Lao William Shihao, Poisson Jessica L, Vatsaas Cory J, Dente Christopher J, Kirk Allan D, Agarwal Suresh K, Vaslef Steven N
From the Department of Surgery, Duke University Medical Center, Durham, NC.
Department of Pathology, Duke University Medical Center, Durham, NC.
Ann Surg Open. 2021 Dec 14;2(4):e109. doi: 10.1097/AS9.0000000000000109. eCollection 2021 Dec.
Integrate a predictive model for massive transfusion protocol (MTP) activation and delivery in the electronic medical record (EMR) using prospectively gathered data; externally validate the model and assess the accuracy and precision of the model over time.
The Emory model for predicting MTP using only four input variables was chosen to be integrated into our hospital's EMR to provide a real time clinical decision support tool. The continuous variable output allows for periodic re-calibration of the model to optimize sensitivity and specificity.
Prospectively collected data from level 1 and 2 trauma activations were used to input heart rate, systolic blood pressure, base excess (BE) and mechanism of injury into the EMR-integrated model for predicting MTP activation and delivery. MTP delivery was defined as: 6 units of packed red blood cells/6 hours (MTP1) or 10 units in 24 hours (MTP2). The probability of MTP was reported in the EMR. ROC and PR curves were constructed at 6, 12, and 20 months to assess the adequacy of the model.
Data from 1162 patients were included. Areas under ROC for MTP activation, MTP1 and MTP2 delivery at 6, 12, and 20 months were 0.800, 0.821, and 0.831; 0.796, 0.861, and 0.879; and 0.809, 0.875, and 0.905 (all < 0.001). The areas under the PR curves also improved, reaching values at 20 months of 0.371, 0.339, and 0.355 for MTP activation, MTP1 delivery, and MTP2 delivery.
A predictive model for MTP activation and delivery was integrated into our EMR using prospectively collected data to externally validate the model. The model's performance improved over time. The ability to choose the cut-points of the ROC and PR curves due to the continuous variable output of probability of MTP allows one to optimize sensitivity or specificity.
利用前瞻性收集的数据，将大量输血方案（MTP）激活和输血的预测模型整合到电子病历（EMR）中；对该模型进行外部验证，并评估其随时间推移的准确性和精确性。
仅使用四个输入变量预测MTP的埃默里模型被选择整合到我院的EMR中，以提供实时临床决策支持工具。连续变量输出允许对模型进行定期重新校准，以优化敏感性和特异性。
前瞻性收集的1级和2级创伤激活数据用于将心率、收缩压、碱剩余（BE）和损伤机制输入到整合于EMR中的预测MTP激活和输血的模型中。MTP输血定义为：6单位浓缩红细胞/6小时（MTP1）或24小时内10单位（MTP2）。EMR中报告了MTP的概率。在6、12和20个月时构建ROC和PR曲线，以评估模型的充分性。
纳入了1162例患者的数据。6、12和20个月时，MTP激活、MTP1和MTP2输血的ROC曲线下面积分别为0.800、0.821和0.831；0.796、0.861和0.879；以及0.809、0.875和0.905（均<0.001）。PR曲线下面积也有所改善，在20个月时，MTP激活、MTP1输血和MTP2输血的值分别为0.371、0.339和0.355。
利用前瞻性收集的数据将MTP激活和输血的预测模型整合到我院的EMR中，以对该模型进行外部验证。该模型的性能随时间推移有所改善。由于MTP概率的连续变量输出，可以选择ROC和PR曲线的切点，从而优化敏感性或特异性。
