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

Burton, Ross J., Raffray, Loïc, Moet, Linda M., Cuff, Simone M. ORCID: https://orcid.org/0000-0002-0546-3579, White, Daniel A., Baker, Sarah E. ORCID: https://orcid.org/0000-0002-7474-9757, Moser, Bernhard ORCID: https://orcid.org/0000-0002-4354-4572, O'Donnell, Valerie B. ORCID: https://orcid.org/0000-0003-4089-8460, Ghazal, Peter ORCID: https://orcid.org/0000-0003-0035-2228, Morgan, Matt P., Artemiou, Andreas ORCID: https://orcid.org/0000-0002-7501-4090 and Eberl, Matthias ORCID: https://orcid.org/0000-0002-9390-5348 2024. Conventional and unconventional T cell responses contribute to the prediction of clinical outcome and causative bacterial pathogen in sepsis patients. Clinical & Experimental Immunology 216 (3) , pp. 293-306. 10.1093/cei/uxae019

Preview

PDF - Published Version Available under License Creative Commons Attribution. Download (2MB) | Preview

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.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Medicine
Publisher:	Oxford University Press
ISSN:	0009-9104
Date of First Compliant Deposit:	4 March 2024
Date of Acceptance:	28 February 2024
Last Modified:	21 Nov 2024 13:24
URI:	https://orca.cardiff.ac.uk/id/eprint/166778

Actions (repository staff only)

Edit Item