Chapman Michael P, Moore Ernest E, Burneikis Dominykas, Moore Hunter B, Gonzalez Eduardo, Anderson Kelsey C, Ramos Christopher R, Banerjee Anirban
Department of Surgery, University of Colorado-Denver, Aurora, Colorado.
Department of Surgery, University of Colorado-Denver, Aurora, Colorado; Department of Surgery, Denver Health Medical Center, Denver, Colorado.
J Surg Res. 2015 Mar;194(1):1-7. doi: 10.1016/j.jss.2014.12.012. Epub 2014 Dec 10.
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.
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.
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%.
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.
血栓弹力图(TEG)是一种粘弹性止血检测方法。我们观察到终末期肾病(ESRD)和创伤性凝血病(TIC)会产生独特的TEG描记图。我们假设,严格可定义的TEG模式可以区分健康对照者与ESRD和TIC患者。
对ESRD患者的血液(n = 54)和需要大量输血的创伤患者的血液(n = 16)进行TEG检测。分析独立TEG参数的图表,以寻找与疾病状态相关的模式,并与对照组进行比较。然后使用“R项目”统计软件构建分类分类决策树。
当在正交轴上绘制最大振幅(MA)和TEG激活凝血时间(ACT)的坐标对时,观察到三组患者的TEG结果有最小程度的重叠聚类。基于这些分组,使用MA和TEG ACT构建了一个分类树。分支点设定在ACT为103秒处,这些分支在高ACT分支处以MA 60.8毫米、低ACT分支处以MA 72.6毫米进一步细分,正确分类率为93.4%。
ESRD和TIC表现出不同的TEG模式。ESRD的凝血病以凝血形成的酶促期延长为特征,最终凝血强度正常至升高。相反,TIC的特征是凝血形成延长和凝血强度减弱。我们的分类方法构成了一个基于聚类分析对TEG进行算法解释的严格系统。这将为粘弹性止血检测的临床决策支持软件奠定基础。