Assessment of cerebral autoregulation: the quandary of quantification.

We assessed the convergent validity of commonly applied metrics of cerebral autoregulation (CA) to determine the extent to which the metrics can be used interchangeably. To examine between-subject relationships among low-frequency (LF; 0.07-0.2 Hz) and very-low-frequency (VLF; 0.02-0.07 Hz) transfer function coherence, phase, gain, and normalized gain, we performed retrospective transfer function analysis on spontaneous blood pressure and middle cerebral artery blood velocity recordings from 105 individuals. We characterized the relationships (n = 29) among spontaneous transfer function metrics and the rate of regulation index and autoregulatory index derived from bilateral thigh-cuff deflation tests. In addition, we analyzed data from subjects (n = 29) who underwent a repeated squat-to-stand protocol to determine the relationships between transfer function metrics during forced blood pressure fluctuations. Finally, data from subjects (n = 16) who underwent step changes in end-tidal P(CO2) (P(ET)(CO2) were analyzed to determine whether transfer function metrics could reliably track the modulation of CA within individuals. CA metrics were generally unrelated or showed only weak to moderate correlations. Changes in P(ET)(CO2) were positively related to coherence [LF: ß = 0.0065 arbitrary units (AU)/mmHg and VLF: ß = 0.011 AU/mmHg, both P < 0.01] and inversely related to phase (LF: ß = -0.026 rad/mmHg and VLF: ß = -0.018 rad/mmHg, both P < 0.01) and normalized gain (LF: ß = -0.042%/mmHg(2) and VLF: ß = -0.013%/mmHg(2), both P < 0.01). However, Pet(CO(2)) was positively associated with gain (LF: ß = 0.0070 cm·s(-1)·mmHg(-2), P < 0.05; and VLF: ß = 0.014 cm·s(-1)·mmHg(-2), P < 0.01). Thus, during changes in P(ET)(CO2), LF phase was inversely related to LF gain (ß = -0.29 cm·s(-1)·mmHg(-1)·rad(-1), P < 0.01) but positively related to LF normalized gain (ß = 1.3% mmHg(-1)/rad, P < 0.01). These findings collectively suggest that only select CA metrics can be used interchangeably and that interpretation of these measures should be done cautiously.

Monitoring non-invasive cardiac output and stroke volume during experimental human hypovolaemia and resuscitation.

BACKGROUND: Multiple methods for non-invasive measurement of cardiac output (CO) and stroke volume (SV) exist. Their comparative capabilities are not clearly established. METHODS: Healthy human subjects (n=21) underwent central hypovolaemia through progressive lower body negative pressure (LBNP) until the onset of presyncope, followed by termination of LBNP, to simulate complete resuscitation. Measurement methods were electrical bioimpedance (EBI) of the thorax and three measurements of CO and SV derived from the arterial blood pressure (ABP) waveform: the Modelflow (MF) method, the long-time interval (LTI) method, and pulse pressure (PP). We computed areas under receiver-operating characteristic curves (ROC AUCs) for the investigational metrics, to determine how well they discriminated between every combination of LBNP levels. RESULTS: LTI and EBI yielded similar reductions in SV during progressive hypovolaemia and resuscitation (correlation coefficient 0.83) with ROC AUCs for distinguishing major LBNP (-60 mm Hg) vs resuscitation (0 mm Hg) of 0.98 and 0.99, respectively. MF yielded very similar reductions and ROC AUCs during progressive hypovolaemia, but after resuscitation, MF-CO did not return to baseline, yielding lower ROC AUCs (ΔROC AUC range, -0.18 to -0.26, P < 0.01). PP declined during hypovolaemia but tended to be an inferior indicator of specific LBNP levels, and PP did not recover during resuscitation, yielding lower ROC curves (P < 0.01). CONCLUSIONS: LTI, EBI, and MF were able to track progressive hypovolaemia. PP decreased during hypovolaemia but its magnitude of reduction underestimated reductions in SV. PP and MF were inferior for the identification of resuscitation.