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Purpose: Sepsis and non-septic systemic inflammatory response syndrome (SIRS) are the same syndromes, differing by their cause, sepsis being secondary to microbial infection. Microbiological tests are not enough to detect infection early. While more than 50 biomarkers have been proposed to detect infection, none have been repeatedly validated.
Aim: To assess the accuracy of circulating biomarkers to discriminate between sepsis and non-septic SIRS.
Methods: The CAPTAIN study was a prospective observational multicenter cohort of 279 ICU patients with hypo- or hyperthermia and criteria of SIRS, included at the time the attending physician considered antimicrobial therapy. Investigators collected blood at inclusion to measure 29 plasma compounds and ten whole blood RNAs, and-for those patients included within working hours-14 leukocyte surface markers. Patients were classified as having sepsis or non-septic SIRS blindly to the biomarkers results. We used the LASSO method as the technique of multivariate analysis, because of the large number of biomarkers.
Results: During the study period, 363 patients with SIRS were screened, 84 having exclusion criteria. Ninety-one patients were classified as having non-septic SIRS and 188 as having sepsis. Eight biomarkers had an area under the receiver operating curve (ROC-AUC) over 0.6 with a 95% confidence interval over 0.5. LASSO regression identified CRP and HLA-DRA mRNA as being repeatedly associated with sepsis, and no model performed better than CRP alone (ROC-AUC 0.76 [0.68-0.84]).
Conclusions: The circulating biomarkers tested were found to discriminate poorly between sepsis and non-septic SIRS, and no combination performed better than CRP alone.
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http://dx.doi.org/10.1007/s00134-018-5228-3 | DOI Listing |
Mil Med Res
September 2025
Department of Biochemistry and Biomedical Sciences, Faculty of Health Sciences, McMaster University, Hamilton, ON, L8S 4K1, Canada.
Background: Although sepsis is known to be the leading cause of morbidity and mortality in adult burn patients, its epidemiology and impact are poorly understood. This study aims to address these gaps by further characterizing predictors of sepsis and comparing outcomes between septic and non-septic burn patients in different age groups.
Methods: We included patients (≥ 18 years) with thermal burn injuries ≥ 5% total body surface area (TBSA) admitted to two burn centers between 1 January 2006 and 30 June 2021, and 1 January 2023 and 6 April 2025.
Crit Care
August 2025
Department of Infection Control Center, Super drug-resistant Organism Infection Prevention and Control Research Center, Xiangya Hospital, Central South University, Changsha, Hunan Province, China.
Background: Infections, particularly sepsis, are a global health threat and a leading cause of mortality among elderly patients (≥ 60 years) in intensive care units (ICUs). The variable immune responses in this vulnerable population warrant deeper investigation.
Methods: This multicenter prospective study included elderly patients with infections admitted to the ICUs of four hospitals between May 2023 and October 2024.
Int J Lab Hematol
August 2025
Department of Lab Sciences, Command Hospital, Kolkata, West Bengal, India.
Background: Cell Population Data (CPD), derived from next-generation hematology analyzers, is emerging as a promising tool for diagnosis of early sepsis. This preliminary case-control study assessed the diagnostic utility of CPD and its association with sequential organ failure assessment (SOFA) scores and procalcitonin levels in sepsis patients.
Methods: Seventy-two sepsis patients and 72 age- and sex-matched non-septic controls were enrolled.
Int J Mol Sci
August 2025
Clinical Pathology Unit, Pescara General Hospital, 65124 Pescara, Italy.
We planned a systemic review and meta-analysis to evaluate the diagnostic accuracy of Monocyte Distribution Width (MDW) in aiding the diagnosis of sepsis in the Emergency Department (ED) and Intensive Care Unit (ICU). A systematic literature search was performed in PubMed, Scopus, and OVID to retrieve studies published up to 29 January 2024. We examined results using mean difference and conducted a diagnostic test accuracy (DTA) meta-analysis using a bivariate random effects model.
View Article and Find Full Text PDFStud Health Technol Inform
August 2025
University of Technology Sydney, Australia.
This study adapts BERT for vital sign time-series analysis in sepsis detection. Using MIMIC-III data, our model's embeddings reveal patient clusters that partition septic from non-septic cases while capturing physiological complexity through diagnosis count distributions. The BERT-based classifier achieves robust performance in both Precision-Recall Area Under Curve (PR AUC), measuring precision maintenance across recall thresholds, and Receiver Operating Characteristic Area Under Curve (ROC AUC), quantifying septic/non-septic case discrimination.
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