Reproducibility of dynamic cerebral autoregulation parameters: A multi-centre, multi-method study

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 param...

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Tipo de recurso:
Fecha de publicación:
2018
Institución:
Universidad del Rosario
Repositorio:
Repositorio EdocUR - U. Rosario
Idioma:
eng
OAI Identifier:
oai:repository.urosario.edu.co:10336/22810
Acceso en línea:
https://doi.org/10.1088/1361-6579/aae9fd
https://repository.urosario.edu.co/handle/10336/22810
Palabra clave:
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
Cerebral autoregulation
Method comparison
Reproducibility
Surrogate data
Rights
License
Abierto (Texto Completo)
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oai_identifier_str oai:repository.urosario.edu.co:10336/22810
network_acronym_str EDOCUR2
network_name_str Repositorio EdocUR - U. Rosario
repository_id_str
dc.title.spa.fl_str_mv Reproducibility of dynamic cerebral autoregulation parameters: A multi-centre, multi-method study
title Reproducibility of dynamic cerebral autoregulation parameters: A multi-centre, multi-method study
spellingShingle Reproducibility of dynamic cerebral autoregulation parameters: A multi-centre, multi-method study
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
Cerebral autoregulation
Method comparison
Reproducibility
Surrogate data
title_short Reproducibility of dynamic cerebral autoregulation parameters: A multi-centre, multi-method study
title_full Reproducibility of dynamic cerebral autoregulation parameters: A multi-centre, multi-method study
title_fullStr Reproducibility of dynamic cerebral autoregulation parameters: A multi-centre, multi-method study
title_full_unstemmed Reproducibility of dynamic cerebral autoregulation parameters: A multi-centre, multi-method study
title_sort Reproducibility of dynamic cerebral autoregulation parameters: A multi-centre, multi-method study
dc.subject.keyword.spa.fl_str_mv 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
Cerebral autoregulation
Method comparison
Reproducibility
Surrogate data
topic 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
Cerebral autoregulation
Method comparison
Reproducibility
Surrogate data
description 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 less than 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.
publishDate 2018
dc.date.created.spa.fl_str_mv 2018
dc.date.accessioned.none.fl_str_mv 2020-05-25T23:58:08Z
dc.date.available.none.fl_str_mv 2020-05-25T23:58:08Z
dc.type.eng.fl_str_mv article
dc.type.coarversion.fl_str_mv http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.coar.fl_str_mv http://purl.org/coar/resource_type/c_6501
dc.type.spa.spa.fl_str_mv Artículo
dc.identifier.doi.none.fl_str_mv https://doi.org/10.1088/1361-6579/aae9fd
dc.identifier.issn.none.fl_str_mv 9673334
dc.identifier.uri.none.fl_str_mv https://repository.urosario.edu.co/handle/10336/22810
url https://doi.org/10.1088/1361-6579/aae9fd
https://repository.urosario.edu.co/handle/10336/22810
identifier_str_mv 9673334
dc.language.iso.spa.fl_str_mv eng
language eng
dc.relation.citationIssue.none.fl_str_mv No. 12
dc.relation.citationTitle.none.fl_str_mv Physiological Measurement
dc.relation.citationVolume.none.fl_str_mv Vol. 39
dc.relation.ispartof.spa.fl_str_mv Physiological Measurement, ISSN:9673334, Vol.39, No.12 (2018)
dc.relation.uri.spa.fl_str_mv https://www.scopus.com/inward/record.uri?eid=2-s2.0-85058910412&doi=10.1088%2f1361-6579%2faae9fd&partnerID=40&md5=4da3abd807d3027594c877e4f38409d2
dc.rights.coar.fl_str_mv http://purl.org/coar/access_right/c_abf2
dc.rights.acceso.spa.fl_str_mv Abierto (Texto Completo)
rights_invalid_str_mv Abierto (Texto Completo)
http://purl.org/coar/access_right/c_abf2
dc.format.mimetype.none.fl_str_mv application/pdf
dc.publisher.spa.fl_str_mv Institute of Physics Publishing
institution Universidad del Rosario
dc.source.instname.spa.fl_str_mv instname:Universidad del Rosario
dc.source.reponame.spa.fl_str_mv reponame:Repositorio Institucional EdocUR
repository.name.fl_str_mv Repositorio institucional EdocUR
repository.mail.fl_str_mv edocur@urosario.edu.co
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spelling ececf8a5-8fcb-4613-9849-4dd206df1899-113fbc6eb-a699-4a75-a6cb-2f868141ce6e-1a1266612-4902-4dd8-8a7b-6fd80143c556-1934df0df-7b14-4019-8f77-462038366063-121e3cc2b-385f-4821-a9ca-d9d7303f84ce-13726fba3-9136-493f-b93a-fabf8f78dd06-1476e6b39-860f-4c00-8fb7-4d0c7eb76379-113e85339-b951-4e4f-84a3-92c720dbe6e0-1ab62d5b0-4bbc-4676-a72b-7b78456b666a-1bafc7278-dddc-45e1-a422-8b1924dd9d50-141c13ca3-7938-4e32-bf76-60cd76725441-187832868-21ae-49f6-af04-ef84c7b5f1d5-12738407d-168c-4e51-bc55-b5a6e8475d17-191a336ef-c96d-4235-b974-0753aea81593-1c8eff2b4-e378-4f5e-a531-324df568d3f3-1a685789f-ee46-47be-976c-5439b06d156d-1e92472ea-6233-430e-8ebf-b515e7b4a074-1202b95dc-cfdd-43c0-acf6-286d0b6629b8-1579a9b1b-5232-4bf3-ba45-327363aed8f9-1f20d4189-3a2c-41d0-8e0f-a8167633e71c-1f071f3b1-3756-4305-8a6c-86a64ecb6658-19eb7caa3-36cd-4f4a-bf0f-5c40e408d26d-1ba4c46fe-9d76-46f0-8730-e61056d200b4-1fb653bf5-39c9-44ee-9cdf-0a41cf09e61d-10184cce9-860a-4fc7-aaab-22a55a180b12-12020-05-25T23:58:08Z2020-05-25T23:58:08Z2018Objective: 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 less than 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.application/pdfhttps://doi.org/10.1088/1361-6579/aae9fd9673334https://repository.urosario.edu.co/handle/10336/22810engInstitute of Physics PublishingNo. 12Physiological MeasurementVol. 39Physiological Measurement, ISSN:9673334, Vol.39, No.12 (2018)https://www.scopus.com/inward/record.uri?eid=2-s2.0-85058910412&doi=10.1088%2f1361-6579%2faae9fd&partnerID=40&md5=4da3abd807d3027594c877e4f38409d2Abierto (Texto Completo)http://purl.org/coar/access_right/c_abf2instname:Universidad del Rosarioreponame:Repositorio Institucional EdocURAgedBlood pressure measurementBrain circulationClinical trialFemaleHomeostasisHumanMaleMulticenter studyReproducibilityAgedBlood pressure determinationCerebrovascular circulationFemaleHomeostasisHumansMaleReproducibility of resultsCerebral autoregulationMethod comparisonReproducibilitySurrogate dataReproducibility of dynamic cerebral autoregulation parameters: A multi-centre, multi-method studyarticleArtículohttp://purl.org/coar/version/c_970fb48d4fbd8a85http://purl.org/coar/resource_type/c_6501Sanders 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.Müller 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.10336/22810oai:repository.urosario.edu.co:10336/228102022-05-02 07:37:19.397036https://repository.urosario.edu.coRepositorio institucional EdocURedocur@urosario.edu.co