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Association Between the Cerebral Autoregulation Index (Pressure Reactivity), Patient’s Clinical Outcome, and Quality of ABP(t) and ICP(t) Signals for CA Monitoring

Authors
  • Bajpai, Basant K.1
  • Preiksaitis, Aidanas2
  • Vosylius, Saulius3
  • Rocka, Saulius2
  • 1 Health Telematics Science Institute, Kaunas University of Technology, LT-51423 Kaunas, Lithuania
  • 2 Centre of Neurosurgery, Clinic of Neurology and Neurosurgery, Faculty of Medicine, Vilnius University, LT08661 Vilnius, Lithuania
  • 3 Clinic of Anesthesiology and Intensive Care, Institute of Clinical Medicine, Faculty of Medicine, Vilnius University, LT08661 Vilnius, Lithuania
Type
Published Article
Journal
Medicina
Publisher
MDPI
Publication Date
Mar 20, 2020
Volume
56
Issue
3
Identifiers
DOI: 10.3390/medicina56030143
PMID: 32245122
PMCID: PMC7143400
Source
PubMed Central
Keywords
License
Green

Abstract

Background and Objectives : The aim of this study was to explore the association between the cerebral autoregulation (CA) index, the pressure reactivity index (PRx), the patient’s clinical outcome, and the quality of arterial blood pressure (ABP(t)) and intracranial blood pressure (ICP(t)) signals by comparing two filtering methods to derive the PRx. Materials and Methods : Data from 60 traumatic brain injury (TBI) patients were collected. Moving averaging and FIR (Finite Impulse Response) filtering were performed on the ABP(t) and ICP(t) signals, and the PRx was estimated from both filtered datasets. Sensitivity, specificity, and receiver-operating characteristic (ROC) curves with the area under the curves (AUCs) were determined using patient outcomes as a reference. The outcome chosen for comparison among the two filtering methods were mortality and survival. Results : The FIR filtering approach, compared with clinical outcome, had a sensitivity of 70%, a specificity of 81%, and a level of significance p = 0.001 with an area under the curve (AUC) of 0.78. The moving average filtering method compared with the clinical outcome had a sensitivity of 58%, a specificity of 72%, and a level of significance p = 0.054, with an area under the curve (AUC) of 0.66. Conclusions : The FIR (optimal) filtering approach was found to be more sensitive for discriminating between two clinical outcomes, namely intact (survival) and impaired (death) cerebral autoregulation for TBI treatment decision making.

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