maximum positive derivative of left ventricular pressure (%ΔdP/dt max) was compared with the clinical outcome. The operators of the simulation were blinded to the clinical outcome. In six patients, the relative reduction in end-systolic volume exceeded 15% in the follow-up echocardiogram at 3 months (responders) and the remaining two patients were judged as non-responders. The simulated %ΔdP/dt max at the best lead position could identify responders and non-responders successfully. With further refinement of the model, patient-specific simulation could be a useful tool for identifying non-responders to CRT.

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

“…New artificial intellegence and ML based approaches to data analysis have been extensively used in attempts to increase the accuracy of patient differentiation (Kalscheur et al, 2018 ; Feeny et al, 2019 , 2020 ; Tokodi et al, 2021 ). Computational models based on clinical data are also employed to identify mechanisms responsible for the poor efficacy and develop approaches improving CRT outcomes (Lumens et al, 2015 ; Huntjens et al, 2018 ; Lee et al, 2018 ; Isotani et al, 2020 ). Recently, a new trend has emerged in this research area, which uses a combination of clinical and model data together with ML for solving challenging medical problems (Aronis et al, 2021 ; Heijman et al, 2021 ).…”

Machine Learning prediction of cardiac resynchronisation therapy response from combination of clinical and model-driven data

“…New artificial intellegence and ML based approaches to data analysis have been extensively used in attempts to increase the accuracy of patient differentiation [7,9,13,12]. Computational models based on clinical data are also employed to identify mechanisms responsible for the poor efficacy and develop approaches improving CRT outcomes [14,34,35,36]. Recently, a new trend has emerged in this research area, which uses a combination of clinical and model data together with ML for solving challenging medical problems [37,38].…”

“…This opens up a further direction for studies using electromechanical models of cardiac activity which are being developed in modeling community including our group [34,52,53] and which are able to predict directly EF, dP/dtmax changes and other mechanical biomarkers of CRT response. Such models were already used for clinical data analysis in CRT patients by several groups [34,54,14,55] , demonstrating the power of such simulations for CRT response predictions. In particular, we believe that reduced mechanical models using regression or ML approaches to reproduce the behaviour of complex 3D models such as developed with our participation [56] would be the the best choice in terms of possible clinical application of model simulations.…”

“…However, the goal of CRT implantation is the synchronisation of ventricular contraction and subsequent improvement in the mechanical performance of the ventricles. This opens up a further direction for studies using electromechanical models of cardiac activity which are being developed in modeling community including our group [34,52,53] and which are able to predict directly EF, dP/dtmax changes and other mechanical biomarkers of CRT response. Such models were already used for clinical data analysis in CRT patients by several groups [34,54,14,55] , demonstrating the power of such simulations for CRT response predictions.…”

“…maximal narrowing of QRSd, or minimisation of interventricular dyssynchrony; maximal proximity to the late activation zone or late contraction zone; avoiding the match with infarct zone; or maximizing the mechanical performance characteristics, e.g. dP/dtmax [40,58,60,34]. The use of simulations from personalized models in CRT response prediction opens the possibility to re-evaluate these hypotheses and suggest a new strategy for implantation planning with allowing for model-based prediction of optimal location for pacing electrodes.…”