Abstract
The immune response to major trauma has been analysed mainly within post-hospital admission settings where the inflammatory response is already underway and the early drivers of clinical outcome cannot be readily determined. Thus, there is a need to better understand the immediate immune response to injury and how this might influence important patient outcomes such as multi-organ dysfunction syndrome (MODS). In this study, we have assessed the immune response to trauma in 61 patients at three different post-injury time points (ultra-early (<=1 h), 4–12 h, 48–72 h) and analysed relationships with the development of MODS. We developed a pipeline using Absolute Shrinkage and Selection Operator and Elastic Net feature selection methods that were able to identify 3 physiological features (decrease in neutrophil CD62L and CD63 expression and monocyte CD63 expression and frequency) as possible biomarkers for MODS development. After univariate and multivariate analysis for each feature alongside a stability analysis, the addition of these 3 markers to standard clinical trauma injury severity scores yields a Generalized Liner Model (GLM) with an average Area Under the Curve value of 0.92 ± 0.06. This performance provides an 8% improvement over the Probability of Survival (PS14) outcome measure and a 13% improvement over the New Injury Severity Score (NISS) for identifying patients at risk of MODS.
Highlights
Trauma is responsible for the deaths of 4.9 million people worldwide according to the latest World Health Organization (WHO) reports[1], with two thirds of patients dying as a result of sequelae other than the immediate injury and haemorrhage[2]
All the data sets contain information related to both immunological and clinical parameters with the immunological variables belonging to the following categories: neutrophil functional analysis (response to formyl-methionine-leucine-phenylalanine), monocyte response to lipopolysaccharides (LPS) stimulation, haematological, cell free DNA (cfDNA) analysis and serum analysis
Due to the high correlation between New Injury Severity Score (NISS) and PS14 and the fact that they aggregate complex information such as gravity of the wound to estimate the extent of trauma damage, they are the predominant selected features at all time points, analysis was performed in each time point separately (Fig. 1) to uncover the other important features
Summary
Trauma is responsible for the deaths of 4.9 million people worldwide according to the latest World Health Organization (WHO) reports[1], with two thirds of patients dying as a result of sequelae other than the immediate injury and haemorrhage[2]. Secondary complications such as acute respiratory distress syndrome (ARDS), nosocomial infections, sepsis or multi-organ dysfunction syndrome (MODS) with multi organ failure (MOF) at the end of the spectrum, have become increasingly significant, creating an urgent need to develop novel approaches by which to identify patients at risk. Activated platelets and neutrophil extracellular trap (NET) generation are associated with organ dysfunction[10]
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