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Point-of-care neutrophil and monocyte surface markers differentiate bacterial from viral infections at the emergency department within 30 min.
Jukema, Bernard N; de Hond, Titus A P; Kroon, Martijn; Maranus, Anna E; Koenderman, Leo; Kaasjager, Karin A H.
Afiliación
  • Jukema BN; Department of Respiratory Medicine, University Medical Centre Utrecht, Utrecht University, Heidelberglaan 100, Utrecht, 3584 CX, The Netherlands.
  • de Hond TAP; Centre for Translational Immunology, University Medical Centre Utrecht, Utrecht University, Heidelberglaan 100, Utrecht, 3584 CX, The Netherlands.
  • Kroon M; Department of Internal Medicine and Acute Medicine, University Medical Centre Utrecht, Utrecht University, Heidelberglaan 100, Utrecht, 3584 CX, The Netherlands.
  • Maranus AE; Centre for Translational Immunology, University Medical Centre Utrecht, Utrecht University, Heidelberglaan 100, Utrecht, 3584 CX, The Netherlands.
  • Koenderman L; Centre for Translational Immunology, University Medical Centre Utrecht, Utrecht University, Heidelberglaan 100, Utrecht, 3584 CX, The Netherlands.
  • Kaasjager KAH; Department of Respiratory Medicine, University Medical Centre Utrecht, Utrecht University, Heidelberglaan 100, Utrecht, 3584 CX, The Netherlands.
J Leukoc Biol ; 115(4): 714-722, 2024 03 29.
Article en En | MEDLINE | ID: mdl-38169315
ABSTRACT
Rapid discrimination between viral and bacterial infections in a point-of-care setting will improve clinical outcome. Expression of CD64 on neutrophils (neuCD64) increases during bacterial infections, whereas expression of CD169 on classical monocytes (cmCD169) increases during viral infections. The diagnostic value of automated point-of-care neuCD64 and cmCD169 analysis was assessed for detecting bacterial and viral infections at the emergency department. Additionally, their value as input for machine learning models was studied. A prospective observational cohort study in patients suspected of infection was performed at an emergency department. A fully automated point-of-care flow cytometer measured neuCD64, cmCD169, and additional leukocyte surface markers. Flow cytometry data were gated using the FlowSOM algorithm. Bacterial and viral infections were assessed in standardized clinical care. The sole and combined diagnostic value of the markers was investigated. Clustering based on unsupervised machine learning identified unique patient clusters. Eighty-six patients were included. Thirty-five had a bacterial infection, 30 had a viral infection, and 21 had no infection. neuCD64 was increased in bacterial infections (P < 0.001), with an area under the receiver operating characteristic curve (AUROC) of 0.73. cmCD169 was higher in virally infected patients (P < 0.001; AUROC 0.79). Multivariate analyses incorporating additional markers increased the AUROC for bacterial and viral infections to 0.86 and 0.93, respectively. The additional clustering identified 4 distinctive patient clusters based on infection type and outcome. Automated neuCD64 and cmCD169 determination can discriminate between bacterial and viral infections. These markers can be determined within 30 min, allowing fast infection diagnostics in the acute clinical setting.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Infecciones Bacterianas / Virosis Tipo de estudio: Observational_studies / Prognostic_studies Límite: Humans Idioma: En Revista: J Leukoc Biol Año: 2024 Tipo del documento: Article País de afiliación: Países Bajos

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Infecciones Bacterianas / Virosis Tipo de estudio: Observational_studies / Prognostic_studies Límite: Humans Idioma: En Revista: J Leukoc Biol Año: 2024 Tipo del documento: Article País de afiliación: Países Bajos