title: Impact of Injury Severity on Dynamic Inflammation Networks Following Blunt Trauma.
abstract: BACKGROUND
  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.
  
  
  METHODS
  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.
authors.affiliation: *Department of Surgery and †Center for Inflammation and Regenerative Modeling, McGowan Institute for Regenerative Medicine, and ‡Department of Critical Care Medicine, University of Pittsburgh, Pennsylvania.
authors.forename: Khalid
authors.initials: K
authors.lastname: Almahmoud
authors.forename: Rami A
authors.initials: RA
authors.lastname: Namas
authors.forename: Othman
authors.initials: O
authors.lastname: Abdul-Malak
authors.forename: Akram M
authors.initials: AM
authors.lastname: Zaaqoq
authors.forename: Ruben
authors.initials: R
authors.lastname: Zamora
authors.forename: Brian S
authors.initials: BS
authors.lastname: Zuckerbraun
authors.forename: Jason
authors.initials: J
authors.lastname: Sperry
authors.forename: Andrew B
authors.initials: AB
authors.lastname: Peitzman
authors.forename: Timothy R
authors.initials: TR
authors.lastname: Billiar
authors.forename: Yoram
authors.initials: Y
authors.lastname: Vodovotz
mesh_terms: D000328:Adult; D001499:Bayes Theorem; D015415:Biomarkers / Q000097:blood; D018932:Chemokine CCL2 / Q000097:blood; D054357:Chemokine CXCL10 / Q000097:blood; D054370:Chemokine CXCL9 / Q000097:blood; D003422:Critical Care; D005260:Female; D005786:Gene Expression Regulation; D006801:Humans; D007022:Hypotension / Q000150:complications; D007249:Inflammation / Q000473:pathology*; D015850:Interleukin-6 / Q000097:blood; D007902:Length of Stay; D008297:Male; D008875:Middle Aged; D012121:Respiration, Artificial; D012189:Retrospective Studies; D012720:Severity of Illness Index; D014949:Wounds, Nonpenetrating / Q000097:blood / Q000175:diagnosis*
chemical_list: D015415:Biomarkers; C111835:CCL2 protein, human; C114753:CXCL10 protein, human; C080810:CXCL9 protein, human; D018932:Chemokine CCL2; D054357:Chemokine CXCL10; D054370:Chemokine CXCL9; C508600:IL6 protein, human; D015850:Interleukin-6