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Machine Learning Approach to Understand Worsening Renal Function in Acute Heart Failure
Acute heart failure (AHF) is a common and severe condition with a poor prognosis. Its course is often complicated by worsening renal function (WRF), exacerbating the outcome. The population of AHF patients experiencing WRF is heterogenous, and some novel possibilities for its analysis have recently...
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/PMC9687716/ https://www.ncbi.nlm.nih.gov/pubmed/36358966 http://dx.doi.org/10.3390/biom12111616 |
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author | Urban, Szymon Błaziak, Mikołaj Jura, Maksym Iwanek, Gracjan Ponikowska, Barbara Horudko, Jolanta Siennicka, Agnieszka Berka, Petr Biegus, Jan Ponikowski, Piotr Zymliński, Robert |
author_facet | Urban, Szymon Błaziak, Mikołaj Jura, Maksym Iwanek, Gracjan Ponikowska, Barbara Horudko, Jolanta Siennicka, Agnieszka Berka, Petr Biegus, Jan Ponikowski, Piotr Zymliński, Robert |
author_sort | Urban, Szymon |
collection | PubMed |
description | Acute heart failure (AHF) is a common and severe condition with a poor prognosis. Its course is often complicated by worsening renal function (WRF), exacerbating the outcome. The population of AHF patients experiencing WRF is heterogenous, and some novel possibilities for its analysis have recently emerged. Clustering is a machine learning (ML) technique that divides the population into distinct subgroups based on the similarity of cases (patients). Given that, we decided to use clustering to find subgroups inside the AHF population that differ in terms of WRF occurrence. We evaluated data from the three hundred and twelve AHF patients hospitalized in our institution who had creatinine assessed four times during hospitalization. Eighty-six variables evaluated at admission were included in the analysis. The k-medoids algorithm was used for clustering, and the quality of the procedure was judged by the Davies–Bouldin index. Three clinically and prognostically different clusters were distinguished. The groups had significantly (p = 0.004) different incidences of WRF. Inside the AHF population, we successfully discovered that three groups varied in renal prognosis. Our results provide novel insight into the AHF and WRF interplay and can be valuable for future trial construction and more tailored treatment. |
format | Online Article Text |
id | pubmed-9687716 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-96877162022-11-25 Machine Learning Approach to Understand Worsening Renal Function in Acute Heart Failure Urban, Szymon Błaziak, Mikołaj Jura, Maksym Iwanek, Gracjan Ponikowska, Barbara Horudko, Jolanta Siennicka, Agnieszka Berka, Petr Biegus, Jan Ponikowski, Piotr Zymliński, Robert Biomolecules Article Acute heart failure (AHF) is a common and severe condition with a poor prognosis. Its course is often complicated by worsening renal function (WRF), exacerbating the outcome. The population of AHF patients experiencing WRF is heterogenous, and some novel possibilities for its analysis have recently emerged. Clustering is a machine learning (ML) technique that divides the population into distinct subgroups based on the similarity of cases (patients). Given that, we decided to use clustering to find subgroups inside the AHF population that differ in terms of WRF occurrence. We evaluated data from the three hundred and twelve AHF patients hospitalized in our institution who had creatinine assessed four times during hospitalization. Eighty-six variables evaluated at admission were included in the analysis. The k-medoids algorithm was used for clustering, and the quality of the procedure was judged by the Davies–Bouldin index. Three clinically and prognostically different clusters were distinguished. The groups had significantly (p = 0.004) different incidences of WRF. Inside the AHF population, we successfully discovered that three groups varied in renal prognosis. Our results provide novel insight into the AHF and WRF interplay and can be valuable for future trial construction and more tailored treatment. MDPI 2022-11-02 /pmc/articles/PMC9687716/ /pubmed/36358966 http://dx.doi.org/10.3390/biom12111616 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 Urban, Szymon Błaziak, Mikołaj Jura, Maksym Iwanek, Gracjan Ponikowska, Barbara Horudko, Jolanta Siennicka, Agnieszka Berka, Petr Biegus, Jan Ponikowski, Piotr Zymliński, Robert Machine Learning Approach to Understand Worsening Renal Function in Acute Heart Failure |
title | Machine Learning Approach to Understand Worsening Renal Function in Acute Heart Failure |
title_full | Machine Learning Approach to Understand Worsening Renal Function in Acute Heart Failure |
title_fullStr | Machine Learning Approach to Understand Worsening Renal Function in Acute Heart Failure |
title_full_unstemmed | Machine Learning Approach to Understand Worsening Renal Function in Acute Heart Failure |
title_short | Machine Learning Approach to Understand Worsening Renal Function in Acute Heart Failure |
title_sort | machine learning approach to understand worsening renal function in acute heart failure |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9687716/ https://www.ncbi.nlm.nih.gov/pubmed/36358966 http://dx.doi.org/10.3390/biom12111616 |
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