External validation of a smartphone app model to predict the need for massive transfusion using five different definitions

E. I. Hodgman, M. W. Cripps, M. J. Mina, E. M. Bulger, Martin Schreiber, Karen Brasel, M. J. Cohen, P. Muskat, J. G. Myers, L. H. Alarcon, M. H. Rahbar, J. B. Holcomb, B. A. Cotton, E. E. Fox, D. J. Del Junco, C. E. Wade, H. A. Phelan

    Research output: Contribution to journalArticle

    1 Citation (Scopus)

    Abstract

    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.

    Original languageEnglish (US)
    Pages (from-to)397-402
    Number of pages6
    JournalJournal of Trauma and Acute Care Surgery
    Volume84
    Issue number2
    DOIs
    StatePublished - Feb 1 2018

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    Erythrocytes
    Area Under Curve
    Routine Diagnostic Tests
    Resuscitation
    Databases
    Wounds and Injuries
    Population
    Smartphone
    Datasets
    Machine Learning

    Keywords

    • Massive transfusion
    • prediction model
    • smartphone application
    • trauma

    ASJC Scopus subject areas

    • Surgery
    • Critical Care and Intensive Care Medicine

    Cite this

    External validation of a smartphone app model to predict the need for massive transfusion using five different definitions. / Hodgman, E. I.; Cripps, M. W.; Mina, M. J.; Bulger, E. M.; Schreiber, Martin; Brasel, Karen; Cohen, M. J.; Muskat, P.; Myers, J. G.; Alarcon, L. H.; Rahbar, M. H.; Holcomb, J. B.; Cotton, B. A.; Fox, E. E.; Del Junco, D. J.; Wade, C. E.; Phelan, H. A.

    In: Journal of Trauma and Acute Care Surgery, Vol. 84, No. 2, 01.02.2018, p. 397-402.

    Research output: Contribution to journalArticle

    Hodgman, EI, Cripps, MW, Mina, MJ, Bulger, EM, Schreiber, M, Brasel, K, Cohen, MJ, Muskat, P, Myers, JG, Alarcon, LH, Rahbar, MH, Holcomb, JB, Cotton, BA, Fox, EE, Del Junco, DJ, Wade, CE & Phelan, HA 2018, 'External validation of a smartphone app model to predict the need for massive transfusion using five different definitions', Journal of Trauma and Acute Care Surgery, vol. 84, no. 2, pp. 397-402. https://doi.org/10.1097/TA.0000000000001756
    Hodgman, E. I. ; Cripps, M. W. ; Mina, M. J. ; Bulger, E. M. ; Schreiber, Martin ; Brasel, Karen ; Cohen, M. J. ; Muskat, P. ; Myers, J. G. ; Alarcon, L. H. ; Rahbar, M. H. ; Holcomb, J. B. ; Cotton, B. A. ; Fox, E. E. ; Del Junco, D. J. ; Wade, C. E. ; Phelan, H. A. / External validation of a smartphone app model to predict the need for massive transfusion using five different definitions. In: Journal of Trauma and Acute Care Surgery. 2018 ; Vol. 84, No. 2. pp. 397-402.
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    abstract = "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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    T1 - External validation of a smartphone app model to predict the need for massive transfusion using five different definitions

    AU - Hodgman, E. I.

    AU - Cripps, M. W.

    AU - Mina, M. J.

    AU - Bulger, E. M.

    AU - Schreiber, Martin

    AU - Brasel, Karen

    AU - Cohen, M. J.

    AU - Muskat, P.

    AU - Myers, J. G.

    AU - Alarcon, L. H.

    AU - Rahbar, M. H.

    AU - Holcomb, J. B.

    AU - Cotton, B. A.

    AU - Fox, E. E.

    AU - Del Junco, D. J.

    AU - Wade, C. E.

    AU - Phelan, H. A.

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    N2 - 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.

    AB - 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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