COHmax: an algorithm to maximise coherence in estimates of dynamic cerebral autoregulation.

OBJECTIVE: The reliability of dynamic cerebral autoregulation (dCA) parameters, obtained with transfer function analysis (TFA) of spontaneous fluctuations in arterial blood pressure (BP), require statistically significant values of the coherence function. A new algorithm (COHmax) is proposed to increase values of coherence by means of the automated, selective removal of sub-segments of data. APPROACH: Healthy subjects were studied at baseline (normocapnia) and during 5% breathing of CO2 (hypercapnia). BP (Finapres), cerebral blood flow velocity (CBFV, transcranial Doppler), end-tidal CO2 (EtCO2, capnography) and heart rate (ECG) were recorded continuously during 5 min in each condition. TFA was performed with sub-segments of data of duration (SEGD) 100 s, 50 s or 25 s and the autoregulation index (ARI) was obtained from the CBFV response to a step change in BP. The area-under-the curve (AUC) was obtained from the receiver-operating characteristic (ROC) curve for the detection of changes in dCA resulting from hypercapnia. MAIN RESULTS: In 120 healthy subjects (69 male, age range 20-77 years), CO2 breathing was effective in changing mean EtCO2 and CBFV (p < 0.001). For SEGD = 100 s, ARI changed from 5.8 ± 1.4 (normocapnia) to 4.0 ± 1.7 (hypercapnia, p < 0.0001), with similar differences for SEGD = 50 s or 25 s. Depending on the value of SEGD, in normocapnia, 15.8% to 18.3% of ARI estimates were rejected due to poor coherence, with corresponding rates of 8.3% to 13.3% in hypercapnia. With increasing coherence, 36.4% to 63.2% of these could be recovered in normocapnia (p < 0.001) and 50.0% to 83.0% in hypercapnia (p < 0.005). For SEGD = 100 s, ROC AUC was not influenced by the algorithm, but it was superior to corresponding values for SEGD = 50 s or 25 s. SIGNIFICANCE: COHmax has the potential to improve the yield of TFA estimates of dCA parameters, without introducing a bias or deterioration of their ability to detect impairment of autoregulation. Further studies are needed to assess the behaviour of the algorithm in patients with different cerebrovascular conditions.