Cytokine-Based Insights into Bloodstream Infections and Bacterial Gram Typing in ICU COVID-19 Patients
Year: 2025
Type: article
Study design: Observational Study
Abstract: Background: Timely and accurate identification of bloodstream infections (BSIs) in intensive care unit (ICU) patients remains a key challenge, particularly in COVID-19 settings, where immune dysregulation can obscure early clinical signs. Methods: Cytokine profiling was evaluated to discriminate between ICU patients with and without BSIs, and, among those with confirmed BSIs, to further stratify bacterial infections by Gram type. Serum samples from 45 ICU COVID-19 patients were analyzed using a 21-cytokine panel, with feature selection applied to identify candidate markers. Results: A machine learning workflow identified key features, achieving robust performance metrics with AUC values up to 0.97 for BSI classification and 0.98 for Gram typing. Conclusions: In contrast to traditional approaches that focus on individual cytokines or simple ratios, the present analysis employed programmatically generated ratios between pro-inflammatory and anti-inflammatory cytokines, refined through feature selection. Although further validation in larger and more diverse cohorts is warranted, these findings underscore the potential of advanced cytokine-based diagnostics to enhance precision medicine in infection management.
Source: Metabolites
Institutions: Universidade Politécnica de Lisboa, Comprehensive Health Research Centre, Universidade Nova de Lisboa, Instituto Portugues do Sangue e da Transplantacao, IP, University of Lisbon, Instituto Superior Técnico, Institute for Bioengineering and Biosciences, Unidade Local de Saúde de São José
Language: English
Subfield: Epidemiology
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $3,258
Awards: Programa Doutoral em Biomedicina., G2246039698, G4586771618, The dynamics of biomarkers as a predictive factor in the morbidity and mortality of critically ill patients., Modelos de Previsão de Desenvolvimento da COVID-19 em Doentes de Risco para uma Medicina de Precisão, Diagnosis and prognosis disease biomarkers on critically ill patients with COVID towards a precision medicine – a machine learning approach
OpenAlex ID: W4408504012
