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

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 (COH_max) is proposed to increase values of coherence by means of the automated, selective removal of sub-segments of data.

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 (SEG_D) 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.

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 SEG_D = 100 s, ARI changed from 5.8 ± 1.4 (normocapnia) to 4.0 ± 1.7 (hypercapnia, p < 0.0001), with similar differences for SEG_D = 50 s or 25 s. Depending on the value of SEG_D, 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 SEG_D = 100 s, ROC AUC was not influenced by the algorithm, but it was superior to corresponding values for SEG_D = 50 s or 25 s.

COH_max 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.