ArticleThe Journal of surgical research2022
Early Biomarker Signatures in Surgical Sepsis.
Article in The Journal of surgical research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Artificial Intelligence- and Machine Learning-Assisted Subphenotyping for Personalized Immunotherapy in Sepsis.Journal of personalized medicine · 2026Review
- Bridging sepsis physiology and biosensor engineering: a clinical perspective on electrochemical cytokine sensor design.Frontiers in bioengineering and biotechnology · 2026Article
- Bundle compliance patterns in septic shock and their association with patient outcomes: an unsupervised cluster analysis.Internal and emergency medicine · 2025Article
- Sepsis subphenotypes: bridging the gaps in sepsis treatment strategies.Frontiers in immunology · 2025Review
- Development and validation of an ensemble learning risk model for sepsis after abdominal surgery.Archives of medical science : AMS · 2025Article
- Definitions, epidemiology, and outcomes of persistent/chronic critical illness: a scoping review for translation to clinical practice.Critical care (London, England) · 2024Article
- An early sepsis prediction model utilizing machine learning and unbalanced data processing in a clinical context.Preventive medicine reports · 2024Article
- Identifying acute illness phenotypes via deep temporal interpolation and clustering network on physiologic signatures.Scientific reports · 2024Article
- Sepsis-associated acute kidney injury: recent advances in enrichment strategies, sub-phenotyping and clinical trials.Critical care (London, England) · 2024Review
- Biomarker Enrichment in Sepsis-Associated Acute Kidney Injury: Finding High-Risk Patients in the Intensive Care Unit.American journal of nephrology · 2024Review
- Biological Phenotyping in Sepsis and Acute Respiratory Distress Syndrome.Annual review of medicine · 2023Review
- The Value of Neutrophil/Lymphocyte Ratio Combined with Red Blood Cell Distribution Width in Evaluating the Prognosis of Emergency Patients with Sepsis.Emergency medicine international · 2022Article
Corrections and comments
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Authors and funding
14 authors.
Funding
Abstract
introductionSepsis 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.
methodsThis 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 naïve Bayes classifier predicted cluster labels in a validation cohort.
resultsThe 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.
conclusionsMachine 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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