https://scholars.lib.ntu.edu.tw/handle/123456789/611740
標題: | Reproducibility of dynamic cerebral autoregulation parameters: A multi-centre, multi-method study | 作者: | Sanders M.L. Claassen J.A.H.R. Aries M. Bor-Seng-Shu E. Caicedo A. Chacon M. Gommer E.D. Van Huffel S. Jara J.L. Kostoglou K. Mahdi A. Marmarelis V.Z. Mitsis G.D. M?ller M. Nikolic D. Nogueira R.C. Payne S.J. Puppo C. Shin D.C. Simpson D.M. Tarumi T. Yelicich B. Zhang R. Panerai R.B. Elting J.W.J. STEPHEN JOHN PAYNE |
關鍵字: | Blood;Blood pressure;Correlation methods;Flow velocity;Hemodynamics;Cerebral autoregulation;Cerebral blood flow;Clinical use;Intraclass correlation coefficients;Method comparison;Multi methods;Protocol modeling;Reproducibilities;Surrogate data;Transfer function analysis;Systematic errors;aged;blood pressure measurement;brain circulation;clinical trial;female;homeostasis;human;male;multicenter study;reproducibility;Aged;Blood Pressure Determination;Cerebrovascular Circulation;Female;Homeostasis;Humans;Male;Reproducibility of Results | 公開日期: | 2018 | 卷: | 39 | 期: | 12 | 來源出版物: | Physiological Measurement | 摘要: | 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. ? 2018 Institute of Physics and Engineering in Medicine. |
URI: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85058910412&doi=10.1088%2f1361-6579%2faae9fd&partnerID=40&md5=4da3abd807d3027594c877e4f38409d2 https://scholars.lib.ntu.edu.tw/handle/123456789/611740 |
ISSN: | 09673334 | DOI: | 10.1088/1361-6579/aae9fd |
顯示於: | 應用力學研究所 |
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