Biostatistics/Epidemiology/Research Design Core, Center for Clinical and Translational Sciences, University of Texas Health Science Center at Houston, Houston, Texas, USA.
J Trauma Acute Care Surg. 2013 Jul;75(1 Suppl 1):S82-8. doi: 10.1097/TA.0b013e31828fa3d3.
Several predictive models have been developed to identify trauma patients who have had severe hemorrhage (SH) and may need a massive transfusion (MT) protocol. However, almost all these models define SH as the transfusion of 10 or more units of red blood cells (RBCs) within 24 hours of emergency department admission (also known as MT). This definition excludes some patients with SH, especially those who die before a 10th unit of RBCs could be transfused, which calls the validity of these prediction models into question. We show how a latent class model could improve the accuracy of identifying the SH patients.
Modeling SH classification as a latent variable, we estimate the posterior probability of a patient in SH based on emergency department admission variables (systolic blood pressure, heart rate, pH, hemoglobin), the 24-hour blood product use (plasma/RBC and platelet/RBC ratios), and 24-hour survival status. We define the SH subgroup as those having a posterior probability of 0.5 or greater. We compare our new classification of SH with that of the traditional MT using data from PROMMTT study.
Of the 1,245 patients, 913 had complete data, which were used in the latent class model. About 25.3% of patients were classified as SH. The overall agreement between the MT and SH classifications was 83.8%. However, among 49 patients who died before receiving the 10th unit of RBCs, 41 (84%) were classified as SH. Seven (87.5%) of the remaining eight patients who were not classified as SH had head injury.
Our definition of SH based on the aforementioned latent class model has an advantage of improving on the traditional MT definition by identifying SH patients who die before receiving the 10th unit of RBCs. We recommend further improvements to more accurately classify SH patients, which could replace the traditional definition of MT for use in developing prediction algorithms.
已经开发出了几种预测模型，以识别发生严重出血（SH）并可能需要大量输血（MT）方案的创伤患者。然而，几乎所有这些模型都将 SH 定义为在急诊科就诊后 24 小时内输注 10 个或更多单位的红细胞（RBC）（也称为 MT）。这种定义排除了一些存在 SH 的患者，特别是那些在输注第 10 单位 RBC 之前死亡的患者，这使得这些预测模型的有效性受到质疑。我们展示了如何使用潜在类别模型来提高识别 SH 患者的准确性。
我们将 SH 分类建模为潜在变量，根据急诊科就诊时的变量（收缩压、心率、pH 值、血红蛋白）、24 小时血液制品使用情况（血浆/RBC 和血小板/RBC 比值）以及 24 小时生存状态，估计患者发生 SH 的后验概率。我们将 SH 亚组定义为后验概率为 0.5 或更高的患者。我们使用 PROMMTT 研究的数据比较了我们的 SH 新分类与传统 MT 的分类。
在 1245 名患者中，有 913 名患者有完整的数据，用于潜在类别模型。约 25.3%的患者被归类为 SH。MT 和 SH 分类之间的总体一致性为 83.8%。然而，在 49 名在接受第 10 单位 RBC 之前死亡的患者中，有 41 名（84%）被归类为 SH。在其余未被归类为 SH 的 8 名患者中，有 7 名（87.5%）有头部损伤。
我们基于上述潜在类别模型的 SH 定义具有优于传统 MT 定义的优势，通过识别在接受第 10 单位 RBC 之前死亡的 SH 患者来提高识别 SH 患者的准确性。我们建议进一步改进，以更准确地对 SH 患者进行分类，这可以替代传统的 MT 定义，用于开发预测算法。
