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Prediction of Mortality in Hemodialysis Patients Using Moving Multivariate Distance
There is an increasingly widespread use of biomarkers in network physiology to evaluate an organism’s physiological state. A recent study showed that albumin variability increases before death in chronic hemodialysis patients. We hypothesized that a multivariate statistical approach would better all...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7993059/ https://www.ncbi.nlm.nih.gov/pubmed/33776784 http://dx.doi.org/10.3389/fphys.2021.612494 |
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author | Liu, Mingxin Legault, Véronique Fülöp, Tamàs Côté, Anne-Marie Gravel, Dominique Blanchet, F. Guillaume Leung, Diana L. Lee, Sylvia Juhong Nakazato, Yuichi Cohen, Alan A. |
author_facet | Liu, Mingxin Legault, Véronique Fülöp, Tamàs Côté, Anne-Marie Gravel, Dominique Blanchet, F. Guillaume Leung, Diana L. Lee, Sylvia Juhong Nakazato, Yuichi Cohen, Alan A. |
author_sort | Liu, Mingxin |
collection | PubMed |
description | There is an increasingly widespread use of biomarkers in network physiology to evaluate an organism’s physiological state. A recent study showed that albumin variability increases before death in chronic hemodialysis patients. We hypothesized that a multivariate statistical approach would better allow us to capture signals of impending physiological collapse/death. We proposed a Moving Multivariate Distance (MMD), based on the Mahalanobis distance, to quantify the variability of the multivariate biomarker profile as a whole from one visit to the next. Biomarker profiles from a visit were used as the reference to calculate MMD at the subsequent visit. We selected 16 biomarkers (of which 11 are measured every 2 weeks) from blood samples of 763 chronic kidney disease patients hemodialyzed at the CHUS hospital in Quebec, who visited the hospital regularly (∼every 2 weeks) to perform routine blood tests. MMD tended to increase markedly preceding death, indicating an increasing intraindividual multivariate variability presaging a critical transition. In survival analysis, the hazard ratio between the 97.5th percentile and the 2.5th percentile of MMD reached as high as 21.1 [95% CI: 14.3, 31.2], showing that higher variability indicates substantially higher mortality risk. Multivariate approaches to early warning signs of critical transitions hold substantial clinical promise to identify early signs of critical transitions, such as risk of death in hemodialysis patients; future work should also explore whether the MMD approach works in other complex systems (i.e., ecosystems, economies), and should compare it to other multivariate approaches to quantify system variability. |
format | Online Article Text |
id | pubmed-7993059 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-79930592021-03-26 Prediction of Mortality in Hemodialysis Patients Using Moving Multivariate Distance Liu, Mingxin Legault, Véronique Fülöp, Tamàs Côté, Anne-Marie Gravel, Dominique Blanchet, F. Guillaume Leung, Diana L. Lee, Sylvia Juhong Nakazato, Yuichi Cohen, Alan A. Front Physiol Physiology There is an increasingly widespread use of biomarkers in network physiology to evaluate an organism’s physiological state. A recent study showed that albumin variability increases before death in chronic hemodialysis patients. We hypothesized that a multivariate statistical approach would better allow us to capture signals of impending physiological collapse/death. We proposed a Moving Multivariate Distance (MMD), based on the Mahalanobis distance, to quantify the variability of the multivariate biomarker profile as a whole from one visit to the next. Biomarker profiles from a visit were used as the reference to calculate MMD at the subsequent visit. We selected 16 biomarkers (of which 11 are measured every 2 weeks) from blood samples of 763 chronic kidney disease patients hemodialyzed at the CHUS hospital in Quebec, who visited the hospital regularly (∼every 2 weeks) to perform routine blood tests. MMD tended to increase markedly preceding death, indicating an increasing intraindividual multivariate variability presaging a critical transition. In survival analysis, the hazard ratio between the 97.5th percentile and the 2.5th percentile of MMD reached as high as 21.1 [95% CI: 14.3, 31.2], showing that higher variability indicates substantially higher mortality risk. Multivariate approaches to early warning signs of critical transitions hold substantial clinical promise to identify early signs of critical transitions, such as risk of death in hemodialysis patients; future work should also explore whether the MMD approach works in other complex systems (i.e., ecosystems, economies), and should compare it to other multivariate approaches to quantify system variability. Frontiers Media S.A. 2021-03-11 /pmc/articles/PMC7993059/ /pubmed/33776784 http://dx.doi.org/10.3389/fphys.2021.612494 Text en Copyright © 2021 Liu, Legault, Fülöp, Côté, Gravel, Blanchet, Leung, Lee, Nakazato and Cohen. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Physiology Liu, Mingxin Legault, Véronique Fülöp, Tamàs Côté, Anne-Marie Gravel, Dominique Blanchet, F. Guillaume Leung, Diana L. Lee, Sylvia Juhong Nakazato, Yuichi Cohen, Alan A. Prediction of Mortality in Hemodialysis Patients Using Moving Multivariate Distance |
title | Prediction of Mortality in Hemodialysis Patients Using Moving Multivariate Distance |
title_full | Prediction of Mortality in Hemodialysis Patients Using Moving Multivariate Distance |
title_fullStr | Prediction of Mortality in Hemodialysis Patients Using Moving Multivariate Distance |
title_full_unstemmed | Prediction of Mortality in Hemodialysis Patients Using Moving Multivariate Distance |
title_short | Prediction of Mortality in Hemodialysis Patients Using Moving Multivariate Distance |
title_sort | prediction of mortality in hemodialysis patients using moving multivariate distance |
topic | Physiology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7993059/ https://www.ncbi.nlm.nih.gov/pubmed/33776784 http://dx.doi.org/10.3389/fphys.2021.612494 |
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