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Early Biomarker Signatures in Surgical Sepsis
INTRODUCTION: Sepsis has complex, time-sensitive pathophysiology and important phenotypic subgroups. The objective of this study was to use machine learning analyses of blood and urine biomarker profiles to elucidate the pathophysiologic signatures of subgroups of surgical sepsis patients. METHODS:...
Autores principales: | , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9827429/ https://www.ncbi.nlm.nih.gov/pubmed/35569215 http://dx.doi.org/10.1016/j.jss.2022.04.052 |
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author | Madushani, R.W.M.A. Patel, Vishal Loftus, Tyler Ren, Yuanfang Li, Han Jacob Velez, Laura Wu, Quran Adhikari, Lasith Efron, Philip Segal, Mark Ozrazgat-Baslanti, Tezcan Rashidi, Parisa Bihorac, Azra |
author_facet | Madushani, R.W.M.A. Patel, Vishal Loftus, Tyler Ren, Yuanfang Li, Han Jacob Velez, Laura Wu, Quran Adhikari, Lasith Efron, Philip Segal, Mark Ozrazgat-Baslanti, Tezcan Rashidi, Parisa Bihorac, Azra |
author_sort | Madushani, R.W.M.A. |
collection | PubMed |
description | INTRODUCTION: Sepsis has complex, time-sensitive pathophysiology and important phenotypic subgroups. The objective of this study was to use machine learning analyses of blood and urine biomarker profiles to elucidate the pathophysiologic signatures of subgroups of surgical sepsis patients. METHODS: This prospective cohort study included 243 surgical sepsis patients admitted to a quaternary care center between January 2015 and June 2017. We applied hierarchical clustering to clinical variables and 42 blood and urine biomarkers to identify phenotypic subgroups in a development cohort. Clinical characteristics and short-term and long-term outcomes were compared between clusters. A naїve Bayes classifier predicted cluster labels in a validation cohort. RESULTS: The development cohort contained one cluster characterized by early organ dysfunction (cluster I, n = 18) and one cluster characterized by recovery (cluster II, n = 139). Cluster I was associated with higher Acute Physiologic Assessment and Chronic Health Evaluation II (30 versus 16, P < 0.001) and SOFA scores (13 versus 5, P < 0.001), greater prevalence of chronic cardiovascular and renal disease (P < 0.001) and septic shock (78% versus 17%, P < 0.001). Cluster I had higher mortality within 14 d of sepsis onset (11% versus 1.5%, P = 0.001) and within 1 y (44% versus 20%, P = 0.032), and higher incidence of chronic critical illness (61% versus 30%, P = 0.001). The Bayes classifier achieved 95% accuracy and identified two clusters that were similar to development cohort clusters. CONCLUSIONS: Machine learning analyses of clinical and biomarker variables identified an early organ dysfunction sepsis phenotype characterized by inflammation, renal dysfunction, endotheliopathy, and immunosuppression, as well as poor short-term and long-term clinical outcomes. |
format | Online Article Text |
id | pubmed-9827429 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
record_format | MEDLINE/PubMed |
spelling | pubmed-98274292023-01-09 Early Biomarker Signatures in Surgical Sepsis Madushani, R.W.M.A. Patel, Vishal Loftus, Tyler Ren, Yuanfang Li, Han Jacob Velez, Laura Wu, Quran Adhikari, Lasith Efron, Philip Segal, Mark Ozrazgat-Baslanti, Tezcan Rashidi, Parisa Bihorac, Azra J Surg Res Article INTRODUCTION: Sepsis has complex, time-sensitive pathophysiology and important phenotypic subgroups. The objective of this study was to use machine learning analyses of blood and urine biomarker profiles to elucidate the pathophysiologic signatures of subgroups of surgical sepsis patients. METHODS: This prospective cohort study included 243 surgical sepsis patients admitted to a quaternary care center between January 2015 and June 2017. We applied hierarchical clustering to clinical variables and 42 blood and urine biomarkers to identify phenotypic subgroups in a development cohort. Clinical characteristics and short-term and long-term outcomes were compared between clusters. A naїve Bayes classifier predicted cluster labels in a validation cohort. RESULTS: The development cohort contained one cluster characterized by early organ dysfunction (cluster I, n = 18) and one cluster characterized by recovery (cluster II, n = 139). Cluster I was associated with higher Acute Physiologic Assessment and Chronic Health Evaluation II (30 versus 16, P < 0.001) and SOFA scores (13 versus 5, P < 0.001), greater prevalence of chronic cardiovascular and renal disease (P < 0.001) and septic shock (78% versus 17%, P < 0.001). Cluster I had higher mortality within 14 d of sepsis onset (11% versus 1.5%, P = 0.001) and within 1 y (44% versus 20%, P = 0.032), and higher incidence of chronic critical illness (61% versus 30%, P = 0.001). The Bayes classifier achieved 95% accuracy and identified two clusters that were similar to development cohort clusters. CONCLUSIONS: Machine learning analyses of clinical and biomarker variables identified an early organ dysfunction sepsis phenotype characterized by inflammation, renal dysfunction, endotheliopathy, and immunosuppression, as well as poor short-term and long-term clinical outcomes. 2022-09 2022-05-12 /pmc/articles/PMC9827429/ /pubmed/35569215 http://dx.doi.org/10.1016/j.jss.2022.04.052 Text en https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) ). |
spellingShingle | Article Madushani, R.W.M.A. Patel, Vishal Loftus, Tyler Ren, Yuanfang Li, Han Jacob Velez, Laura Wu, Quran Adhikari, Lasith Efron, Philip Segal, Mark Ozrazgat-Baslanti, Tezcan Rashidi, Parisa Bihorac, Azra Early Biomarker Signatures in Surgical Sepsis |
title | Early Biomarker Signatures in Surgical Sepsis |
title_full | Early Biomarker Signatures in Surgical Sepsis |
title_fullStr | Early Biomarker Signatures in Surgical Sepsis |
title_full_unstemmed | Early Biomarker Signatures in Surgical Sepsis |
title_short | Early Biomarker Signatures in Surgical Sepsis |
title_sort | early biomarker signatures in surgical sepsis |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9827429/ https://www.ncbi.nlm.nih.gov/pubmed/35569215 http://dx.doi.org/10.1016/j.jss.2022.04.052 |
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