加速从即时血栓弹力描记仪设备中获得具有临床相关性的参数估计值。
Accelerating availability of clinically-relevant parameter estimates from thromboelastogram point-of-care device.
机构信息
From the Department of Chemical and Petroleum Engineering (M.A.P., R.S.P., G.C.), Swanson School of Engineering, Department of Critical Care Medicine (R.S.P., M.D.N., J.L.S., G.C.), Department of Surgery (M.D.N., J.L.S.), and Department of Bioengineering (R.S.P., G.C.), Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA.
出版信息
J Trauma Acute Care Surg. 2020 May;88(5):654-660. doi: 10.1097/TA.0000000000002608.
BACKGROUND
Modeling approaches offer a novel way to detect and predict coagulopathy in trauma patients. A dynamic model, built and tested on thromboelastogram (TEG) data, was used to generate a virtual library of over 160,000 simulated RapidTEGs. The patient-specific parameters are the initial platelet count, platelet activation rate, thrombus growth rate, and lysis rate (P(0), k1, k2, and k3, respectively).
METHODS
Patient data from both STAAMP (n = 182 patients) and PAMPer (n = 111 patients) clinical trials were collected. A total of 873 RapidTEGs were analyzed. One hundred sixteen TEGs indicated maximum amplitude (MA) below normal and 466 TEGs indicated lysis percent above normal. Each patient's TEG response was compared against the virtual library of TEGs to determine library trajectories having the least sum-of-squared error versus the patient TEG up to each specified evaluation time ∈ (3, 4, 5, 7.5, 10, 15, 20 minutes). Using 10 nearest-neighbor trajectories, a logistic regression was performed to predict if the patient TEG indicated MA below normal (<50 mm), lysis percent 30 minutes after MA (LY30) greater than 3%, and/or blood transfusion need using the parameters from the dynamic model.
RESULTS
The algorithm predicts abnormal MA values using the initial 3 minutes of RapidTEG data with a median area under the curve of 0.95, and improves with more data to 0.98 by 10 minutes. Prediction of future platelet and packed red blood cell transfusion based on parameters at 4 and 5 minutes, respectively, provides equivalent predictions to the traditional TEG parameters in significantly less time. Dynamic model parameters could not predict abnormal LY30 or future fresh-frozen plasma transfusion.
CONCLUSION
This analysis could be incorporated into TEG software and workflow to quickly estimate if the MA would be below or above threshold value within the initial minutes following a TEG, along with an estimate of what blood products to have on hand.
LEVEL OF EVIDENCE
Therapeutic/Care Management: Level IV.
背景
建模方法为检测和预测创伤患者的凝血功能障碍提供了一种新途径。一种基于血栓弹力图（TEG）数据建立并测试的动态模型，生成了一个超过 16 万例模拟 RapidTEG 的虚拟库。患者的特定参数为初始血小板计数、血小板激活率、血栓生成率和溶解率（分别为 P(0)、k1、k2 和 k3）。
方法
收集了来自 STAAMP（n=182 例患者）和 PAMPer（n=111 例患者）临床试验的数据。共分析了 873 例 RapidTEG。116 例 TEG 的最大振幅（MA）低于正常水平，466 例 TEG 的溶解百分比高于正常水平。将每位患者的 TEG 反应与虚拟 TEG 库进行比较，以确定在每个指定的评估时间∈（3、4、5、7.5、10、15、20 分钟）内，库轨迹与患者 TEG 的最小均方误差。使用 10 个最近邻轨迹，通过逻辑回归预测患者 TEG 是否指示 MA 低于正常水平（<50mm）、MA 后 30 分钟的溶解百分比（LY30）大于 3%以及/或需要输血，使用动态模型中的参数。
结果
该算法使用 RapidTEG 数据的前 3 分钟预测异常 MA 值，曲线下面积中位数为 0.95，在 10 分钟时增加到 0.98。使用 4 分钟和 5 分钟时的参数分别预测未来血小板和浓缩红细胞输血，可提供与传统 TEG 参数等效的预测，但时间明显缩短。动态模型参数无法预测异常 LY30 或未来新鲜冰冻血浆输血。
结论
这项分析可以整合到 TEG 软件和工作流程中，以快速估计 TEG 后最初几分钟内 MA 是否低于或高于阈值，并估计需要准备哪些血液制品。
证据水平
治疗/护理管理：IV 级。
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