Distinct immune profiles and clinical outcomes in sepsis subphenotypes based on temperature trajectories

Abstract

Purpose: Sepsis is a heterogeneous syndrome. Identification of sepsis subphenotypes with distinct immune profiles could lead to targeted therapies. This study investigates the immune profiles of patients with sepsis following distinct body temperature patterns (i.e., temperature trajectory subphenotypes).

Methods: Hospitalized patients from four hospitals between 2018 and 2022 with suspicion of infection were included. A previously validated temperature trajectory algorithm was used to classify study patients into temperature trajectory subphenotypes. Microbiological profiles, clinical outcomes, and levels of 31 biomarkers were compared between these subphenotypes.

Results: The 3576 study patients were classified into four temperature trajectory subphenotypes: hyperthermic slow resolvers (N = 563, 16%), hyperthermic fast resolvers (N = 805, 23%), normothermic (N = 1693, 47%), hypothermic (N = 515, 14%). The mortality rate was significantly different between subphenotypes, with the highest rate in hypothermics (14.2%), followed by hyperthermic slow resolvers 6%, normothermic 5.5%, and lowest in hyperthermic fast resolvers 3.6% (p < 0.001). After multiple testing correction for the 31 biomarkers tested, 20 biomarkers remained significantly different between temperature trajectories: angiopoietin-1 (Ang-1), C-reactive protein (CRP), feline McDonough sarcoma-like tyrosine kinase 3 ligand (Flt-3l), granulocyte colony stimulating factor (G-CSF), granulocyte-macrophage colony stimulating factor (GM-CSF), interleukin (IL)-15, IL-1 receptor antagonist (RA), IL-2, IL-6, IL-7, interferon gamma-induced protein 10 (IP-10), monocyte chemoattractant protein-1 (MCP-1), human macrophage inflammatory protein 3 alpha (MIP-3a), neutrophil gelatinase-associated lipocalin (NGAL), pentraxin-3, thrombomodulin, tissue factor, soluble triggering receptor expressed on myeloid cells-1 (sTREM-1), and vascular cellular adhesion molecule-1 (vCAM-1).The hyperthermic fast and slow resolvers had the highest levels of most pro- and anti-inflammatory cytokines. Hypothermics had suppressed levels of most cytokines but the highest levels of several coagulation markers (Ang-1, thrombomodulin, tissue factor).

Conclusion: Sepsis subphenotypes identified using the universally available measurement of body temperature had distinct immune profiles. Hypothermic patients, who had the highest mortality rate, also had the lowest levels of most pro- and anti-inflammatory cytokines.

Keywords: Artificial intelligence; Fever; Phenotypes; Sepsis; Temperature.

Fig. 1

Temperature trajectory subphenotypes. Presented are the mean and 95% confidence interval of the subphenotypes measurements at each hour over the first 72 h of hospital admission

Fig. 2

The four patterns of biomarkers are presented. The levels of biomarkers represent the mean and standard error around the mean for each subphenotype. Pattern 1—highest in hyperthermic fast resolvers; Pattern 2—highest in hyperthermic slow resolvers; Pattern 3—equally elevated in hyperthermic cohorts; Pattern 4—highest in hypothermics

Fig. 3

Composite inflammatory and coagulation scores. HSR hyperthermic slow resolvers, HFR hyperthermic fast resolvers; NT normothermic, HT hypothermic. Of the significant biomarkers, the following were categorized as inflammatory markers: CRP, E-selectin, Flt-3l, G-CSF, GM-CSF, IL-15, IL-1RA, IL-2, IL-6, IL-7, IP-10, MCP-1, MIP-3α, NGAL, pentraxin-3, sTREM-1, and VCAM-1. The following biomarkers were categorized as coagulation markers: thrombomodulin, tissue factor, and Ang-1. The median value of each biomarker amongst non-survivors was used to normalize the values and the normalized values were averaged to calculate the inflammatory and coagulation scores. ANOVA testing was used to compare the mean values of the scores between the 4 subphenotypes (p < 0.001 for inflammatory and coagulation scores). Pairwise comparisons between HT and each of the other subphenotypes were performed, and significant comparisons are indicated by asterisk, with the color of the comparison subphenotype. In the inflammatory score, HT had significantly lower scores compared to HSR and HFR (p < 0.001 for both comparisons). In the coagulation score, HT had significantly higher scores than HSR, HFR, and NT (p < 0.001 in all 3 comparisons)

Fig. 4

Multinomial regression of association of variables on subphenotype membership. Presented are the SHAP values for variables that were significantly associated with subphenotype membership in the multinomial regression for hyperthermic slow resolvers (HSR), hyperthermic fast resolvers (HFR), normothermic (NT), and hypothermic (HT). The variables are arranged by order of importance in predicting the subphenotype membership for patients who were in that subphenotype. The values of the variables were normalized within the subphenotype so that red values represent values closer to the maximum while blue values represent values closer to the minimum. In HSR, IL-15 is the most important predictor, with higher values (red) increasing the probability of membership, while lower values (blue) decreasing the probability of membership. Conversely, in HSR, younger age (blue) predicts membership, while older age (red) decreases the probability of membership. In HT, sTREM-1 is the most important predictor, with higher values (red) predicting membership