Author/Authors :
Meel-van den Abeelen، نويسنده , , Aisha S.S. and Simpson، نويسنده , , David M. and Wang، نويسنده , , Lotte J.Y. and Slump، نويسنده , , Cornelis H. and Zhang، نويسنده , , Rong and Tarumi، نويسنده , , Takashi and Rickards، نويسنده , , Caroline A. and Payne، نويسنده , , Stephen and Mitsis، نويسنده , , Georgios D. and Kostoglou، نويسنده , , Kyriaki and Marmarelis، نويسنده , , Vasilis and Shin، نويسنده , , Dae and Tzeng، نويسنده , , Yu-Chieh and Ainslie، نويسنده , , Philip N. and Gommer، نويسنده , , Erik and Müller، نويسنده , , Martin and Dorado، نويسنده , , Alexander C. and Smielewski، نويسنده , , Peter and Yelicich، نويسنده , , Bernardo and Puppo، نويسنده , , Corina and Liu، نويسنده , , Xiuyun and Czosnyka، نويسنده , , Marek and Wang، نويسنده , , Cheng-Yen and Novak، نويسنده , , Vera and Panerai، نويسنده , , Ronney B. and Claassen، نويسنده , , Jurgen AHR Claassen، نويسنده ,
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.
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.
