Differences in analytical approach during conduction velocity analysis of optically mapped hearts affect reported data and conclusions.

BACKGROUND: Slowed cardiac conduction velocity (CV) is a substrate for arrhythmia. Reported CV values from optically mapped whole-heart preparations vary widely in the literature, suggesting that analytical variability may contribute to inconsistency. OBJECTIVE: To determine how variation in vector inclusion criteria affects conduction velocity measurements and their interpretation, and to establish a principled, constraint-based approach for evaluating parameter sensitivity in the absence of ground truth. METHODS: CV and anisotropic ratio (AR) were quantified from optically mapped, paced Langendorff-perfused guinea pig hearts under four interventions: time control (N = 7), gap junctional uncoupling (carbenoxolone, N = 5), sodium channel inhibition (flecainide, N = 7), and ephaptic disruption (mannitol, N = 5). A semi-automated algorithm evaluated 441 combinations of dilation and angle. Paired t-tests compare baseline and intervention values across parameter combinations. Inclusion criteria were applied to determine parameter combinations constrained by minimal processing errors and consistency with controls. RESULTS: Manual analysis produced significant inter-analyst variability, whereas the semi-automated approach eliminated analyst dependence. Increasing dilation or angle increased transverse CV (CVT) and decreased longitudinal CV (CVL), often altering AR and affecting statistical significance of intervention effects. Parameter selection determined whether anisotropic conduction slowing was significant. Parameter combinations meeting inclusion criteria were identified which minimized processing errors, produced significant differences between intervention and control and eliminated analyst variability. CONCLUSION: Vector inclusion parameters substantially influence reported conduction velocity and anisotropy, and can alter statistical conclusions. A constraint-based, parsimonious approach to parameter selection improves robustness and transparency without invoking optimization, providing a framework adaptable to other analytical workflows lacking ground truth.