Machine Learning Consensus Clustering Approach for Hospitalized Patients with Dysmagnesemia

Background: The objectives of this study were to classify patients with serum magnesium derangement on hospital admission into clusters using unsupervised machine learning approach and to evaluate the mortality risks among these distinct clusters. Methods: Consensus cluster analysis was performed ba...

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Autores principales:	Charat Thongprayoon, Janina Paula T. Sy-Go, Voravech Nissaisorakarn, Carissa Y. Dumancas, Mira T. Keddis, Andrea G. Kattah, Pattharawin Pattharanitima, Saraschandra Vallabhajosyula, Michael A. Mao, Fawad Qureshi, Vesna D. Garovic, John J. Dillon, Stephen B. Erickson, Wisit Cheungpasitporn
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	artificial intelligence clustering consensus clustering dysmagnesemia electrolytes hypomagnesemia Medicine (General) R5-920
Acceso en línea:	https://doaj.org/article/3ab80296b4334b97afb4bc0d2561f609
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spelling	oai:doaj.org-article:3ab80296b4334b97afb4bc0d2561f6092021-11-25T17:21:47ZMachine Learning Consensus Clustering Approach for Hospitalized Patients with Dysmagnesemia10.3390/diagnostics111121192075-4418https://doaj.org/article/3ab80296b4334b97afb4bc0d2561f6092021-11-01T00:00:00Zhttps://www.mdpi.com/2075-4418/11/11/2119https://doaj.org/toc/2075-4418Background: The objectives of this study were to classify patients with serum magnesium derangement on hospital admission into clusters using unsupervised machine learning approach and to evaluate the mortality risks among these distinct clusters. Methods: Consensus cluster analysis was performed based on demographic information, principal diagnoses, comorbidities, and laboratory data in hypomagnesemia (serum magnesium ≤ 1.6 mg/dL) and hypermagnesemia cohorts (serum magnesium ≥ 2.4 mg/dL). Each cluster’s key features were determined using the standardized mean difference. The associations of the clusters with hospital mortality and one-year mortality were assessed. Results: In hypomagnesemia cohort (<i>n</i> = 13,320), consensus cluster analysis identified three clusters. Cluster 1 patients had the highest comorbidity burden and lowest serum magnesium. Cluster 2 patients had the youngest age, lowest comorbidity burden, and highest kidney function. Cluster 3 patients had the oldest age and lowest kidney function. Cluster 1 and cluster 3 were associated with higher hospital and one-year mortality compared to cluster 2. In hypermagnesemia cohort (<i>n</i> = 4671), the analysis identified two clusters. Compared to cluster 1, the key features of cluster 2 included older age, higher comorbidity burden, more hospital admissions primarily due to kidney disease, more acute kidney injury, and lower kidney function. Compared to cluster 1, cluster 2 was associated with higher hospital mortality and one-year mortality. Conclusion: Our cluster analysis identified clinically distinct phenotypes with differing mortality risks in hospitalized patients with dysmagnesemia. Future studies are required to assess the application of this ML consensus clustering approach to care for hospitalized patients with dysmagnesemia.Charat ThongprayoonJanina Paula T. Sy-GoVoravech NissaisorakarnCarissa Y. DumancasMira T. KeddisAndrea G. KattahPattharawin PattharanitimaSaraschandra VallabhajosyulaMichael A. MaoFawad QureshiVesna D. GarovicJohn J. DillonStephen B. EricksonWisit CheungpasitpornMDPI AGarticleartificial intelligenceclusteringconsensus clusteringdysmagnesemiaelectrolyteshypomagnesemiaMedicine (General)R5-920ENDiagnostics, Vol 11, Iss 2119, p 2119 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	artificial intelligence clustering consensus clustering dysmagnesemia electrolytes hypomagnesemia Medicine (General) R5-920
spellingShingle	artificial intelligence clustering consensus clustering dysmagnesemia electrolytes hypomagnesemia Medicine (General) R5-920 Charat Thongprayoon Janina Paula T. Sy-Go Voravech Nissaisorakarn Carissa Y. Dumancas Mira T. Keddis Andrea G. Kattah Pattharawin Pattharanitima Saraschandra Vallabhajosyula Michael A. Mao Fawad Qureshi Vesna D. Garovic John J. Dillon Stephen B. Erickson Wisit Cheungpasitporn Machine Learning Consensus Clustering Approach for Hospitalized Patients with Dysmagnesemia
description	Background: The objectives of this study were to classify patients with serum magnesium derangement on hospital admission into clusters using unsupervised machine learning approach and to evaluate the mortality risks among these distinct clusters. Methods: Consensus cluster analysis was performed based on demographic information, principal diagnoses, comorbidities, and laboratory data in hypomagnesemia (serum magnesium ≤ 1.6 mg/dL) and hypermagnesemia cohorts (serum magnesium ≥ 2.4 mg/dL). Each cluster’s key features were determined using the standardized mean difference. The associations of the clusters with hospital mortality and one-year mortality were assessed. Results: In hypomagnesemia cohort (<i>n</i> = 13,320), consensus cluster analysis identified three clusters. Cluster 1 patients had the highest comorbidity burden and lowest serum magnesium. Cluster 2 patients had the youngest age, lowest comorbidity burden, and highest kidney function. Cluster 3 patients had the oldest age and lowest kidney function. Cluster 1 and cluster 3 were associated with higher hospital and one-year mortality compared to cluster 2. In hypermagnesemia cohort (<i>n</i> = 4671), the analysis identified two clusters. Compared to cluster 1, the key features of cluster 2 included older age, higher comorbidity burden, more hospital admissions primarily due to kidney disease, more acute kidney injury, and lower kidney function. Compared to cluster 1, cluster 2 was associated with higher hospital mortality and one-year mortality. Conclusion: Our cluster analysis identified clinically distinct phenotypes with differing mortality risks in hospitalized patients with dysmagnesemia. Future studies are required to assess the application of this ML consensus clustering approach to care for hospitalized patients with dysmagnesemia.
format	article
author	Charat Thongprayoon Janina Paula T. Sy-Go Voravech Nissaisorakarn Carissa Y. Dumancas Mira T. Keddis Andrea G. Kattah Pattharawin Pattharanitima Saraschandra Vallabhajosyula Michael A. Mao Fawad Qureshi Vesna D. Garovic John J. Dillon Stephen B. Erickson Wisit Cheungpasitporn
author_facet	Charat Thongprayoon Janina Paula T. Sy-Go Voravech Nissaisorakarn Carissa Y. Dumancas Mira T. Keddis Andrea G. Kattah Pattharawin Pattharanitima Saraschandra Vallabhajosyula Michael A. Mao Fawad Qureshi Vesna D. Garovic John J. Dillon Stephen B. Erickson Wisit Cheungpasitporn
author_sort	Charat Thongprayoon
title	Machine Learning Consensus Clustering Approach for Hospitalized Patients with Dysmagnesemia
title_short	Machine Learning Consensus Clustering Approach for Hospitalized Patients with Dysmagnesemia
title_full	Machine Learning Consensus Clustering Approach for Hospitalized Patients with Dysmagnesemia
title_fullStr	Machine Learning Consensus Clustering Approach for Hospitalized Patients with Dysmagnesemia
title_full_unstemmed	Machine Learning Consensus Clustering Approach for Hospitalized Patients with Dysmagnesemia
title_sort	machine learning consensus clustering approach for hospitalized patients with dysmagnesemia
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/3ab80296b4334b97afb4bc0d2561f609
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Machine Learning Consensus Clustering Approach for Hospitalized Patients with Dysmagnesemia

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