Machine Learning Approach to Understand Worsening Renal Function in Acute Heart Failure Article Swipe
Szymon Urban
,
Mikołaj Błaziak
,
Maksym Jura
,
Gracjan Iwanek
,
Barbara Ponikowska
,
Jolanta Horudko
,
Agnieszka Siennicka
,
Petr Berka
,
Jan Biegus
,
Piotr Ponikowski
,
Robert Zymliński
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3390/biom12111616
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3390/biom12111616
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.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/biom12111616
- https://www.mdpi.com/2218-273X/12/11/1616/pdf?version=1667358674
- OA Status
- gold
- Cited By
- 6
- References
- 54
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4308119340
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4308119340Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/biom12111616Digital Object Identifier
- Title
-
Machine Learning Approach to Understand Worsening Renal Function in Acute Heart FailureWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-11-02Full publication date if available
- Authors
-
Szymon Urban, Mikołaj Błaziak, Maksym Jura, Gracjan Iwanek, Barbara Ponikowska, Jolanta Horudko, Agnieszka Siennicka, Petr Berka, Jan Biegus, Piotr Ponikowski, Robert ZymlińskiList of authors in order
- Landing page
-
https://doi.org/10.3390/biom12111616Publisher landing page
- PDF URL
-
https://www.mdpi.com/2218-273X/12/11/1616/pdf?version=1667358674Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.mdpi.com/2218-273X/12/11/1616/pdf?version=1667358674Direct OA link when available
- Concepts
-
Heart failure, Population, Cluster analysis, Renal function, Medicine, Creatinine, Weather Research and Forecasting Model, Similarity (geometry), Internal medicine, Intensive care medicine, Computer science, Artificial intelligence, Geography, Meteorology, Environmental health, Image (mathematics)Top concepts (fields/topics) attached by OpenAlex
- Cited by
-
6Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 1, 2024: 3, 2023: 2Per-year citation counts (last 5 years)
- References (count)
-
54Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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