Continuous estimates of dynamic cerebral autoregulation: influence of non-invasive arterial blood pressure measurements

Temporal variability of parameters which describe dynamic cerebral autoregulation (CA), usually quantified by the short-term relationship between arterial blood pressure (BP) and cerebral blood flow velocity (CBFV), could result from continuous adjustments in physiological regulatory mechanisms or could be the result of artefacts in methods of measurement, such as the use of non-invasive measurements of BP in the finger. In 27 subjects (61 ± 11 years old) undergoing coronary artery angioplasty, BP was continuously recorded at rest with the Finapres device and in the ascending aorta (Millar catheter, BPAO), together with bilateral transcranial Doppler ultrasound in the middle cerebral artery, surface ECG and transcutaneous CO2. Dynamic CA was expressed by the autoregulation index (ARI), ranging from 0 (absence of CA) to 9 (best CA). Time-varying, continuous estimates of ARI (ARI(t)) were obtained with an autoregressive moving-average (ARMA) model applied to a 60 s sliding data window. No significant differences were observed in the accuracy and precision of ARI(t) between estimates derived from the Finapres and BPAO. Highly significant correlations were obtained between ARI(t) estimates from the right and left middle cerebral artery (MCA) (Finapres r = 0.60 ± 0.20; BPAO r = 0.56 ± 0.22) and also between the ARI(t) estimates from the Finapres and BPAO (right MCA r = 0.70 ± 0.22; left MCA r = 0.74 ± 0.22). Surrogate data showed that ARI(t) was highly sensitive to the presence of noise in the CBFV signal, with both the bias and dispersion of estimates increasing for lower values of ARI(t). This effect could explain the sudden drops of ARI(t) to zero as reported previously. Simulated sudden changes in ARI(t) can be detected by the Finapres, but the bias and variability of estimates also increase for lower values of ARI. In summary, the Finapres does not distort time-varying estimates of dynamic CA obtained with a sliding window combined with an ARMA model, but further research is needed to confirm these findings in healthy subjects and to assess the influence of different physiological manoeuvre

Objective selection of signals for assessment of cerebral blood flow autoregulation in neonates

A number of different system identification techniques have been proposed to assess dynamic cerebral autoregulation in critically ill patients. From these methods, the response to a standard stepwise change in blood pressure can be estimated. Responses lacking physiological consistency are a common occurrence and could be the consequence of particular system identification procedures or, alternatively, caused by measurements with a poor signal-to-noise ratio. A multi-observer approach was adopted in this paper to classify cerebral blood flow velocity (CBFV) step responses to spontaneous changes in arterial blood pressure in a group of 43 neonates with a mean gestational age of 33.7 weeks (range 24–42 weeks) and a mean birthweight of 1980 g (range 570–3910 g). Three experienced observers independently analysed the estimated step responses in 191 recordings each lasting 100 s; for an autoregressive (ARX) model, 124 (65%) of the step responses were accepted by at least two of the three observers. Two other system identification methods, transfer function analysis and the moving average Wiener–Laguerre model, gave 90 (45%) and 98 (51%) acceptable responses, respectively. Only 54 epochs (28%) were accepted with all three methods. With 88 (46%) responses rejected by at least two methods, it can be concluded that signal quality was the main reason for nonphysiological step responses. To avoid the need for subjective visual selection, an automatic procedure for classifying step responses was implemented leading to sensitivities and specificities in the range 85–90%, with respect to the agreement with subjective evaluations. Objective selection of CBFV step responses is thus feasible and could also be adapted for other physiological measurement techniques relying on system identification methods