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Evaluating clinical heterogeneity and predicting mortality in severely burned patients through unsupervised clustering and latent class analysis

Burn injuries often result in a high level of clinical heterogeneity and poor prognosis in patients with severe burns. Clustering algorithms, which are unsupervised methods that can identify groups with similar trajectories in patients with heterogeneous diseases, can provide insights into the mecha...

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Autores principales: Kim, Sungmin, Yoon, Jaechul, Kym, Dohern, Hur, Jun, Kim, Myongjin, Park, Jongsoo, Cho, Yong Suk, Chun, Wook, Yoon, Dogeon
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10442401/
https://www.ncbi.nlm.nih.gov/pubmed/37604951
http://dx.doi.org/10.1038/s41598-023-40927-7
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author Kim, Sungmin
Yoon, Jaechul
Kym, Dohern
Hur, Jun
Kim, Myongjin
Park, Jongsoo
Cho, Yong Suk
Chun, Wook
Yoon, Dogeon
author_facet Kim, Sungmin
Yoon, Jaechul
Kym, Dohern
Hur, Jun
Kim, Myongjin
Park, Jongsoo
Cho, Yong Suk
Chun, Wook
Yoon, Dogeon
author_sort Kim, Sungmin
collection PubMed
description Burn injuries often result in a high level of clinical heterogeneity and poor prognosis in patients with severe burns. Clustering algorithms, which are unsupervised methods that can identify groups with similar trajectories in patients with heterogeneous diseases, can provide insights into the mechanisms of the disease pathogenesis. This study aimed to analyze routinely collected biomarkers to understand their mortality prediction power, identify the clinical meanings or subtypes, and inform treatment decisions to improve the outcomes of patients with burns. This retrospective cohort study included patients aged ≥ 18 years who were admitted between January 2010 and December 2021. The patients were divided into four subgroups based on the time period of their admission: week 1, 2, 3, and 4. The study revealed that 22 biomarkers were evaluated, and the red blood cell distribution width, bicarbonate level, pH, platelets, and lymphocytes were significantly associated with the mortality risk. Latent class analysis further demonstrated that the pH, platelets, lymphocytes, lactate, and albumin demonstrated the lowest levels in the cluster with the highest risk of mortality, with the lowest levels of pH and lactate being particularly noteworthy in week 1 of the study. During the week 2, the pH and lymphocyte levels were demonstrated to be significant predictors of the mortality risk, whereas the lymphocyte and platelet counts were meaningful predictors in week 3. During week 4, pH, platelet count, and albumin level were important predictors of mortality risk. Analysis of routinely collected biomarkers using clustering algorithms and latent class analysis can provide valuable insights into the heterogeneity of burn injuries and improve the ability to predict disease progression and mortality. Our findings suggest that lactate levels are a better indicator of cellular hypoxia in the early stages of burn shock, whereas platelet and lymphocyte levels are more indicative of infections such as sepsis. Albumin levels are considered a better indicator of reduced nutritional loss with decrease in unhealed burn wounds; however, the pH levels reflect the overall condition of the patient throughout the study period. These findings can be used to inform treatment decisions and improve the outcomes of burn patients.
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spelling pubmed-104424012023-08-23 Evaluating clinical heterogeneity and predicting mortality in severely burned patients through unsupervised clustering and latent class analysis Kim, Sungmin Yoon, Jaechul Kym, Dohern Hur, Jun Kim, Myongjin Park, Jongsoo Cho, Yong Suk Chun, Wook Yoon, Dogeon Sci Rep Article Burn injuries often result in a high level of clinical heterogeneity and poor prognosis in patients with severe burns. Clustering algorithms, which are unsupervised methods that can identify groups with similar trajectories in patients with heterogeneous diseases, can provide insights into the mechanisms of the disease pathogenesis. This study aimed to analyze routinely collected biomarkers to understand their mortality prediction power, identify the clinical meanings or subtypes, and inform treatment decisions to improve the outcomes of patients with burns. This retrospective cohort study included patients aged ≥ 18 years who were admitted between January 2010 and December 2021. The patients were divided into four subgroups based on the time period of their admission: week 1, 2, 3, and 4. The study revealed that 22 biomarkers were evaluated, and the red blood cell distribution width, bicarbonate level, pH, platelets, and lymphocytes were significantly associated with the mortality risk. Latent class analysis further demonstrated that the pH, platelets, lymphocytes, lactate, and albumin demonstrated the lowest levels in the cluster with the highest risk of mortality, with the lowest levels of pH and lactate being particularly noteworthy in week 1 of the study. During the week 2, the pH and lymphocyte levels were demonstrated to be significant predictors of the mortality risk, whereas the lymphocyte and platelet counts were meaningful predictors in week 3. During week 4, pH, platelet count, and albumin level were important predictors of mortality risk. Analysis of routinely collected biomarkers using clustering algorithms and latent class analysis can provide valuable insights into the heterogeneity of burn injuries and improve the ability to predict disease progression and mortality. Our findings suggest that lactate levels are a better indicator of cellular hypoxia in the early stages of burn shock, whereas platelet and lymphocyte levels are more indicative of infections such as sepsis. Albumin levels are considered a better indicator of reduced nutritional loss with decrease in unhealed burn wounds; however, the pH levels reflect the overall condition of the patient throughout the study period. These findings can be used to inform treatment decisions and improve the outcomes of burn patients. Nature Publishing Group UK 2023-08-21 /pmc/articles/PMC10442401/ /pubmed/37604951 http://dx.doi.org/10.1038/s41598-023-40927-7 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Kim, Sungmin
Yoon, Jaechul
Kym, Dohern
Hur, Jun
Kim, Myongjin
Park, Jongsoo
Cho, Yong Suk
Chun, Wook
Yoon, Dogeon
Evaluating clinical heterogeneity and predicting mortality in severely burned patients through unsupervised clustering and latent class analysis
title Evaluating clinical heterogeneity and predicting mortality in severely burned patients through unsupervised clustering and latent class analysis
title_full Evaluating clinical heterogeneity and predicting mortality in severely burned patients through unsupervised clustering and latent class analysis
title_fullStr Evaluating clinical heterogeneity and predicting mortality in severely burned patients through unsupervised clustering and latent class analysis
title_full_unstemmed Evaluating clinical heterogeneity and predicting mortality in severely burned patients through unsupervised clustering and latent class analysis
title_short Evaluating clinical heterogeneity and predicting mortality in severely burned patients through unsupervised clustering and latent class analysis
title_sort evaluating clinical heterogeneity and predicting mortality in severely burned patients through unsupervised clustering and latent class analysis
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10442401/
https://www.ncbi.nlm.nih.gov/pubmed/37604951
http://dx.doi.org/10.1038/s41598-023-40927-7
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