Cargando…
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...
Autores principales: | , , , , , , , , , , , , |
---|---|
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 |
_version_ | 1784582306066857984 |
---|---|
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. |
format | Online Article Text |
id | pubmed-8509302 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT thongprayooncharat machinelearningconsensusclusteringapproachforhospitalizedpatientswithphosphatederangements AT dumancascarissay machinelearningconsensusclusteringapproachforhospitalizedpatientswithphosphatederangements AT nissaisorakarnvoravech machinelearningconsensusclusteringapproachforhospitalizedpatientswithphosphatederangements AT keddismirat machinelearningconsensusclusteringapproachforhospitalizedpatientswithphosphatederangements AT kattahandreag machinelearningconsensusclusteringapproachforhospitalizedpatientswithphosphatederangements AT pattharanitimapattharawin machinelearningconsensusclusteringapproachforhospitalizedpatientswithphosphatederangements AT petnaktananchai machinelearningconsensusclusteringapproachforhospitalizedpatientswithphosphatederangements AT vallabhajosyulasaraschandra machinelearningconsensusclusteringapproachforhospitalizedpatientswithphosphatederangements AT garovicvesnad machinelearningconsensusclusteringapproachforhospitalizedpatientswithphosphatederangements AT maomichaela machinelearningconsensusclusteringapproachforhospitalizedpatientswithphosphatederangements AT dillonjohnj machinelearningconsensusclusteringapproachforhospitalizedpatientswithphosphatederangements AT ericksonstephenb machinelearningconsensusclusteringapproachforhospitalizedpatientswithphosphatederangements AT cheungpasitpornwisit machinelearningconsensusclusteringapproachforhospitalizedpatientswithphosphatederangements |