Conventional and unconventional T-cell responses contribute to the prediction of clinical outcome and causative bacterial pathogen in sepsis patients

Abstract

Sepsis is characterised by a dysfunctional host response to infection culminating in life-threatening organ failure that requires complex patient management and rapid intervention. Timely diagnosis of the underlying cause of sepsis is crucial, and identifying those at risk of complications and death is imperative for triaging treatment and resource allocation. Here, we explored the potential of explainable machine learning models to predict mortality and causative pathogen in sepsis patients. By using a modelling pipeline employing multiple feature selection algorithms, we demonstrate the feasibility to identify integrative patterns from clinical parameters, plasma biomarkers and extensive phenotyping of blood immune cells. Whilst no single variable had sufficient predictive power, models that combined five and more features showed a macro area under the curve (AUC) of 0.85 to predict 90 day mortality after sepsis diagnosis, and a macro AUC of 0.86 to discriminate between Gram-positive and Gram-negative bacterial infections. Parameters associated with the cellular immune response contributed the most to models predictive of 90 day mortality, most notably, the proportion of T cells among PBMCs, together with expression of CXCR3 by CD4+ T cells and CD25 by mucosal-associated invariant T (MAIT) cells. Frequencies of Vδ2+ γδ T cells had the most profound impact on the prediction of Gram-negative infections, alongside other T cell-related variables and total neutrophil count. Overall, our findings highlight the added value of measuring the proportion and activation patterns of conventional and unconventional T cells in the blood of sepsis patients in combination with other immunological, biochemical and clinical parameters.

Blood immune profiles reveal a CXCR3/CCR5 axis of dysregulation in early sepsis

“…Finally, sepsis is a condition that prevalently affect elderly individuals and age-related changes in immunity as well as dysfunctions due to existing underlying conditions potentially increase the difficulty in discriminating between early sepsis and other forms of systemic inflammation, even based on extensive blood immune profiling. Note that similar issues were raised in a very recent study using machine learning models to predict mortality among a cohort of 77 sepsis patients, and which identified the frequency of T cells and the expression of CXCR3 on CD4 T cells as dominant parameters 86 .…”

“…Note that similar issues were raised in a very recent study using machine learning models to predict mortality among a cohort of 77 sepsis patients, and which identified the frequency of T cells and the expression of CXCR3 on CD4 T cells as dominant parameters 86 .…”