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Between-centre variability in transfer function analysis, a widely used method for linear quantification of the

Aisha S S Meel-van den Abeelen1, David M Simpson2, Lotte J Y Wang1

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Summary

Transfer function analysis (TFA) for cerebral autoregulation (CA) shows high variability between research centers. Standardizing TFA methods is crucial for reliable comparisons and accurate assessment of dynamic CA.

Keywords:
Cerebral autoregulationMethod comparisonStandardisationTransfer function analysis

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Physiology

Background:

  • Dynamic cerebral autoregulation (CA) is assessed using transfer function analysis (TFA) of blood pressure (BP) and cerebral blood flow velocity (CBFV) oscillations.
  • Existing research exhibits significant variability in TFA methodologies and interpretations across different study groups.

Purpose of the Study:

  • To quantify the between-centre variability in transfer function analysis (TFA) outcome metrics for dynamic cerebral autoregulation (CA).
  • To identify specific TFA settings contributing to outcome variations and to propose standardization recommendations.

Main Methods:

  • Fifteen research centres analyzed identical datasets (70 real, 10 synthetic) of BP and CBFV recordings from healthy subjects under resting and hypercapnic conditions.
  • Each centre employed their standard TFA protocols, with some parameters pre-specified to minimize variability.
  • Statistical analysis included Mann-Whitney tests and logistic regression to assess discriminatory performance and identify influential settings.

Main Results:

  • A substantial and heterogeneous variation in TFA outcome metrics was observed across participating centres.
  • Logistic regression indicated that 11 centres achieved an AUC > 0.85, successfully distinguishing between normal and impaired CA.
  • Specific TFA parameter settings were identified as significant contributors to the observed outcome variability.

Conclusions:

  • Significant between-centre variability exists in TFA for assessing dynamic CA, hindering consistent interpretation.
  • Standardization of TFA methods and signal processing is essential to improve reliability and comparability across studies.
  • The findings provide a basis for developing standardized protocols for TFA in CA research.