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Background: Ventricular tachycardia (VT) is a life-threatening heart condition commonly seen in patients with myocardial infarction (MI). Although personalized computational modeling has been used to understand VT and its treatment noninvasively, this approach can be computationally intensive and time consuming. Therefore, finding a balance between mesh size and computational efficiency is important. This study aimed to find an optimal mesh resolution that minimizes the need for computational resources while maintaining numerical accuracy and to investigate the effect of mesh resolution variation on the simulation results. Methods: We constructed ventricular models from contrast-enhanced magnetic resonance imaging data from six patients with MI. We created seven different models for each patient, with average edge lengths ranging from 315 to 645 µm using commercial software, Mimics. Programmed electrical stimulation was used to assess VT inducibility from 19 sites in each heart model. Results: The simulation results in the slab model with adaptive tetrahedral mesh (same as in the patient-specific model) showed that the absolute and relative differences in conduction velocity (CV) were 6.1 cm/s and 7.8% between average mesh sizes of 142 and 600 µm, respectively. However, the simulation results in the six patient-specific models showed that average mesh sizes with 350 µm yielded over 85% accuracy for clinically relevant VT. Although average mesh sizes of 417 and 478 µm could also achieve approximately 80% accuracy for clinically relevant VT, the percentage of incorrectly predicted VTs increases. When conductivity was modified to match the CV in the model with the finest mesh size, the overall ratio of positively predicted VT increased. Conclusions: The proposed personalized heart model could achieve an optimal balance between simulation time and VT prediction accuracy when discretized with adaptive tetrahedral meshes with an average edge length about 350 µm.

Evaluation of the effects of alirocumab on cardiovascular (CV) events, CV mortality and all-cause mortality. Data search was carried out using the Cochrane Library, PubMed, Web of Science and Embase. The search time is up to November 18, 2020. All randomized clinical trials (AEs) comparing alirocumab with placebo were searched. Meta-analysis was performed by Review Manager version 5.3 (The Cochrane Collaboration, Copenhagen, Denmark), and the heterogeneity between studies was tested by Cochrane’s Q test and measured with I2 statistics. A total of 13 randomized controlled trials with 24,815 participants were included. Alirocumab usage can considerably lower the incidence of CV events when compared to the control group (risk ratio(RR) 0.89, 95% confidence interval(CI) 0.83–0.95). No significant difference in CV mortality between the two groups was observed (RR 0.87, 95% CI 0.74–1.04). Treatment with alirocumab has been associated with a major decrease in all-cause mortality compared to placebo (RR 0.80, 95% CI 0.66–0.96). The incidence of serious adverse events (AEs) was similar in the two groups (RR 0.94, 95% CI 0.90–0.99). Alirocumab can reduce CV events and all-cause mortality. The AEs were mild and tolerable.