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Blood Pressure Variability Indices for Outcome Prediction After Thrombectomy in Stroke by Using High-Resolution Data

BACKGROUND: Blood pressure variability (BPV) is associated with outcome after endovascular thrombectomy in acute large vessel occlusion stroke. We aimed to provide the optimal sampling frequency and BPV index for outcome prediction by using high-resolution blood pressure (BP) data. METHODS: Patient...

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Autores principales: Inauen, Corinne, Boss, Jens M., Katan, Mira, Luft, Andreas R., Kulcsar, Zsolt, Willms, Jan F., Bögli, Stefan Y., Keller, Emanuela
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer US 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9343264/
https://www.ncbi.nlm.nih.gov/pubmed/35606560
http://dx.doi.org/10.1007/s12028-022-01519-x
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author Inauen, Corinne
Boss, Jens M.
Katan, Mira
Luft, Andreas R.
Kulcsar, Zsolt
Willms, Jan F.
Bögli, Stefan Y.
Keller, Emanuela
author_facet Inauen, Corinne
Boss, Jens M.
Katan, Mira
Luft, Andreas R.
Kulcsar, Zsolt
Willms, Jan F.
Bögli, Stefan Y.
Keller, Emanuela
author_sort Inauen, Corinne
collection PubMed
description BACKGROUND: Blood pressure variability (BPV) is associated with outcome after endovascular thrombectomy in acute large vessel occlusion stroke. We aimed to provide the optimal sampling frequency and BPV index for outcome prediction by using high-resolution blood pressure (BP) data. METHODS: Patient characteristics, 3-month outcome, and BP values measured intraarterially at 1 Hz for up to 24 h were extracted from 34 patients treated at a tertiary care center neurocritical care unit. Outcome was dichotomized (modified Rankin Scale 0–2, favorable, and 3–6, unfavorable) and associated with systolic BPV (as calculated by using standard deviation, coefficient of variation, averaged real variability, successive variation, number of trend changes, and a spectral approach using the power of specific BP frequencies). BP values were downsampled by either averaging or omitting all BP values within each prespecified time bin to compare the different sampling rates. RESULTS: Out of 34 patients (age 72 ± 12.7 years, 67.6% men), 10 (29.4%) achieved a favorable functional outcome and 24 (70.6%) had an unfavorable functional outcome at 3 months. No group differences were found in mean absolute systolic BP (SBP) (130 ± 18 mm Hg, p = 0.82) and diastolic BP (DBP) (59 ± 10 mm Hg, p = 1.00) during the monitoring time. BPV only reached predictive significance when using successive variation extracted from downsampled (averaged over 5 min) SBP data (median 4.8 mm Hg [range 3.8–7.1]) in patients with favorable versus 7.1 mmHg [range 5.5–9.7] in those with unfavorable outcome, area under the curve = 0.74 [confidence interval (CI) 0.57–0.85; p = 0.031], or the power of midrange frequencies between 1/20 and 1/5 min [area under the curve = 0.75 (CI 0.59–0.86), p = 0.020]. CONCLUSIONS: Using high-resolution BP data of 1 Hz, downsampling by averaging all BP values within 5-min intervals is essential to find relevant differences in systolic BPV, as noise can be avoided (confirmed by the significance of the power of midrange frequencies). These results demonstrate how high-resolution BP data can be processed for effective outcome prediction. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s12028-022-01519-x.
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spelling pubmed-93432642022-08-03 Blood Pressure Variability Indices for Outcome Prediction After Thrombectomy in Stroke by Using High-Resolution Data Inauen, Corinne Boss, Jens M. Katan, Mira Luft, Andreas R. Kulcsar, Zsolt Willms, Jan F. Bögli, Stefan Y. Keller, Emanuela Neurocrit Care Big Data in Neurocritical Care BACKGROUND: Blood pressure variability (BPV) is associated with outcome after endovascular thrombectomy in acute large vessel occlusion stroke. We aimed to provide the optimal sampling frequency and BPV index for outcome prediction by using high-resolution blood pressure (BP) data. METHODS: Patient characteristics, 3-month outcome, and BP values measured intraarterially at 1 Hz for up to 24 h were extracted from 34 patients treated at a tertiary care center neurocritical care unit. Outcome was dichotomized (modified Rankin Scale 0–2, favorable, and 3–6, unfavorable) and associated with systolic BPV (as calculated by using standard deviation, coefficient of variation, averaged real variability, successive variation, number of trend changes, and a spectral approach using the power of specific BP frequencies). BP values were downsampled by either averaging or omitting all BP values within each prespecified time bin to compare the different sampling rates. RESULTS: Out of 34 patients (age 72 ± 12.7 years, 67.6% men), 10 (29.4%) achieved a favorable functional outcome and 24 (70.6%) had an unfavorable functional outcome at 3 months. No group differences were found in mean absolute systolic BP (SBP) (130 ± 18 mm Hg, p = 0.82) and diastolic BP (DBP) (59 ± 10 mm Hg, p = 1.00) during the monitoring time. BPV only reached predictive significance when using successive variation extracted from downsampled (averaged over 5 min) SBP data (median 4.8 mm Hg [range 3.8–7.1]) in patients with favorable versus 7.1 mmHg [range 5.5–9.7] in those with unfavorable outcome, area under the curve = 0.74 [confidence interval (CI) 0.57–0.85; p = 0.031], or the power of midrange frequencies between 1/20 and 1/5 min [area under the curve = 0.75 (CI 0.59–0.86), p = 0.020]. CONCLUSIONS: Using high-resolution BP data of 1 Hz, downsampling by averaging all BP values within 5-min intervals is essential to find relevant differences in systolic BPV, as noise can be avoided (confirmed by the significance of the power of midrange frequencies). These results demonstrate how high-resolution BP data can be processed for effective outcome prediction. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s12028-022-01519-x. Springer US 2022-05-23 2022 /pmc/articles/PMC9343264/ /pubmed/35606560 http://dx.doi.org/10.1007/s12028-022-01519-x Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Big Data in Neurocritical Care
Inauen, Corinne
Boss, Jens M.
Katan, Mira
Luft, Andreas R.
Kulcsar, Zsolt
Willms, Jan F.
Bögli, Stefan Y.
Keller, Emanuela
Blood Pressure Variability Indices for Outcome Prediction After Thrombectomy in Stroke by Using High-Resolution Data
title Blood Pressure Variability Indices for Outcome Prediction After Thrombectomy in Stroke by Using High-Resolution Data
title_full Blood Pressure Variability Indices for Outcome Prediction After Thrombectomy in Stroke by Using High-Resolution Data
title_fullStr Blood Pressure Variability Indices for Outcome Prediction After Thrombectomy in Stroke by Using High-Resolution Data
title_full_unstemmed Blood Pressure Variability Indices for Outcome Prediction After Thrombectomy in Stroke by Using High-Resolution Data
title_short Blood Pressure Variability Indices for Outcome Prediction After Thrombectomy in Stroke by Using High-Resolution Data
title_sort blood pressure variability indices for outcome prediction after thrombectomy in stroke by using high-resolution data
topic Big Data in Neurocritical Care
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9343264/
https://www.ncbi.nlm.nih.gov/pubmed/35606560
http://dx.doi.org/10.1007/s12028-022-01519-x
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