Table 1.
Characteristics of patients receiving MT.
Table 2.
Characteristics of patients receiving UMT.
Table 3.
Comparison of final models to L1 regularization regression models.
Fig 1.
Receiver-operator curves for prediction of 6-hour mortality in each model.
AUROC: area under the receiver-operator curve.
Table 4.
Secondary metrics for each model developed and tested.
Fig 2.
Reliability diagrams to assess calibration of each model.
Fig 3.
Decision Curve Analysis to assess net benefit of each model.
Fig 4.
Shapley additive explanation (SHAP) methods to assess feature importance for both massive transfusion models.
Left side: Beeswarm plot-each point represents an individual patient, the color of each point represents the value of that variable for that patient, and the horizontal displacement represents the effect of that value of that variable on the model’s outcome prediction for that individual patient. Right side: Top 20 most important features in relation to the model’s decision making, ranked by mean absolute SHAP value. ED: emergency department; SPB: systolic blood pressure; GCS: Glasgow Coma Scale; BMI: body mass index; Avg: average; EMS: emergency medical services.
Fig 5.
Shapley additive explanation (SHAP) methods to assess feature importance for all ultramassive transfusion models.
Left side: Beeswarm plot-each point represents an individual patient, the color of each point represents the value of that variable for that patient, and the horizontal displacement represents the effect of that value of that variable on the model’s outcome prediction for that individual patient. Right side: Top 20 most important features in relation to the model’s decision making, ranked by mean absolute SHAP value. ED: emergency department; GCS: Glasgow Coma Scale; SPB: systolic blood pressure; BMI: body mass index; Avg: average; EMS: emergency medical services.