Impact of Injury Severity on Dynamic Inflammation Networks Following Blunt Trauma

Abstract

Introduction: Clinical outcomes following trauma depend on the extent of injury and the host's response to injury, along with medical care. We hypothesized that dynamic networks of systemic inflammation manifest differently as a function of injury severity in human blunt trauma.

Study design: From a cohort of 472 blunt trauma survivors studied following institutional review board approval, three Injury Severity Score (ISS) subcohorts were derived after matching for age and sex: mild ISS (49 patients [33 males and 16 females, aged 42 ± 1.9 years; ISS 9.5 ± 0.4]); moderate ISS (49 patients [33 males and 16 females, aged 42 ± 1.9; ISS 19.9 ± 0.4]), and severe ISS (49 patients [33 males and 16 females, aged 42 ± 2.5 years; ISS 33 ± 1.1]). Multiple inflammatory mediators were assessed in serial blood samples. Dynamic Bayesian Network inference was utilized to infer causal relationships based on probabilistic measures.

Results: Intensive care unit length of stay, total length of stay, days on mechanical ventilation, Marshall Multiple Organ Dysfunction score, prevalence of prehospital hypotension and nosocomial infection, and admission lactate and base deficit were elevated as a function of ISS. Multiple circulating inflammatory mediators were significantly elevated in severe ISS versus moderate or mild ISS over both the first 24 h and out to 7 days after injury. Dynamic Bayesian Network suggested that interleukin 6 production in severe ISS was affected by monocyte chemotactic protein 1/CCL2, monokine inducible by interferon γ (MIG)/CXCL9, and IP-10/CXCL10; by monocyte chemotactic protein 1/CCL2 and MIG/CXCL9 in moderate ISS; and by MIG/CXCL9 alone in mild ISS over 7 days after injury.

Conclusions: Injury Severity Score correlates linearly with morbidity, prevalence of infection, and early systemic inflammatory connectivity of chemokines to interleukin 6.

Figure 1. Differences in the Abbreviated Injury Scale (AIS)

AIS scores were statistically significantly higher in the head and neck, chest, abdomen, and extremities regions in the severe injury sub-cohort when compared to mild and moderate injury sub-cohorts (*P<0.05 vs. mild injury analyzed by One-Way ANOVA).

Figure 2. Differences in biochemical parameters between mild, moderate, and severe injury patients at time of admission

(A) Plasma lactate levels assessed at the time of admission statistically significant higher in the severe injury sub-cohort when compared to mild and moderate injury sub-cohorts. (B) Base deficit (BD) assessed at the time of admission was statistically significantly higher in severe injury sub-cohort when compared to mild and moderate injury sub-cohorts. Values are mean ± SEM. (*P<0.05 vs. mild injury, **P<0.05 vs. moderate injury, analyzed by One-Way ANOVA).

Figure 3. Multiple organ dysfunction (MOD)

The severe injury sub-cohort exhibited a higher degree of MOD from day 1 up to day 7 post-injury when compared to mild and moderate injury sub-cohorts. (*P<0.05 vs. mild, **P<0.05 vs. moderate, analyzed by Two-Way ANOVA).

Figure 4. Dynamic Bayesian network (DyBN) inference suggests injury-graded impact of chemokines on IL-6 and other systemic inflammation biomarkers

(A) DyBN inference of the mild injury sub-cohort suggested that systemic IL-6 is affected by MIG/CXCL9 only. (B) DyBN inference of the moderate injury sub-cohort suggested that systemic IL-6 is affected jointly by MCP-1/CCL2 and MIG/CXCL9. (C) DyBN inference of the severe injury sub-cohort suggested that systemic IL-6 in the severe injury sub-cohort was affected jointly by MCP-1/CCL2, MIG/CXCL9, and IP-10/CXCL10.

Figure 4. Dynamic Bayesian network (DyBN) inference suggests injury-graded impact of chemokines on IL-6 and other systemic inflammation biomarkers

(A) DyBN inference of the mild injury sub-cohort suggested that systemic IL-6 is affected by MIG/CXCL9 only. (B) DyBN inference of the moderate injury sub-cohort suggested that systemic IL-6 is affected jointly by MCP-1/CCL2 and MIG/CXCL9. (C) DyBN inference of the severe injury sub-cohort suggested that systemic IL-6 in the severe injury sub-cohort was affected jointly by MCP-1/CCL2, MIG/CXCL9, and IP-10/CXCL10.

Figure 4. Dynamic Bayesian network (DyBN) inference suggests injury-graded impact of chemokines on IL-6 and other systemic inflammation biomarkers

(A) DyBN inference of the mild injury sub-cohort suggested that systemic IL-6 is affected by MIG/CXCL9 only. (B) DyBN inference of the moderate injury sub-cohort suggested that systemic IL-6 is affected jointly by MCP-1/CCL2 and MIG/CXCL9. (C) DyBN inference of the severe injury sub-cohort suggested that systemic IL-6 in the severe injury sub-cohort was affected jointly by MCP-1/CCL2, MIG/CXCL9, and IP-10/CXCL10.