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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:...

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Autores principales: 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
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2022
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.
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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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