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Machine Learning Consensus Clustering Approach for Hospitalized Patients with Phosphate Derangements

Background: The goal of this study was to categorize patients with abnormal serum phosphate upon hospital admission into distinct clusters utilizing an unsupervised machine learning approach, and to assess the mortality risk associated with these clusters. Methods: We utilized the consensus clusteri...

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Autores principales: Thongprayoon, Charat, Dumancas, Carissa Y., Nissaisorakarn, Voravech, Keddis, Mira T., Kattah, Andrea G., Pattharanitima, Pattharawin, Petnak, Tananchai, Vallabhajosyula, Saraschandra, Garovic, Vesna D., Mao, Michael A., Dillon, John J., Erickson, Stephen B., Cheungpasitporn, Wisit
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8509302/
https://www.ncbi.nlm.nih.gov/pubmed/34640457
http://dx.doi.org/10.3390/jcm10194441
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author Thongprayoon, Charat
Dumancas, Carissa Y.
Nissaisorakarn, Voravech
Keddis, Mira T.
Kattah, Andrea G.
Pattharanitima, Pattharawin
Petnak, Tananchai
Vallabhajosyula, Saraschandra
Garovic, Vesna D.
Mao, Michael A.
Dillon, John J.
Erickson, Stephen B.
Cheungpasitporn, Wisit
author_facet Thongprayoon, Charat
Dumancas, Carissa Y.
Nissaisorakarn, Voravech
Keddis, Mira T.
Kattah, Andrea G.
Pattharanitima, Pattharawin
Petnak, Tananchai
Vallabhajosyula, Saraschandra
Garovic, Vesna D.
Mao, Michael A.
Dillon, John J.
Erickson, Stephen B.
Cheungpasitporn, Wisit
author_sort Thongprayoon, Charat
collection PubMed
description Background: The goal of this study was to categorize patients with abnormal serum phosphate upon hospital admission into distinct clusters utilizing an unsupervised machine learning approach, and to assess the mortality risk associated with these clusters. Methods: We utilized the consensus clustering approach on demographic information, comorbidities, principal diagnoses, and laboratory data of hypophosphatemia (serum phosphate ≤ 2.4 mg/dL) and hyperphosphatemia cohorts (serum phosphate ≥ 4.6 mg/dL). The standardized mean difference was applied to determine each cluster’s key features. We assessed the association of the clusters with mortality. Results: In the hypophosphatemia cohort (n = 3113), the consensus cluster analysis identified two clusters. The key features of patients in Cluster 2, compared with Cluster 1, included: older age; a higher comorbidity burden, particularly hypertension; diabetes mellitus; coronary artery disease; lower eGFR; and more acute kidney injury (AKI) at admission. Cluster 2 had a comparable hospital mortality (3.7% vs. 2.9%; p = 0.17), but a higher one-year mortality (26.8% vs. 14.0%; p < 0.001), and five-year mortality (20.2% vs. 44.3%; p < 0.001), compared to Cluster 1. In the hyperphosphatemia cohort (n = 7252), the analysis identified two clusters. The key features of patients in Cluster 2, compared with Cluster 1, included: older age; more primary admission for kidney disease; more history of hypertension; more end-stage kidney disease; more AKI at admission; and higher admission potassium, magnesium, and phosphate. Cluster 2 had a higher hospital (8.9% vs. 2.4%; p < 0.001) one-year mortality (32.9% vs. 14.8%; p < 0.001), and five-year mortality (24.5% vs. 51.1%; p < 0.001), compared with Cluster 1. Conclusion: Our cluster analysis classified clinically distinct phenotypes with different mortality risks among hospitalized patients with serum phosphate derangements. Age, comorbidities, and kidney function were the key features that differentiated the phenotypes.
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spelling pubmed-85093022021-10-13 Machine Learning Consensus Clustering Approach for Hospitalized Patients with Phosphate Derangements Thongprayoon, Charat Dumancas, Carissa Y. Nissaisorakarn, Voravech Keddis, Mira T. Kattah, Andrea G. Pattharanitima, Pattharawin Petnak, Tananchai Vallabhajosyula, Saraschandra Garovic, Vesna D. Mao, Michael A. Dillon, John J. Erickson, Stephen B. Cheungpasitporn, Wisit J Clin Med Article Background: The goal of this study was to categorize patients with abnormal serum phosphate upon hospital admission into distinct clusters utilizing an unsupervised machine learning approach, and to assess the mortality risk associated with these clusters. Methods: We utilized the consensus clustering approach on demographic information, comorbidities, principal diagnoses, and laboratory data of hypophosphatemia (serum phosphate ≤ 2.4 mg/dL) and hyperphosphatemia cohorts (serum phosphate ≥ 4.6 mg/dL). The standardized mean difference was applied to determine each cluster’s key features. We assessed the association of the clusters with mortality. Results: In the hypophosphatemia cohort (n = 3113), the consensus cluster analysis identified two clusters. The key features of patients in Cluster 2, compared with Cluster 1, included: older age; a higher comorbidity burden, particularly hypertension; diabetes mellitus; coronary artery disease; lower eGFR; and more acute kidney injury (AKI) at admission. Cluster 2 had a comparable hospital mortality (3.7% vs. 2.9%; p = 0.17), but a higher one-year mortality (26.8% vs. 14.0%; p < 0.001), and five-year mortality (20.2% vs. 44.3%; p < 0.001), compared to Cluster 1. In the hyperphosphatemia cohort (n = 7252), the analysis identified two clusters. The key features of patients in Cluster 2, compared with Cluster 1, included: older age; more primary admission for kidney disease; more history of hypertension; more end-stage kidney disease; more AKI at admission; and higher admission potassium, magnesium, and phosphate. Cluster 2 had a higher hospital (8.9% vs. 2.4%; p < 0.001) one-year mortality (32.9% vs. 14.8%; p < 0.001), and five-year mortality (24.5% vs. 51.1%; p < 0.001), compared with Cluster 1. Conclusion: Our cluster analysis classified clinically distinct phenotypes with different mortality risks among hospitalized patients with serum phosphate derangements. Age, comorbidities, and kidney function were the key features that differentiated the phenotypes. MDPI 2021-09-27 /pmc/articles/PMC8509302/ /pubmed/34640457 http://dx.doi.org/10.3390/jcm10194441 Text en © 2021 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
Thongprayoon, Charat
Dumancas, Carissa Y.
Nissaisorakarn, Voravech
Keddis, Mira T.
Kattah, Andrea G.
Pattharanitima, Pattharawin
Petnak, Tananchai
Vallabhajosyula, Saraschandra
Garovic, Vesna D.
Mao, Michael A.
Dillon, John J.
Erickson, Stephen B.
Cheungpasitporn, Wisit
Machine Learning Consensus Clustering Approach for Hospitalized Patients with Phosphate Derangements
title Machine Learning Consensus Clustering Approach for Hospitalized Patients with Phosphate Derangements
title_full Machine Learning Consensus Clustering Approach for Hospitalized Patients with Phosphate Derangements
title_fullStr Machine Learning Consensus Clustering Approach for Hospitalized Patients with Phosphate Derangements
title_full_unstemmed Machine Learning Consensus Clustering Approach for Hospitalized Patients with Phosphate Derangements
title_short Machine Learning Consensus Clustering Approach for Hospitalized Patients with Phosphate Derangements
title_sort machine learning consensus clustering approach for hospitalized patients with phosphate derangements
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8509302/
https://www.ncbi.nlm.nih.gov/pubmed/34640457
http://dx.doi.org/10.3390/jcm10194441
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