title: Thrombelastographic pattern recognition in renal disease and trauma.
abstract: BACKGROUND
  Thrombelastography (TEG) is a viscoelastic hemostatic assay. We have observed that end-stage renal disease (ESRD) and trauma-induced coagulopathy (TIC) produce distinctive TEG tracings. We hypothesized that rigorously definable TEG patterns could discriminate between healthy controls and patients with ESRD and TIC.
  
  
  METHODS
  TEG was performed on blood from ESRD patients (n = 54) and blood from trauma patients requiring a massive blood transfusion (n = 16). Plots of independent TEG parameters were analyzed for patterns coupled to disease state, compared with controls. Decision trees for taxonomic classification were then built using the "R-Project" statistical software.
  
  
  RESULTS
  Minimally overlapping clusters of TEG results were observed for the three patient groups when coordinate pairs of maximum amplitude (MA) and TEG-activated clotting time (ACT) were plotted on orthogonal axes. Based on these groupings, a taxonomical classification tree was constructed using MA and TEG ACT. Branch points were set at an ACT of 103 s, and these branches subdivided for MA at 60.8 mm for the high ACT branch and 72.6 mm for the low ACT branch, providing a correct classification rate of 93.4%.
  
  
  CONCLUSIONS
  ESRD and TIC demonstrate distinct TEG patterns. The coagulopathy of ESRD is typified by a prolonged enzymatic phase of clot formation, with normal-to-elevated final clot strength. Conversely, TIC is typified by prolonged clot formation and weakened clot strength. Our taxonomic categorization constitutes a rigorous system for the algorithmic interpretation of TEG based on cluster analysis. This will form the basis for clinical decision support software for viscoelastic hemostatic assays.
authors.affiliation: Department of Surgery, University of Colorado-Denver, Aurora, Colorado. Electronic address: michael.chapman@ucdenver.edu.
authors.forename: Michael P
authors.initials: MP
authors.lastname: Chapman
authors.affiliation: Department of Surgery, University of Colorado-Denver, Aurora, Colorado; Department of Surgery, Denver Health Medical Center, Denver, Colorado.
authors.forename: Ernest E
authors.initials: EE
authors.lastname: Moore
authors.affiliation: Department of Surgery, University of Colorado-Denver, Aurora, Colorado.
authors.forename: Dominykas
authors.initials: D
authors.lastname: Burneikis
authors.affiliation: Department of Surgery, University of Colorado-Denver, Aurora, Colorado.
authors.forename: Hunter B
authors.initials: HB
authors.lastname: Moore
authors.affiliation: Department of Surgery, University of Colorado-Denver, Aurora, Colorado.
authors.forename: Eduardo
authors.initials: E
authors.lastname: Gonzalez
authors.affiliation: Department of Surgery, University of Colorado-Denver, Aurora, Colorado.
authors.forename: Kelsey C
authors.initials: KC
authors.lastname: Anderson
authors.affiliation: Department of Surgery, University of Colorado-Denver, Aurora, Colorado.
authors.forename: Christopher R
authors.initials: CR
authors.lastname: Ramos
authors.affiliation: Department of Surgery, University of Colorado-Denver, Aurora, Colorado.
authors.forename: Anirban
authors.initials: A
authors.lastname: Banerjee
mesh_terms: D016000:Cluster Analysis; D006801:Humans; D007676:Kidney Failure, Chronic / Q000097:blood*; D013916:Thrombelastography; D014914:Whole Blood Coagulation Time; D014947:Wounds and Injuries / Q000097:blood*
keywords: Classification tree analysis; Coagulopathy; Hemodialysis; Hypercoagulability; Machine learning; Pattern recognition; Renal disease; Taxonomy; Thrombelastography; Trauma; Trauma-induced coagulopathy; Viscoelastic hemostatic assay