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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

Background Cardiac resynchronization therapy (CRT) with biventricular epicardial (BV‐CS) or endocardial left ventricular (LV) stimulation (BV‐EN) improves LV hemodynamics. The effect of CRT on right ventricular function is less clear, particularly for BV‐EN. Our objective was to compare the simultaneous acute hemodynamic response (AHR) of the right and left ventricles (RV and LV) with BV‐CS and BV‐EN in order to determine the optimal mode of CRT delivery. Methods Nine patients with previously implanted CRT devices successfully underwent a temporary pacing study. Pressure wires measured the simultaneous AHR in both ventricles during different pacing protocols. Conventional epicardial CRT was delivered in LV‐only (LV‐CS) and BV‐CS configurations and compared with BV‐EN pacing in multiple locations using a roving decapolar catheter. Results Best BV‐EN (optimal AHR of all LV endocardial pacing sites) produced a significantly greater RV AHR compared with LV‐CS and BV‐CS pacing (P < 0.05). RV AHR had a significantly increased standard deviation compared to LV AHR (P < 0.05) with a weak correlation between RV and LV AHR (Spearman rs = −0.06). Compromised biventricular optimization, whereby RV AHR was increased at the expense of a smaller decrease in LV AHR, was achieved in 56% of cases, all with BV‐EN pacing. Conclusions BV‐EN pacing produces significant increases in both LV and RV AHR, above that achievable with conventional epicardial pacing. RV AHR cannot be used as a surrogate for optimizing LV AHR; however, compromised biventricular optimization is possible. The beneficial effect of endocardial LV pacing on RV function may have important clinical benefits beyond conventional CRT.

Myocardial stiffness is a clinical biomarker used to diagnose and stratify diseases such as heart failure. This biomechanical property can be inferred from the personalisation of computational cardiac models to clinical measures. Nevertheless, previous attempts have been unable to determine a unique set of material constitutive parameters. In this study we address this shortcoming by proposing a new cost function that allows us to uncouple key parameters and uniquely describe passive material properties in patients from available clinical data.