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Chronic Kidney Disease as a Cardiovascular Disorder—Tonometry Data Analyses
Tonometry is commonly used to provide efficient and good diagnostics for cardiovascular disease (CVD). There are many advantages of this method, including low cost, non-invasiveness and little time to perform. In this study, the effort was undertaken to check whether tonometry data hides valuable in...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9566812/ https://www.ncbi.nlm.nih.gov/pubmed/36231682 http://dx.doi.org/10.3390/ijerph191912339 |
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author | Twardawa, Mateusz Formanowicz, Piotr Formanowicz, Dorota |
author_facet | Twardawa, Mateusz Formanowicz, Piotr Formanowicz, Dorota |
author_sort | Twardawa, Mateusz |
collection | PubMed |
description | Tonometry is commonly used to provide efficient and good diagnostics for cardiovascular disease (CVD). There are many advantages of this method, including low cost, non-invasiveness and little time to perform. In this study, the effort was undertaken to check whether tonometry data hides valuable information associated with different stages of chronic kidney disease (CKD) and end-stage renal disease (ESRD) treatment. For this purpose, six groups containing patients at different stages of CKD following different ways of dialysis treatment, as well as patients without CKD but with CVD and healthy volunteers were assessed. It was revealed that each of the studied groups had a unique profile. Only the type of dialysis was indistinguishable a from tonometric perspective (hemodialysis vs. peritoneal dialysis). Several techniques were used to build profiles that independently gave the same outcome: analysis of variance, network correlation structure analysis, multinomial logistic regression, and discrimination analysis. Moreover, to evaluate the classification potential of the discriminatory model, all mentioned techniques were later compared and treated as feature selection methods. Although the results are promising, it could be difficult to express differences as simple mathematical relations. This study shows that artificial intelligence can differentiate between different stages of CKD and patients without CKD. Potential future machine learning models will be able to determine kidney health with high accuracy and thereby classify patients. ClinicalTrials.gov Identifier: NCT05214872. |
format | Online Article Text |
id | pubmed-9566812 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-95668122022-10-15 Chronic Kidney Disease as a Cardiovascular Disorder—Tonometry Data Analyses Twardawa, Mateusz Formanowicz, Piotr Formanowicz, Dorota Int J Environ Res Public Health Article Tonometry is commonly used to provide efficient and good diagnostics for cardiovascular disease (CVD). There are many advantages of this method, including low cost, non-invasiveness and little time to perform. In this study, the effort was undertaken to check whether tonometry data hides valuable information associated with different stages of chronic kidney disease (CKD) and end-stage renal disease (ESRD) treatment. For this purpose, six groups containing patients at different stages of CKD following different ways of dialysis treatment, as well as patients without CKD but with CVD and healthy volunteers were assessed. It was revealed that each of the studied groups had a unique profile. Only the type of dialysis was indistinguishable a from tonometric perspective (hemodialysis vs. peritoneal dialysis). Several techniques were used to build profiles that independently gave the same outcome: analysis of variance, network correlation structure analysis, multinomial logistic regression, and discrimination analysis. Moreover, to evaluate the classification potential of the discriminatory model, all mentioned techniques were later compared and treated as feature selection methods. Although the results are promising, it could be difficult to express differences as simple mathematical relations. This study shows that artificial intelligence can differentiate between different stages of CKD and patients without CKD. Potential future machine learning models will be able to determine kidney health with high accuracy and thereby classify patients. ClinicalTrials.gov Identifier: NCT05214872. MDPI 2022-09-28 /pmc/articles/PMC9566812/ /pubmed/36231682 http://dx.doi.org/10.3390/ijerph191912339 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Twardawa, Mateusz Formanowicz, Piotr Formanowicz, Dorota Chronic Kidney Disease as a Cardiovascular Disorder—Tonometry Data Analyses |
title | Chronic Kidney Disease as a Cardiovascular Disorder—Tonometry Data Analyses |
title_full | Chronic Kidney Disease as a Cardiovascular Disorder—Tonometry Data Analyses |
title_fullStr | Chronic Kidney Disease as a Cardiovascular Disorder—Tonometry Data Analyses |
title_full_unstemmed | Chronic Kidney Disease as a Cardiovascular Disorder—Tonometry Data Analyses |
title_short | Chronic Kidney Disease as a Cardiovascular Disorder—Tonometry Data Analyses |
title_sort | chronic kidney disease as a cardiovascular disorder—tonometry data analyses |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9566812/ https://www.ncbi.nlm.nih.gov/pubmed/36231682 http://dx.doi.org/10.3390/ijerph191912339 |
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