Cytokine-Based Insights into Bloodstream Infections and Bacterial Gram Typing in ICU COVID-19 Patients
<b>Background:</b> 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. <b>Methods:</b> Cytokine profi...
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MDPI AG
2025-03-01
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| Online Access: | https://www.mdpi.com/2218-1989/15/3/204 |
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| author | Rúben Araújo Luís Ramalhete Cristiana P. Von Rekowski Tiago A. H. Fonseca Cecília R. C. Calado Luís Bento |
| author_facet | Rúben Araújo Luís Ramalhete Cristiana P. Von Rekowski Tiago A. H. Fonseca Cecília R. C. Calado Luís Bento |
| author_sort | Rúben Araújo |
| collection | DOAJ |
| description | <b>Background:</b> 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. <b>Methods:</b> 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. <b>Results:</b> 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. <b>Conclusions:</b> 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. |
| format | Article |
| id | doaj-art-961ab9e11eb744ae83c7ca199dd5a76c |
| institution | OA Journals |
| issn | 2218-1989 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Metabolites |
| spelling | doaj-art-961ab9e11eb744ae83c7ca199dd5a76c2025-08-20T01:49:05ZengMDPI AGMetabolites2218-19892025-03-0115320410.3390/metabo15030204Cytokine-Based Insights into Bloodstream Infections and Bacterial Gram Typing in ICU COVID-19 PatientsRúben Araújo0Luís Ramalhete1Cristiana P. Von Rekowski2Tiago A. H. Fonseca3Cecília R. C. Calado4Luís Bento5NMS—NOVA Medical School, FCM—Faculdade de Ciências Médicas, Universidade NOVA de Lisboa, Campo dos Mártires da Pátria 130, 1169-056 Lisbon, PortugalNMS—NOVA Medical School, FCM—Faculdade de Ciências Médicas, Universidade NOVA de Lisboa, Campo dos Mártires da Pátria 130, 1169-056 Lisbon, PortugalNMS—NOVA Medical School, FCM—Faculdade de Ciências Médicas, Universidade NOVA de Lisboa, Campo dos Mártires da Pátria 130, 1169-056 Lisbon, PortugalNMS—NOVA Medical School, FCM—Faculdade de Ciências Médicas, Universidade NOVA de Lisboa, Campo dos Mártires da Pátria 130, 1169-056 Lisbon, PortugalISEL—Instituto Superior de Engenharia de Lisboa, Instituto Politécnico de Lisboa, Rua Conselheiro Emídio Navarro 1, 1959-007 Lisbon, PortugalNMS—NOVA Medical School, FCM—Faculdade de Ciências Médicas, Universidade NOVA de Lisboa, Campo dos Mártires da Pátria 130, 1169-056 Lisbon, Portugal<b>Background:</b> 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. <b>Methods:</b> 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. <b>Results:</b> 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. <b>Conclusions:</b> 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.https://www.mdpi.com/2218-1989/15/3/204cytokine profilingbloodstream infectionsGram typingICU diagnosticsCOVID-19machine learning |
| spellingShingle | Rúben Araújo Luís Ramalhete Cristiana P. Von Rekowski Tiago A. H. Fonseca Cecília R. C. Calado Luís Bento Cytokine-Based Insights into Bloodstream Infections and Bacterial Gram Typing in ICU COVID-19 Patients Metabolites cytokine profiling bloodstream infections Gram typing ICU diagnostics COVID-19 machine learning |
| title | Cytokine-Based Insights into Bloodstream Infections and Bacterial Gram Typing in ICU COVID-19 Patients |
| title_full | Cytokine-Based Insights into Bloodstream Infections and Bacterial Gram Typing in ICU COVID-19 Patients |
| title_fullStr | Cytokine-Based Insights into Bloodstream Infections and Bacterial Gram Typing in ICU COVID-19 Patients |
| title_full_unstemmed | Cytokine-Based Insights into Bloodstream Infections and Bacterial Gram Typing in ICU COVID-19 Patients |
| title_short | Cytokine-Based Insights into Bloodstream Infections and Bacterial Gram Typing in ICU COVID-19 Patients |
| title_sort | cytokine based insights into bloodstream infections and bacterial gram typing in icu covid 19 patients |
| topic | cytokine profiling bloodstream infections Gram typing ICU diagnostics COVID-19 machine learning |
| url | https://www.mdpi.com/2218-1989/15/3/204 |
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