Identifying acute kidney injury subtypes based on serum electrolyte data in ICU via K -medoids clustering
This study proposes to use the K-medoids clustering method to identify subtypes of Intensive Care Unit (ICU)-acquired acute kidney injury (AKI) patients based on serum electrolyte data. Three distinct AKI subtypes with different serum electrolyte characteristics were identified by clustering analysi...
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Published in | AMIA ... Annual Symposium proceedings Vol. 2024; p. 733 |
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2024
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Abstract | This study proposes to use the K-medoids clustering method to identify subtypes of Intensive Care Unit (ICU)-acquired acute kidney injury (AKI) patients based on serum electrolyte data. Three distinct AKI subtypes with different serum electrolyte characteristics were identified by clustering analysis. Further, descriptive analysis was employed to characterize in-hospital mortality and renal replacement therapy, diuretic and vasopressor usage in the three subtypes, and Chi-square tests were conducted to check the differences of prognosis and treatments among the identified subtypes. This study enables the subclassification of AKI patients in the ICU, facilitating ICU physicians to make timely clinical decisions about AKI, and ultimately may contribute to patient outcome improvement. |
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AbstractList | This study proposes to use the K-medoids clustering method to identify subtypes of Intensive Care Unit (ICU)-acquired acute kidney injury (AKI) patients based on serum electrolyte data. Three distinct AKI subtypes with different serum electrolyte characteristics were identified by clustering analysis. Further, descriptive analysis was employed to characterize in-hospital mortality and renal replacement therapy, diuretic and vasopressor usage in the three subtypes, and Chi-square tests were conducted to check the differences of prognosis and treatments among the identified subtypes. This study enables the subclassification of AKI patients in the ICU, facilitating ICU physicians to make timely clinical decisions about AKI, and ultimately may contribute to patient outcome improvement.This study proposes to use the K-medoids clustering method to identify subtypes of Intensive Care Unit (ICU)-acquired acute kidney injury (AKI) patients based on serum electrolyte data. Three distinct AKI subtypes with different serum electrolyte characteristics were identified by clustering analysis. Further, descriptive analysis was employed to characterize in-hospital mortality and renal replacement therapy, diuretic and vasopressor usage in the three subtypes, and Chi-square tests were conducted to check the differences of prognosis and treatments among the identified subtypes. This study enables the subclassification of AKI patients in the ICU, facilitating ICU physicians to make timely clinical decisions about AKI, and ultimately may contribute to patient outcome improvement. This study proposes to use the K-medoids clustering method to identify subtypes of Intensive Care Unit (ICU)-acquired acute kidney injury (AKI) patients based on serum electrolyte data. Three distinct AKI subtypes with different serum electrolyte characteristics were identified by clustering analysis. Further, descriptive analysis was employed to characterize in-hospital mortality and renal replacement therapy, diuretic and vasopressor usage in the three subtypes, and Chi-square tests were conducted to check the differences of prognosis and treatments among the identified subtypes. This study enables the subclassification of AKI patients in the ICU, facilitating ICU physicians to make timely clinical decisions about AKI, and ultimately may contribute to patient outcome improvement. |
Author | Shi, Tongyue Hao, Jianguo Liu, Wentie Xu, Haowei Zhao, Huiying Kong, Guilan |
Author_xml | – sequence: 1 givenname: Wentie surname: Liu fullname: Liu, Wentie organization: National Institute of Health Data Science, Peking University, Beijing, China – sequence: 2 givenname: Tongyue surname: Shi fullname: Shi, Tongyue organization: National Institute of Health Data Science, Peking University, Beijing, China – sequence: 3 givenname: Haowei surname: Xu fullname: Xu, Haowei organization: School of Artificial Intelligence, Optics and Electronics (iOPEN), NorthwesternPolytechnical University, Xi'an, China – sequence: 4 givenname: Huiying surname: Zhao fullname: Zhao, Huiying organization: Department of Critical Care Medicine, Peking University People's Hospital, Beijing, China – sequence: 5 givenname: Jianguo surname: Hao fullname: Hao, Jianguo organization: National Institute of Health Data Science, Peking University, Beijing, China – sequence: 6 givenname: Guilan surname: Kong fullname: Kong, Guilan organization: Advanced Institute of Information Technology, Peking University, Hangzhou, China |
BackLink | https://www.ncbi.nlm.nih.gov/pubmed/40417583$$D View this record in MEDLINE/PubMed |
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Snippet | This study proposes to use the K-medoids clustering method to identify subtypes of Intensive Care Unit (ICU)-acquired acute kidney injury (AKI) patients based... |
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SubjectTerms | Acute Kidney Injury - blood Acute Kidney Injury - classification Acute Kidney Injury - diagnosis Acute Kidney Injury - mortality Acute Kidney Injury - therapy Cluster Analysis Electrolytes - blood Hospital Mortality Humans Intensive Care Units Prognosis Renal Replacement Therapy |
Title | Identifying acute kidney injury subtypes based on serum electrolyte data in ICU via K -medoids clustering |
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