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Chasing the evidence: the influence of data segmentation on estimates of dynamic cerebral autoregulation.

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posted on 2020-04-02, 08:35 authored by Ronney B Panerai, Kannakorn Intharakham, Victoria J Haunton, Jatinder Singh Minhas, Osian Llwyd, Manda Lam, Angela SM Salinet, Ricardo C Nogueira, Emmanuel Katsogridakis, Paola Maggio, Thompson G Robinson
OBJECTIVE: Transfer function analysis (TFA) of dynamic cerebral autoregulation (dCA) requires smoothing of spectral estimates using segmentation of the data (SD). Systematic studies are required to elucidate the potential influence of SD on dCA parameters. APPROACH: Healthy subjects (HS, n=237) and acute ischaemic stroke patients (AIS, n=98) were included. Cerebral blood flow velocity (CBFV, transcranial Doppler ultrasound) was recorded supine at rest with continuous arterial blood pressure (BP, Finometer) for a minimum of five minutes. TFA was performed with durations SD = 100, 50 or 25 s and 50% superposition to derive estimates of coherence, gain and phase for the BP-CBFV relationship. The autoregulation index (ARI) was estimated from the CBFV step response. Intrasubject reproducibility was expressed by the intraclass correlation coefficient (ICC). MAIN RESULTS: In HS, the ARI, coherence, gain, and phase (low frequency) were influenced by SD, but in AIS, phase (very low frequency) and ARI were not affected. ICC was excellent (>0.75) for all parameters, for both HS and AIS. For SD=100s, ARI was different between HS and AIS (mean ± sdev: 5.70 ± 1.61 vs 5.1 ± 2.0; p<0.01) and the significance of this difference was maintained for SD = 50s and 25s. Using SD = 100s as reference, the rate of misclassification, based on a threshold of ARI ≤ 4, was 6.3% for SD = 50 s and 8.1% for SD = 25 s in HS, with corresponding values of 11.7% and 8.2% in AIS patients, respectively. SIGNIFICANCE: Further studies are warranted with SD values lower than the recommended standard of SD=100s, to explore possibilities of improving the reproducibility, sensitivity and prognostic value of TFA parameters used as metrics of dCA.

Funding

Supported by EPSRC grant EP/K041207/1. KI is supported by a PhD scholarship of the Ministry of Science and Technology, Royal Thai Government. We are grateful to Dr Christopher Nelson (Department of Cardiovascular Sciences, University of Leicester, UK) for statistical advice. Professor Robinson is a National Institute for Health Research (NIHR, UK) Senior Investigator.

History

Citation

Physiological Measurement (2020)

Version

  • AM (Accepted Manuscript)

Published in

Physiological Measurement

Publisher

IOP Publishing

issn

0967-3334

eissn

1361-6579

Acceptance date

2020-03-09

Copyright date

2020

Available date

2020-03-09

Publisher version

https://iopscience.iop.org/article/10.1088/1361-6579/ab7ddf

Spatial coverage

England

Language

eng

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