Analysis of lead placement optimization metrics in cardiac resynchronization therapy with computational modelling.

AIMS: The efficacy of cardiac resynchronization therapy (CRT) is known to vary considerably with pacing location, however the most effective set of metrics by which to select the optimal pacing site is not yet well understood. Computational modelling offers a powerful methodology to comprehensively test the effect of pacing location in silico and investigate how to best optimize therapy using clinically available metrics for the individual patient. METHODS AND RESULTS: Personalized computational models of cardiac electromechanics were used to perform an in silico left ventricle (LV) pacing site optimization study as part of biventricular CRT in three patient cases. Maps of response to therapy according to changes in total activation time (ΔTAT) and acute haemodynamic response (AHR) were generated and compared with preclinical metrics of electrical function, strain, stress, and mechanical work to assess their suitability for selecting the optimal pacing site. In all three patients, response to therapy was highly sensitive to pacing location, with laterobasal locations being optimal. ΔTAT and AHR were found to be correlated (ρ < -0.80), as were AHR and the preclinical activation time at the pacing site (ρ ≥ 0.73), however pacing in the last activated site did not result in the optimal response to therapy in all cases. CONCLUSION: This computational modelling study supports pacing in laterobasal locations, optimizing pacing site by minimizing paced QRS duration and pacing in regions activated late at sinus rhythm. Results demonstrate information content is redundant using multiple preclinical metrics. Of significance, the correlation of AHR with ΔTAT indicates that minimization of QRSd is a promising metric for optimization of lead placement.

The relative role of patient physiology and device optimisation in cardiac resynchronisation therapy: A computational modelling study.

Cardiac resynchronisation therapy (CRT) is an established treatment for heart failure, however the effective selection of patients and optimisation of therapy remain controversial. While extensive research is ongoing, it remains unclear whether improvements in patient selection or therapy planning offers a greater opportunity for the improvement of clinical outcomes. This computational study investigates the impact of both physiological conditions that guide patient selection and the optimisation of pacing lead placement on CRT outcomes. A multi-scale biophysical model of cardiac electromechanics was developed and personalised to patient data in three patients. These models were separated into components representing cardiac anatomy, pacing lead location, myocardial conductivity and stiffness, afterload, active contraction and conduction block for each individual, and recombined to generate a cohort of 648 virtual patients. The effect of these components on the change in total activation time of the ventricles (ΔTAT) and acute haemodynamic response (AHR) was analysed. The pacing site location was found to have the largest effect on ΔTAT and AHR. Secondary effects on ΔTAT and AHR were found for functional conduction block and cardiac anatomy. The simulation results highlight a need for a greater emphasis on therapy optimisation in order to achieve the best outcomes for patients.

The impact of beat-to-beat variability in optimising the acute hemodynamic response in cardiac resynchronisation therapy.

BACKGROUND: Acute indicators of response to cardiac resynchronisation therapy (CRT) are critical for developing lead optimisation algorithms and evaluating novel multi-polar, multi-lead and endocardial pacing protocols. Accounting for beat-to-beat variability in measures of acute haemodynamic response (AHR) may help clinicians understand the link between acute measurements of cardiac function and long term clinical outcome. METHODS AND RESULTS: A retrospective study of invasive pressure tracings from 38 patients receiving an acute pacing and electrophysiological study was performed. 602 pacing protocols for left ventricle (LV) (n = 38), atria-ventricle (AV) (n = 9), ventricle-ventricle (VV) (n = 12) and endocardial (ENDO) (n = 8) optimisation were performed. AHR was measured as the maximal rate of LV pressure development (dP/dtMx) for each beat. The range of the 95% confidence interval (CI) of mean AHR was ~ 7% across all optimisation protocols compared with the reported CRT response cut off value of 10%. A single clear optimal protocol was identifiable in 61%, 22%, 25% and 50% for LV, AV, VV and ENDO optimisation cases, respectively. A level of service (LOS) optimisation that aimed to maximise the expected AHR 5th percentile, minimising variability and maximising AHR, led to distinct optimal protocols from conventional mean AHR optimisation in 34%, 78%, 67% and 12.5% of LV, AV, VV and ENDO optimisation cases, respectively. CONCLUSION: The beat-to-beat variation in AHR is significant in the context of CRT cut off values. A LOS optimisation offers a novel index to identify the optimal pacing site that accounts for both the mean and variation of the baseline measurement and pacing protocol.

An automatic service for the personalization of ventricular cardiac meshes.

Computational cardiac physiology has great potential to improve the management of cardiovascular diseases. One of the main bottlenecks in this field is the customization of the computational model to the anatomical and physiological status of the patient. We present a fully automatic service for the geometrical personalization of cardiac ventricular meshes with high-order interpolation from segmented images. The method is versatile (able to work with different species and disease conditions) and robust (fully automatic results fulfilling accuracy and quality requirements in 87% of 255 cases). Results also illustrate the capability to minimize the impact of segmentation errors, to overcome the sparse resolution of dynamic studies and to remove the sometimes unnecessary anatomical detail of papillary and trabecular structures. The smooth meshes produced can be used to simulate cardiac function, and in particular mechanics, or can be used as diagnostic descriptors of anatomical shape by cardiologists. This fully automatic service is deployed in a cloud infrastructure, and has been made available and accessible to the scientific community.

Quality metrics for high order meshes: analysis of the mechanical simulation of the heart beat.

The quality of a computational mesh is an important characteristic for stable and accurate simulations. Quality depends on the regularity of the initial mesh, and in mechanical simulations it evolves in time, with deformations causing changes in volume and distortion of mesh elements. Mesh quality metrics are therefore relevant for both mesh personalization and the monitoring of the simulation process. This work evaluates the significance, in meshes with high order interpolation, of four quality metrics described in the literature, applying them to analyse the stability of the simulation of the heart beat. It also investigates how image registration and mesh warping parameters affect the quality and stability of meshes. Jacobian-based metrics outperformed or matched the results of coarse geometrical metrics of aspect ratio or orthogonality, although they are more expensive computationally. The stability of simulations of a complete heart cycle was best predicted with a specificity of 61%, sensitivity of 85%, and only nominal differences were found changing the intra-element and per-element combination of quality values. A compromise between fitting accuracy and mesh stability and quality was found. Generic geometrical quality metrics have a limited success predicting stability, and an analysis of the simulation problem may be required for an optimal definition of quality.