Critical Appraisal of Electrogram Re-Annotation by Global Vectorial Analysis
To solve the problem of assigning activation times for multi-component electrograms the algorithm identifies all possible markings, then selects that which best reconciles global activation (i.e. provides the lowest spatial and temporal errors). This is an elegant approach. However, it is not fully clear how selection is performed. If the algorithm uses rules (equations), their accuracy should ideally be reported for different types of rhythm and patient profile. If the algorithm uses more complex classifications including machine learning, the robustness of training labels is critical5. Global vectors are sensitive to regions that are over- or under-sampled, and so signal acquisition should be as spatially complete and uniform as possible. In theory, algorithmic selection could suppress potentially important mapping solutions, although such errors were not reported in this study. By optimizing maps globally, localized patterns with a small global impact such as micro-reentry with a small organized domain, could also be overlooked. The micro-reentry shown in figure 14 controls relatively large atrial areas, and it would be fascinating to study the sensitivity of this approach to smaller micro-reentrant domains. Extending this logic, it remains to be studied if the global vectorial approach could identify organized driver sites in AF, which may have local control yet a small impact on global activation vectors, and for which several algorithms are in clinical testing6-8.