Can cerebral autoregulation be monitored with non-linear correlation indices? A proof-of-concept study

Objective Cerebral autoregulation (CA) is the mechanism by which cerebral vessels self-regulate blood flow to maintain adequate brain perfusion. Although CA monitoring is recommended in neurocritical care as a means to improve therapy, there is currently no consensus on the most effective computational technique. The aim of this study was to evaluate the feasibility of measuring CA using recurrence quantification analysis (RQA)—a non-linear method for analysing complex systems—as an alternative to traditional, linear correlation-based indices. Methods A retrospective analysis was conducted on a database of head-injured patients treated in a neurocritical care unit who developed spontaneous intracranial pressure (ICP) elevations known as plateau waves. Signals from arterial blood pressure, ICP, and cerebral blood flow velocity were segmented into stable phases of elevated ICP (associated with CA deterioration) and the preceding baseline. Non-linear RQA correlations were calculated for both periods, and the results were statistically compared. For reference, well-established linear CA indices were assessed in the same manner. Results The non-linear correlations followed a similar pattern to the linear CA indices and successfully differentiated the plateau wave phase with a comparable, high level of statistical significance (p < 0.0001), while demonstrating higher values and reduced data dispersion. Conclusion Non-linear RQA correlation is feasible for CA assessment. To the best of the authors’ knowledge, this is the first evaluation of RQA feasibility in CA research. In the pursuit of a reliable index suitable for widespread implementation in neurocritical care, a non-linear approach may offer a promising alternative to traditional linear correlation-based indices.