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Patient-specific pulse wave propagation model identifies cardiovascular risk characteristics in hemodialysis patients

Risk of cardiovascular associated death in dialysis patients is the highest among all other co-morbidities. Improving the identification of patients with the highest cardiovascular risk to design an adequate treatment is, therefore, of utmost importance. There are several non-invasive cardiovascular...

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Autores principales: Poleszczuk, Jan, Debowska, Malgorzata, Dabrowski, Wojciech, Wojcik-Zaluska, Alicja, Zaluska, Wojciech, Waniewski, Jacek
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6157900/
https://www.ncbi.nlm.nih.gov/pubmed/30216341
http://dx.doi.org/10.1371/journal.pcbi.1006417
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author Poleszczuk, Jan
Debowska, Malgorzata
Dabrowski, Wojciech
Wojcik-Zaluska, Alicja
Zaluska, Wojciech
Waniewski, Jacek
author_facet Poleszczuk, Jan
Debowska, Malgorzata
Dabrowski, Wojciech
Wojcik-Zaluska, Alicja
Zaluska, Wojciech
Waniewski, Jacek
author_sort Poleszczuk, Jan
collection PubMed
description Risk of cardiovascular associated death in dialysis patients is the highest among all other co-morbidities. Improving the identification of patients with the highest cardiovascular risk to design an adequate treatment is, therefore, of utmost importance. There are several non-invasive cardiovascular state biomarkers based on the pulse (pressure) wave propagation properties, but their major determinants are not fully understood. In the current study we aimed to provide a framework to precisely dissect the information available in non-invasively recorded pulse wave in hemodialysis patients. Radial pressure wave profiles were recorded before, during and after two independent hemodialysis sessions in 35 anuric prevalent hemodialysis patients and once in a group of 32 healthy volunteers. Each recording was used to estimate six subject-specific parameters of pulse wave propagation model. Pressure profiles were also analyzed using SphygmoCor software (AtCor Medical, Australia) to derive values of already established biomarkers, i.e. augmentation index and sub-endocardial viability ratio (SEVR). Data preprocessing using propensity score matching allowed to compare hemodialysis and healthy groups. Augmentation index remained on average stable at 142 ± 28% during dialysis and had similar values in both considered groups. SEVR, whose pre-dialytic value was on average lower by 12% compared to healthy participants, was improved by hemodialysis, with post-dialytic values indistinguishable from those in healthy population (p-value > 0.2). The model, however, identified that the patients on hemodialysis had significantly increased stiffness of both large and small arteries compared to healthy counterparts (> 60% before dialysis with p-value < 0.05 or borderline) and that it was only transiently decreased during hemodialysis session. Additionally, correlation-based clustering revealed that augmentation index reflects the shape of heart ejection profile and SEVR is associated with stiffness of larger arteries. Patient-specific pulse wave propagation modeling coupled with radial pressure profile recording correctly identified increased arterial stiffness in hemodialysis patients, while regular pulse wave analysis based biomarkers failed to show significant differences. Further model testing in larger populations and investigating other biomarkers are needed to confirm these findings.
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spelling pubmed-61579002018-10-19 Patient-specific pulse wave propagation model identifies cardiovascular risk characteristics in hemodialysis patients Poleszczuk, Jan Debowska, Malgorzata Dabrowski, Wojciech Wojcik-Zaluska, Alicja Zaluska, Wojciech Waniewski, Jacek PLoS Comput Biol Research Article Risk of cardiovascular associated death in dialysis patients is the highest among all other co-morbidities. Improving the identification of patients with the highest cardiovascular risk to design an adequate treatment is, therefore, of utmost importance. There are several non-invasive cardiovascular state biomarkers based on the pulse (pressure) wave propagation properties, but their major determinants are not fully understood. In the current study we aimed to provide a framework to precisely dissect the information available in non-invasively recorded pulse wave in hemodialysis patients. Radial pressure wave profiles were recorded before, during and after two independent hemodialysis sessions in 35 anuric prevalent hemodialysis patients and once in a group of 32 healthy volunteers. Each recording was used to estimate six subject-specific parameters of pulse wave propagation model. Pressure profiles were also analyzed using SphygmoCor software (AtCor Medical, Australia) to derive values of already established biomarkers, i.e. augmentation index and sub-endocardial viability ratio (SEVR). Data preprocessing using propensity score matching allowed to compare hemodialysis and healthy groups. Augmentation index remained on average stable at 142 ± 28% during dialysis and had similar values in both considered groups. SEVR, whose pre-dialytic value was on average lower by 12% compared to healthy participants, was improved by hemodialysis, with post-dialytic values indistinguishable from those in healthy population (p-value > 0.2). The model, however, identified that the patients on hemodialysis had significantly increased stiffness of both large and small arteries compared to healthy counterparts (> 60% before dialysis with p-value < 0.05 or borderline) and that it was only transiently decreased during hemodialysis session. Additionally, correlation-based clustering revealed that augmentation index reflects the shape of heart ejection profile and SEVR is associated with stiffness of larger arteries. Patient-specific pulse wave propagation modeling coupled with radial pressure profile recording correctly identified increased arterial stiffness in hemodialysis patients, while regular pulse wave analysis based biomarkers failed to show significant differences. Further model testing in larger populations and investigating other biomarkers are needed to confirm these findings. Public Library of Science 2018-09-14 /pmc/articles/PMC6157900/ /pubmed/30216341 http://dx.doi.org/10.1371/journal.pcbi.1006417 Text en © 2018 Poleszczuk et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Poleszczuk, Jan
Debowska, Malgorzata
Dabrowski, Wojciech
Wojcik-Zaluska, Alicja
Zaluska, Wojciech
Waniewski, Jacek
Patient-specific pulse wave propagation model identifies cardiovascular risk characteristics in hemodialysis patients
title Patient-specific pulse wave propagation model identifies cardiovascular risk characteristics in hemodialysis patients
title_full Patient-specific pulse wave propagation model identifies cardiovascular risk characteristics in hemodialysis patients
title_fullStr Patient-specific pulse wave propagation model identifies cardiovascular risk characteristics in hemodialysis patients
title_full_unstemmed Patient-specific pulse wave propagation model identifies cardiovascular risk characteristics in hemodialysis patients
title_short Patient-specific pulse wave propagation model identifies cardiovascular risk characteristics in hemodialysis patients
title_sort patient-specific pulse wave propagation model identifies cardiovascular risk characteristics in hemodialysis patients
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6157900/
https://www.ncbi.nlm.nih.gov/pubmed/30216341
http://dx.doi.org/10.1371/journal.pcbi.1006417
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