A combined risk model for the multi-encompassing identification of heterogeneities of prognoses, biological pathway variations and immune states for sepsis patients

Abstract

Background Sepsis is a highly heterogeneous syndrome with stratified severity levels and immune states. Even in patients with similar clinical appearances, the underlying signal transduction pathways are significantly different. To identify the heterogeneities of sepsis from multiple angles, we aimed to establish a combined risk model including the molecular risk score for rapid mortality prediction, pathway risk score for the identification of biological pathway variations, and immunity risk score for guidance with immune-modulation therapy. Methods We systematically searched and screened the mRNA expression profiles of patients with sepsis in the Gene Expression Omnibus public database. The screened datasets were divided into a training cohort and a validation cohort. In the training cohort, authentic prognostic predictor characteristics (differentially expressed mRNAs, pathway activity variations and immune cells) were screened for model construction through bioinformatics analysis and univariate Cox regression, and a P value less than 0.05 of univariate Cox regression on 28-day mortality was set as the cut-off value. The combined risk model was finally established by the decision tree algorithm. In the validation cohort, the model performance was assessed and validated by C statistics and the area under the receiver operating characteristic curve (AUC). Additionally, the current models were further compared in clinical value with traditional indicators, including procalcitonin (PCT) and interleukin-8 (IL-8). Results Datasets from two sepsis cohort studies with a total of 585 consecutive sepsis patients admitted to two intensive care units were downloaded as the training cohort (n = 479) and external validation cohort (n = 106). In the training cohort, 15 molecules, 20 pathways and 4 immune cells were eventually enrolled in model construction. These prognostic factors mainly reflected hypoxia, cellular injury, metabolic disorders and immune dysregulation in sepsis patients. In the validation cohort, the AUCs of the molecular model, pathway model, immune model, and combined model were 0.81, 0.82, 0.62 and 0.873, respectively. The AUCs of the traditional biomarkers (PCT and IL-8) were 0.565 and 0.585, respectively. The survival analysis indicated that patients in the high-risk group identified by models in the current study had a poor prognosis (P < 0.05). The above results indicated that the models in this study are all superior to the traditional biomarkers for the predicting the prognosis of sepsis patients. Furthermore, the current study provides some therapeutic recommendations for patients with high risk scores identified by the three submodels. Conclusions In summary, the present study provides opportunities for bedside tests that could quantitatively and rapidly measure heterogeneous prognosis, underlying biological pathway variations and immune dysfunction in sepsis patients. Further therapeutic recommendations for patients with high risk scores could improve the therapeutic system for sepsis.

Immunological risk factors for sepsis-associated delirium and mortality in ICU patients

“…For critical patients, dysregulated immune response is one of the key factors for determining the severity of disease progression and prognosis ( 10 – 12 ). For instance, sepsis occurs when the body’s immune system overreacts to infection and causes damage to its own tissues and organs ( 13 ).…”

Basophils absence predicts poor prognosis and indicates immunosuppression of patients in intensive care units

“…Absolute lymphocyte subsets levels and lymphocyte counts were downregulated in sepsis, and the proportion of Tregs was positively correlated with disease progression 11 . Moreover, myeloid subsets were also found to be associated with poor prognosis in patients with sepsis 12 . Furthermore, all the initial, maximum, mean, and difference values of a specific parameter from blood tests were reported to affect ICU patients’ prognosis 13 – 15 .…”

Integrated analysis of multi-omics data reveals T cell exhaustion in sepsis

“…The heterogeneity of sepsis conditions and diverse sources of infection may contribute to this result ( 7 ). Conventional single-gene-based approaches are ineffective in distinguishing sepsis from traditional markers such as procalcitonin (PCT) and interleukin-8 (IL-8) ( 8 ). Recombinant human activated protein C (rhAPC) is currently the only drug approved by the U.S. Food and Drug Administration (FDA) for the treatment of severe sepsis.…”