Massive Transfusion Protocol Predictive Modeling in the Modern Electronic Medical Record

William Shihao Lao; Jessica L Poisson; Cory J Vatsaas; Christopher J Dente; Allan D Kirk; Suresh K Agarwal; Steven N Vaslef

doi:10.1097/AS9.0000000000000109

Massive Transfusion Protocol Predictive Modeling in the Modern Electronic Medical Record

Ann Surg Open. 2021 Dec 14;2(4):e109. doi: 10.1097/AS9.0000000000000109. eCollection 2021 Dec.

Authors

William Shihao Lao¹, Jessica L Poisson², Cory J Vatsaas¹, Christopher J Dente^{3

4}, Allan D Kirk^{1

4}, Suresh K Agarwal^{1

4}, Steven N Vaslef^{1

4}

Affiliations

¹ From the Department of Surgery, Duke University Medical Center, Durham, NC.
² Department of Pathology, Duke University Medical Center, Durham, NC.
³ Department of Surgery, Emory University School of Medicine, Atlanta, GA.
⁴ Surgical Critical Care Initiative (SC2i), Bethesda, MD.

Abstract

Objectives: 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.

Background: 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.

Methods: 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.

Results: 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 P < 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.

Conclusions: 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.