Integrated immune and endothelial profiling predicts 90-day mortality in postoperative sepsis and septic shock

Sepsis and septic shock remain major causes of mortality in critically ill postoperative patients, largely because of the lack of reliable biomarkers for early risk stratification. The interplay between immune dysfunction and endothelial activation is key in the progression to multiorgan failure, however phenotypic characterisation of circulating endothelial subpopulations remains limited.

A Prospective multicentre study included 219 postoperative patients (Non-septic ICU patients, sepsis, septic shock). Peripheral Blood Mononuclear Cells were analysed using high-dimensional spectral flow cytometry. Both supervised gating strategies and high-dimensional unsupervised analyses (UMAP and FlowSOM) were applied to identify immune and endothelial cell subsets. Associations with 90-day mortality were assessed using univariate and multivariate Cox proportional hazards models, refined with LASSO-Cox regression, and integrated into a risk score. The predictive performance of this cellular risk score was compared with SOFA and APACHE II scores using ROC curves and survival analysis. Findings were further validated using publicly available single-cell RNA datasets.

Two B cell subsets (plasmablasts/IgG⁺ and IgD^-/IgM⁺ memory B cells) and endothelial cluster (CD36^-/CD16⁺) were independently associated with 90-day mortality. The integrated risk score stratified patients into three groups with significantly different survival outcomes (log-rank p = 0.0009), outperformed SOFA and APACHE II (AUC 0.935 vs. 0.751-0.804). Transcriptomic validation confirmed the presence and dysfunction of endothelial clusters, reinforcing their prognostic relevance.

The combination of immune and endothelial profiling provides a robust cellular signature that improves the prognostic stratification in postoperative sepsis. These biomarkers may support treatment and guide therapeutic strategies aimed at restoring immune-endothelial homoeostasis.

This work was supported by the Instituto de Salud Carlos III [grant numbers: PI24/00754, FI25/00242 and CIBERINFEC CB21/13/00051], Junta de Castilla y León [GRS 2782/A2/2023, GRS 2804/A1/2023].