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

Ronney B. Panerai; Kannakorn Intharakham; Victoria Haunton; Jatinder S. Minhas; Osian Llwyd; Manda Lam; Angela S.M. Salinet; Ricardo C. Nogueira; Emmanuel Katsogridakis; Paola Maggio; Thompson G. Robinson

doi:10.1088/1361-6579/ab7ddf

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

Ronney B. Panerai^*, Kannakorn Intharakham, Victoria Haunton, Jatinder S. Minhas, Osian Llwyd, Manda Lam, Angela S.M. Salinet, Ricardo C. Nogueira, Emmanuel Katsogridakis, Paola Maggio, Thompson G. Robinson

^*Corresponding author for this work

Peninsula Medical School

Research output: Contribution to journal › Article › peer-review

Abstract

Abstract 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 5 min. 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 = 100 s, 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 = 50 s and 25 s. Using SD = 100 s 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 = 100 s, to explore possibilities of improving the reproducibility, sensitivity and prognostic value of TFA parameters used as metrics of dCA.

Original language	English
Pages (from-to)	035006-035006
Number of pages	0
Journal	Physiological Measurement
Volume	41
Issue number	3
DOIs	https://doi.org/10.1088/1361-6579/ab7ddf
Publication status	Published - 1 Mar 2020

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@article{97df06b8aae94552900c95223d70baef,

title = "Chasing the evidence: the influence of data segmentation on estimates of dynamic cerebral autoregulation",

abstract = "Abstract 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 5 min. 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 = 100 s, 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 = 50 s and 25 s. Using SD = 100 s 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 = 100 s, to explore possibilities of improving the reproducibility, sensitivity and prognostic value of TFA parameters used as metrics of dCA.",

author = "Panerai, {Ronney B.} and Kannakorn Intharakham and Victoria Haunton and Minhas, {Jatinder S.} and Osian Llwyd and Manda Lam and Salinet, {Angela S.M.} and Nogueira, {Ricardo C.} and Emmanuel Katsogridakis and Paola Maggio and Robinson, {Thompson G.}",

year = "2020",

month = mar,

day = "1",

doi = "10.1088/1361-6579/ab7ddf",

language = "English",

volume = "41",

pages = "035006--035006",

journal = "Physiological Measurement",

issn = "0967-3334",

publisher = "IOP Publishing Ltd.",

number = "3",

}

TY - JOUR

T1 - Chasing the evidence: the influence of data segmentation on estimates of dynamic cerebral autoregulation

AU - Panerai, Ronney B.

AU - Intharakham, Kannakorn

AU - Haunton, Victoria

AU - Minhas, Jatinder S.

AU - Llwyd, Osian

AU - Lam, Manda

AU - Salinet, Angela S.M.

AU - Nogueira, Ricardo C.

AU - Katsogridakis, Emmanuel

AU - Maggio, Paola

AU - Robinson, Thompson G.

PY - 2020/3/1

Y1 - 2020/3/1

N2 - Abstract 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 5 min. 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 = 100 s, 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 = 50 s and 25 s. Using SD = 100 s 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 = 100 s, to explore possibilities of improving the reproducibility, sensitivity and prognostic value of TFA parameters used as metrics of dCA.

AB - Abstract 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 5 min. 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 = 100 s, 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 = 50 s and 25 s. Using SD = 100 s 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 = 100 s, to explore possibilities of improving the reproducibility, sensitivity and prognostic value of TFA parameters used as metrics of dCA.

U2 - 10.1088/1361-6579/ab7ddf

DO - 10.1088/1361-6579/ab7ddf

M3 - Article

SN - 0967-3334

VL - 41

SP - 35006

EP - 35006

JO - Physiological Measurement

JF - Physiological Measurement

IS - 3

ER -

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

Abstract

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