Early Prediction of Massive Transfusion for Patients With Traumatic Hemorrhage: Development of a Multivariable Machine Learning Model

Objective:. Develop a novel machine learning (ML) model to rapidly identify trauma patients with severe hemorrhage at risk of early mortality. Background:. The critical administration threshold (CAT, 3 or more units of red blood cells in a 60-minute period) indicates severe hemorrhage and predicts mortality, whereas early identification of such patients improves survival. Methods:. Patients from the PRospective, Observational, Multicenter, Major Trauma Transfusion and Pragmatic, Randomized Optimal Platelet, and Plasma Ratio studies were identified as either CAT+ or CAT−. Candidate variables were separated into 4 tiers based on the anticipated time of availability during the patient’s assessment. ML models were created with the stepwise addition of variables and compared with the baseline performance of the assessment of blood consumption (ABC) score for CAT+ prediction using a cross-validated training set and a hold-out validation test set. Results:. Of 1245 PRospective, Observational, Multicenter, Major Trauma Transfusion and 680 Pragmatic, Randomized Optimal Platelet and Plasma Ratio study patients, 1312 were included in this analysis, including 862 CAT+ and 450 CAT−. A CatBoost gradient-boosted decision tree model performed best. Using only variables available prehospital or on initial assessment (Tier 1), the ML model performed superior to the ABC score in predicting CAT+ patients [area under the receiver-operator curve (AUC = 0.71 vs 0.62)]. Model discrimination increased with the addition of Tier 2 (AUC = 0.75), Tier 3 (AUC = 0.77), and Tier 4 (AUC = 0.81) variables. Conclusions:. A dynamic ML model reliably identified CAT+ trauma patients with data available within minutes of trauma center arrival, and the quality of the prediction improved as more patient-level data became available. Such an approach can optimize the accuracy and timeliness of massive transfusion protocol activation

Annotation of the Corymbia terpene synthase gene family shows broad conservation but dynamic evolution of physical clusters relative to Eucalyptus

Terpenes are economically and ecologically important phytochemicals. Their synthesis is controlled by the terpene synthase (TPS) gene family, which is highly diversified throughout the plant kingdom. The plant family Myrtaceae are characterised by especially high terpene concentrations, and considerable variation in terpene profiles. Many Myrtaceae are grown commercially for terpene products including the eucalypts Corymbia and Eucalyptus. Eucalyptus grandis has the largest TPS gene family of plants currently sequenced, which is largely conserved in the closely related E. globulus. However, the TPS gene family has been well studied only in these two eucalypt species. The recent assembly of two Corymbia citriodora subsp. variegata genomes presents an opportunity to examine the conservation of this important gene family across more divergent eucalypt lineages. Manual annotation of the TPS gene family in C. citriodora subsp. variegata revealed a similar overall number, and relative subfamily representation, to that previously reported in E. grandis and E. globulus. Many of the TPS genes were in physical clusters that varied considerably between Eucalyptus and Corymbia, with several instances of translocation, expansion/contraction and loss. Notably, there was greater conservation in the subfamilies involved in primary metabolism than those involved in secondary metabolism, likely reflecting different selective constraints. The variation in cluster size within subfamilies and the broad conservation between the eucalypts in the face of this variation are discussed, highlighting the potential contribution of selection, concerted evolution and stochastic processes. These findings provide the foundation to better understand terpene evolution within the ecologically and economically important Myrtaceae