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...
- Autores:
- 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 |
_version_ |
1814167699962462208 |
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 |