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.