COHmax: an algorithm to maximise coherence in estimates of dynamic cerebral autoregulation.
Panerai RB., Intharakham K., Minhas JS., Llwyd O., Salinet ASM., Katsogridakis E., Maggio P., Robinson TG.
ObjectiveThe 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.ApproachHealthy 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 resultsIn 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.SignificanceCOHmax 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.