Reproducibility of dynamic cerebral autoregulation parameters

A multi-centre, multi-method study

Marit L. Sanders, Jurgen A.H.R. Claassen, Marcel Aries, Edson Bor-Seng-Shu, Alexander Caicedo, Max Chacon, Erik D. Gommer, Sabine Van Huffel, José L. Jara, Kyriaki Kostoglou, Adam Mahdi, Vasilis Z. Marmarelis, Georgios D. Mitsis, Martin Müller, Dragana Nikolic, Ricardo C. Nogueira, Stephen J. Payne, Corina Puppo, Dae C. Shin, David M. Simpson & 5 others Takashi Tarumi Ph.D., Bernardo Yelicich, Rong Zhang, Ronney B. Panerai, Jan Willem J. Elting

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

Objective: Different methods to calculate dynamic cerebral autoregulation (dCA) parameters are available. However, most of these methods demonstrate poor reproducibility that limit their reliability for clinical use. Inter-centre differences in study protocols, modelling approaches and default parameter settings have all led to a lack of standardisation and comparability between studies. We evaluated reproducibility of dCA parameters by assessing systematic errors in surrogate data resulting from different modelling techniques. Approach: Fourteen centres analysed 22 datasets consisting of two repeated physiological blood pressure measurements with surrogate cerebral blood flow velocity signals, generated using Tiecks curves (autoregulation index, ARI 0-9) and added noise. For reproducibility, dCA methods were grouped in three broad categories: 1. Transfer function analysis (TFA)-like output; 2. ARI-like output; 3. Correlation coefficient-like output. For all methods, reproducibility was determined by one-way intraclass correlation coefficient analysis (ICC). Main results: For TFA-like methods the mean (SD; [range]) ICC gain was 0.71 (0.10; [0.49-0.86]) and 0.80 (0.17; [0.36-0.94]) for VLF and LF (p = 0.003) respectively. For phase, ICC values were 0.53 (0.21; [0.09-0.80]) for VLF, and 0.92 (0.13; [0.44-1.00]) for LF (p < 0.001). Finally, ICC for ARI-like methods was equal to 0.84 (0.19; [0.41-0.94]), and for correlation-like methods, ICC was 0.21 (0.21; [0.056-0.35]). Significance: When applied to realistic surrogate data, free from the additional exogenous influences of physiological variability on cerebral blood flow, most methods of dCA modelling showed ICC values considerably higher than what has been reported for physiological data. This finding suggests that the poor reproducibility reported by previous studies may be mainly due to the inherent physiological variability of cerebral blood flow regulatory mechanisms rather than related to (stationary) random noise and the signal analysis methods.

Original languageEnglish (US)
Article number125002
JournalPhysiological Measurement
Volume39
Issue number12
DOIs
StatePublished - Dec 7 2018

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Homeostasis
Blood
Value engineering
Cerebrovascular Circulation
Transfer functions
Correlation methods
Systematic errors
Signal analysis
Blood pressure
Pressure measurement
Flow velocity
Standardization
Blood Flow Velocity
Noise
Blood Pressure

Keywords

  • cerebral autoregulation
  • method comparison
  • reproducibility
  • surrogate data

ASJC Scopus subject areas

  • Biophysics
  • Physiology
  • Biomedical Engineering
  • Physiology (medical)

Cite this

Sanders, M. L., Claassen, J. A. H. R., Aries, M., Bor-Seng-Shu, E., Caicedo, A., Chacon, M., ... Elting, J. W. J. (2018). Reproducibility of dynamic cerebral autoregulation parameters: A multi-centre, multi-method study. Physiological Measurement, 39(12), [125002]. https://doi.org/10.1088/1361-6579/aae9fd

Reproducibility of dynamic cerebral autoregulation parameters : A multi-centre, multi-method study. / Sanders, Marit L.; Claassen, Jurgen A.H.R.; Aries, Marcel; Bor-Seng-Shu, Edson; Caicedo, Alexander; Chacon, Max; Gommer, Erik D.; Van Huffel, Sabine; Jara, José L.; Kostoglou, Kyriaki; Mahdi, Adam; Marmarelis, Vasilis Z.; Mitsis, Georgios D.; Müller, Martin; Nikolic, Dragana; Nogueira, Ricardo C.; Payne, Stephen J.; Puppo, Corina; Shin, Dae C.; Simpson, David M.; Tarumi Ph.D., Takashi; Yelicich, Bernardo; Zhang, Rong; Panerai, Ronney B.; Elting, Jan Willem J.

In: Physiological Measurement, Vol. 39, No. 12, 125002, 07.12.2018.

Research output: Contribution to journalArticle

Sanders, ML, Claassen, JAHR, Aries, M, Bor-Seng-Shu, E, Caicedo, A, Chacon, M, Gommer, ED, Van Huffel, S, Jara, JL, Kostoglou, K, Mahdi, A, Marmarelis, VZ, Mitsis, GD, Müller, M, Nikolic, D, Nogueira, RC, Payne, SJ, Puppo, C, Shin, DC, Simpson, DM, Tarumi Ph.D., T, Yelicich, B, Zhang, R, Panerai, RB & Elting, JWJ 2018, 'Reproducibility of dynamic cerebral autoregulation parameters: A multi-centre, multi-method study', Physiological Measurement, vol. 39, no. 12, 125002. https://doi.org/10.1088/1361-6579/aae9fd
Sanders ML, Claassen JAHR, Aries M, Bor-Seng-Shu E, Caicedo A, Chacon M et al. Reproducibility of dynamic cerebral autoregulation parameters: A multi-centre, multi-method study. Physiological Measurement. 2018 Dec 7;39(12). 125002. https://doi.org/10.1088/1361-6579/aae9fd
Sanders, Marit L. ; Claassen, Jurgen A.H.R. ; Aries, Marcel ; Bor-Seng-Shu, Edson ; Caicedo, Alexander ; Chacon, Max ; Gommer, Erik D. ; Van Huffel, Sabine ; Jara, José L. ; Kostoglou, Kyriaki ; Mahdi, Adam ; Marmarelis, Vasilis Z. ; Mitsis, Georgios D. ; Müller, Martin ; Nikolic, Dragana ; Nogueira, Ricardo C. ; Payne, Stephen J. ; Puppo, Corina ; Shin, Dae C. ; Simpson, David M. ; Tarumi Ph.D., Takashi ; Yelicich, Bernardo ; Zhang, Rong ; Panerai, Ronney B. ; Elting, Jan Willem J. / Reproducibility of dynamic cerebral autoregulation parameters : A multi-centre, multi-method study. In: Physiological Measurement. 2018 ; Vol. 39, No. 12.
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abstract = "Objective: Different methods to calculate dynamic cerebral autoregulation (dCA) parameters are available. However, most of these methods demonstrate poor reproducibility that limit their reliability for clinical use. Inter-centre differences in study protocols, modelling approaches and default parameter settings have all led to a lack of standardisation and comparability between studies. We evaluated reproducibility of dCA parameters by assessing systematic errors in surrogate data resulting from different modelling techniques. Approach: Fourteen centres analysed 22 datasets consisting of two repeated physiological blood pressure measurements with surrogate cerebral blood flow velocity signals, generated using Tiecks curves (autoregulation index, ARI 0-9) and added noise. For reproducibility, dCA methods were grouped in three broad categories: 1. Transfer function analysis (TFA)-like output; 2. ARI-like output; 3. Correlation coefficient-like output. For all methods, reproducibility was determined by one-way intraclass correlation coefficient analysis (ICC). Main results: For TFA-like methods the mean (SD; [range]) ICC gain was 0.71 (0.10; [0.49-0.86]) and 0.80 (0.17; [0.36-0.94]) for VLF and LF (p = 0.003) respectively. For phase, ICC values were 0.53 (0.21; [0.09-0.80]) for VLF, and 0.92 (0.13; [0.44-1.00]) for LF (p < 0.001). Finally, ICC for ARI-like methods was equal to 0.84 (0.19; [0.41-0.94]), and for correlation-like methods, ICC was 0.21 (0.21; [0.056-0.35]). Significance: When applied to realistic surrogate data, free from the additional exogenous influences of physiological variability on cerebral blood flow, most methods of dCA modelling showed ICC values considerably higher than what has been reported for physiological data. This finding suggests that the poor reproducibility reported by previous studies may be mainly due to the inherent physiological variability of cerebral blood flow regulatory mechanisms rather than related to (stationary) random noise and the signal analysis methods.",
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T1 - Reproducibility of dynamic cerebral autoregulation parameters

T2 - A multi-centre, multi-method study

AU - Sanders, Marit L.

AU - Claassen, Jurgen A.H.R.

AU - Aries, Marcel

AU - Bor-Seng-Shu, Edson

AU - Caicedo, Alexander

AU - Chacon, Max

AU - Gommer, Erik D.

AU - Van Huffel, Sabine

AU - Jara, José L.

AU - Kostoglou, Kyriaki

AU - Mahdi, Adam

AU - Marmarelis, Vasilis Z.

AU - Mitsis, Georgios D.

AU - Müller, Martin

AU - Nikolic, Dragana

AU - Nogueira, Ricardo C.

AU - Payne, Stephen J.

AU - Puppo, Corina

AU - Shin, Dae C.

AU - Simpson, David M.

AU - Tarumi Ph.D., Takashi

AU - Yelicich, Bernardo

AU - Zhang, Rong

AU - Panerai, Ronney B.

AU - Elting, Jan Willem J.

PY - 2018/12/7

Y1 - 2018/12/7

N2 - Objective: Different methods to calculate dynamic cerebral autoregulation (dCA) parameters are available. However, most of these methods demonstrate poor reproducibility that limit their reliability for clinical use. Inter-centre differences in study protocols, modelling approaches and default parameter settings have all led to a lack of standardisation and comparability between studies. We evaluated reproducibility of dCA parameters by assessing systematic errors in surrogate data resulting from different modelling techniques. Approach: Fourteen centres analysed 22 datasets consisting of two repeated physiological blood pressure measurements with surrogate cerebral blood flow velocity signals, generated using Tiecks curves (autoregulation index, ARI 0-9) and added noise. For reproducibility, dCA methods were grouped in three broad categories: 1. Transfer function analysis (TFA)-like output; 2. ARI-like output; 3. Correlation coefficient-like output. For all methods, reproducibility was determined by one-way intraclass correlation coefficient analysis (ICC). Main results: For TFA-like methods the mean (SD; [range]) ICC gain was 0.71 (0.10; [0.49-0.86]) and 0.80 (0.17; [0.36-0.94]) for VLF and LF (p = 0.003) respectively. For phase, ICC values were 0.53 (0.21; [0.09-0.80]) for VLF, and 0.92 (0.13; [0.44-1.00]) for LF (p < 0.001). Finally, ICC for ARI-like methods was equal to 0.84 (0.19; [0.41-0.94]), and for correlation-like methods, ICC was 0.21 (0.21; [0.056-0.35]). Significance: When applied to realistic surrogate data, free from the additional exogenous influences of physiological variability on cerebral blood flow, most methods of dCA modelling showed ICC values considerably higher than what has been reported for physiological data. This finding suggests that the poor reproducibility reported by previous studies may be mainly due to the inherent physiological variability of cerebral blood flow regulatory mechanisms rather than related to (stationary) random noise and the signal analysis methods.

AB - Objective: Different methods to calculate dynamic cerebral autoregulation (dCA) parameters are available. However, most of these methods demonstrate poor reproducibility that limit their reliability for clinical use. Inter-centre differences in study protocols, modelling approaches and default parameter settings have all led to a lack of standardisation and comparability between studies. We evaluated reproducibility of dCA parameters by assessing systematic errors in surrogate data resulting from different modelling techniques. Approach: Fourteen centres analysed 22 datasets consisting of two repeated physiological blood pressure measurements with surrogate cerebral blood flow velocity signals, generated using Tiecks curves (autoregulation index, ARI 0-9) and added noise. For reproducibility, dCA methods were grouped in three broad categories: 1. Transfer function analysis (TFA)-like output; 2. ARI-like output; 3. Correlation coefficient-like output. For all methods, reproducibility was determined by one-way intraclass correlation coefficient analysis (ICC). Main results: For TFA-like methods the mean (SD; [range]) ICC gain was 0.71 (0.10; [0.49-0.86]) and 0.80 (0.17; [0.36-0.94]) for VLF and LF (p = 0.003) respectively. For phase, ICC values were 0.53 (0.21; [0.09-0.80]) for VLF, and 0.92 (0.13; [0.44-1.00]) for LF (p < 0.001). Finally, ICC for ARI-like methods was equal to 0.84 (0.19; [0.41-0.94]), and for correlation-like methods, ICC was 0.21 (0.21; [0.056-0.35]). Significance: When applied to realistic surrogate data, free from the additional exogenous influences of physiological variability on cerebral blood flow, most methods of dCA modelling showed ICC values considerably higher than what has been reported for physiological data. This finding suggests that the poor reproducibility reported by previous studies may be mainly due to the inherent physiological variability of cerebral blood flow regulatory mechanisms rather than related to (stationary) random noise and the signal analysis methods.

KW - cerebral autoregulation

KW - method comparison

KW - reproducibility

KW - surrogate data

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