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External validation of a smartphone app model to predict the need for massive transfusion using five different
E I Hodgman1, M W Cripps, M J Mina
1From the Division of Burns, Trauma and Critical Care, Department of Surgery (E.H., M.C., H.P.), University of Texas at Southwestern Medical Center, Dallas, Texas; Department of Clinical Pathology (M.M.), Harvard Medical School, Boston, Massachusetts; Division of Trauma and Critical Care, Department of Surgery (E.M.), School of Medicine, University of Washington, Seattle, Washington; Division of Trauma, Critical Care, and Acute Care Surgery (M.S.), School of Medicine, Oregon Health & Science University, Portland, Oregon; Division of Trauma and Critical Care, Department of Surgery (K.B.), Medical College of Wisconsin, Milwaukee, Wisconsin; Division of General Surgery, Department of Surgery (M.J., P.M.), School of Medicine, University of California San Francisco, San Francisco, California; Division of Trauma, Department of Surgery (J.M.), School of Medicine, University of Texas Health Science Center at San Antonio, San Antonio, Texas; Division of Trauma and General Surgery, Department of Surgery (L.A.), School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania; Biostatistics/Epidemiology/Research Design Core (M.R., E.F., D.d.J.), Center for Clinical and Translational Sciences, Division of Epidemiology, Human Genetics, and Environmental Sciences (M.R.), School of Public Health, and Center for Translational Injury Research, Division of Acute Care Surgery, Department of Surgery (J.H., B.C., E.F., D.d.J., C.W.), Medical School, University of Texas Health Science Center at Houston, Houston, Texas.
Background:
Previously, a model to predict massive transfusion protocol (MTP) (activation) was derived using a single-institution data set. The PRospective, Observational, Multicenter, Major Trauma Transfusion database was used to externally validate this model's ability to predict both MTP activation and massive transfusion (MT) administration using multiple MT definitions.
Methods:
The app model was used to calculate the predicted probability of MTP activation or MT delivery. The five definitions of MT used were: (1) 10 units packed red blood cells (PRBCs) in 24 hours, (2) Resuscitation Intensity score ≥ 4, (3) critical administration threshold, (4) 4 units PRBCs in 4 hours; and (5) 6 units PRBCs in 6 hours. Receiver operating curves were plotted to compare the predicted probability of MT with observed outcomes.
Results:
Of 1,245 patients in the data set, 297 (24%) met definition 1, 570 (47%) met definition 2, 364 (33%) met definition 3, 599 met definition 4 (49.1%), and 395 met definition 5 (32.4%). Regardless of the outcome (MTP activation or MT administration), the predictive ability of the app model was consistent: when predicting activation of the MTP, the area under the curve for the model was 0.694 and when predicting MT administration, the area under the curve ranged from 0.695 to 0.711.
Conclusion:
Regardless of the definition of MT used, the app model demonstrates moderate ability to predict the need for MT in an external, homogenous population. Importantly, the app allows the model to be iteratively recalibrated ("machine learning") and thus could improve its predictive capability as additional data are accrued.
Level Of Evidence:
Diagnostic test study/Prognostic study, level III.
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