Meel-van den Abeelen, Aisha S.S., Simpson, David M., Wang, Lotte J.Y., Slump, Cornelis H., Zhang, Rong, Tarumi, Takashi, Rickards, Caroline A., Payne, Stephen, Mitsis, Georgios D., Kostoglou, Kyriaki, Marmarelis, Vasilis, Shin, Dae, Tzeng, Yu-Chieh, Ainslie, Philip N., Gommer, Erik, Müller, Martin, Dorado, Alexander C., Smielewski, Peter, Yelicich, Bernardo, Puppo, Corina, Liu, Xiuyun, Czosnyka, Marek, Wang, Cheng-Yen, Novak, Vera, Panerai, Ronney B. and Claassen, Jurgen A.H.R. (2014) Between-centre variability in transfer function analysis, a widely used method for linear quantification of the dynamic pressure–flow relation: the CARNet study. Medical Engineering & Physics, 36 (5), 620-627. (doi:10.1016/j.medengphy.2014.02.002).
Abstract
Transfer function analysis (TFA) is a frequently used method to assess dynamic cerebral autoregulation (CA) using spontaneous oscillations in blood pressure (BP) and cerebral blood flow velocity (CBFV). However, controversies and variations exist in how research groups utilise TFA, causing high variability in interpretation. The objective of this study was to evaluate between-centre variability in TFA outcome metrics. 15 centres analysed the same 70 BP and CBFV datasets from healthy subjects (n = 50 rest; n = 20 during hypercapnia); 10 additional datasets were computer-generated. Each centre used their in-house TFA methods; however, certain parameters were specified to reduce a priori between-centre variability. Hypercapnia was used to assess discriminatory performance and synthetic data to evaluate effects of parameter settings. Results were analysed using the Mann–Whitney test and logistic regression. A large non-homogeneous variation was found in TFA outcome metrics between the centres. Logistic regression demonstrated that 11 centres were able to distinguish between normal and impaired CA with an AUC > 0.85. Further analysis identified TFA settings that are associated with large variation in outcome measures.
These results indicate the need for standardisation of TFA settings in order to reduce between-centre variability and to allow accurate comparison between studies. Suggestions on optimal signal processing methods are proposed
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