A reactive monocyte subset characterized by low expression of CD91 is expanded during sterile and septic inflammation.

OBJECTIVES: This study was undertaken to assess CD91 expression on monocytes and changes in monocyte subset distribution during acute tissue damage and bloodstream infection (BSI). METHODS: We investigated blood specimens from healthy individuals, trauma and cardiac surgery patients as a model of tissue damage, and patients with BSI, by flow cytometry using a panel of antibodies comprising CD45, HLA-DR, CD14, CD16 and CD91 for the identification of monocyte subsets. RESULTS: While infrequent in healthy subjects, CD91low/neg monocyte levels were markedly high in BSI, trauma and after cardiac surgery. This monocyte subset expanded up to 15-fold in both patient cohorts, whereas CD14+CD16+ inflammatory monocytes were multiplied by a factor of 5 only. CD14+CD91low monocytes displayed a significantly lower density of HLA-DR and markedly reduced expression of CD300e, compared to the other subsets. They also expressed high levels of myeloperoxidase and showed robust phagocytic and oxidative burst activity. CONCLUSIONS: Expansion of CD91low monocytes is a sensitive marker of acute inflammatory states of infectious and non-infectious etiology.

Machine learning identification of specific changes in myeloid cell phenotype during bloodstream infections.

The early identification of bacteremia is critical for ensuring appropriate treatment of nosocomial infections in intensive care unit (ICU) patients. The aim of this study was to use flow cytometric data of myeloid cells as a biomarker of bloodstream infection (BSI). An eight-color antibody panel was used to identify seven monocyte and two dendritic cell subsets. In the learning cohort, immunophenotyping was applied to (1) control subjects, (2) postoperative heart surgery patients, as a model of noninfectious inflammatory responses, and (3) blood culture-positive patients. Of the complex changes in the myeloid cell phenotype, a decrease in myeloid and plasmacytoid dendritic cell numbers, increase in CD14+CD16+ inflammatory monocyte numbers, and upregulation of neutrophils CD64 and CD123 expression were prominent in BSI patients. An extreme gradient boosting (XGBoost) algorithm called the "infection detection and ranging score" (iDAR), ranging from 0 to 100, was developed to identify infection-specific changes in 101 phenotypic variables related to neutrophils, monocytes and dendritic cells. The tenfold cross-validation achieved an area under the receiver operating characteristic (AUROC) of 0.988 (95% CI 0.985-1) for the detection of bacteremic patients. In an out-of-sample, in-house validation, iDAR achieved an AUROC of 0.85 (95% CI 0.71-0.98) in differentiating localized from bloodstream infection and 0.95 (95% CI 0.89-1) in discriminating infected from noninfected ICU patients. In conclusion, a machine learning approach was used to translate the changes in myeloid cell phenotype in response to infection into a score that could identify bacteremia with high specificity in ICU patients.