Affiliation:
1. School of Nursing, University of Texas-Houston Health
Science Center at Houston, Houston, Texas,
2. Duke University Medical Center
3. Neurosurgery and Neurobiology, Duke University Medical
Center Durham, North Carolina
Abstract
Cerebral compliance is a measure of cerebral adaptability to increases in volume within the intracranial space and an indicator of risk for neurological deterioration. However, no direct measurement of compliance exists in clinical practice to guide nursing care or treatment decisions. Current use of mean intracranial pressure (MICP) and gross morphological intracranial pressure waveform (ICPW) analysis have great variability in predicting outcomes. The purpose of this review and pilot study was to evaluate the effects of suctioning on MICP and other measures estimating cerebral compliance derived from analysis of ICPW on patient outcome. We analyzed arterial blood pressure waveforms (ABPWs), ICPWs, and respiratory cycle variations using Fourier Transform analysis, to explore the potential benefits of studying ICPWs across single cardiac and respiratory cycles using linear modeling and calculation of correlation coefficients. ABPWs, ICPWs, and MICP were measured over individual cardiac cycles across multiple respiratory phases in five critically ill neurological patients. Both direct and derived ICP measures, including Fourier analysis of ABP and ICP and the cross-transform between ABP and ICP, were correlated with patient outcome. This more complex waveform analysis of individual ABPW and ICPW together, and derived measures during both single cardiac and respiratory cycles, may provide information relevant to cerebral compliance and patient outcomes. Pending confirmation with additional data sets, this technique may be a useful real-time clinical tool to provide a measure of compliance and risk of neurological deterioration for clinicians.
Cited by
16 articles.
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