Clinical gestalt and the prediction of massive transfusion after trauma

MPH on behalf of the PROMMTT Study Group

    Research output: Contribution to journalArticle

    26 Citations (Scopus)

    Abstract

    Introduction Early recognition and treatment of trauma patients requiring massive transfusion (MT) has been shown to reduce mortality. While many risk factors predicting MT have been demonstrated, there is no universally accepted method or algorithm to identify these patients. We hypothesised that even among experienced trauma surgeons, the clinical gestalt of identifying patients who will require MT is unreliable. Methods Transfusion and mortality outcomes after trauma were observed at 10 U.S. Level-1 trauma centres in patients who survived ≥30 min after admission and received ≥1 unit of RBC within 6 h of arrival. Subjects who received ≥10 units within 24 h of admission were classified as MT patients. Trauma surgeons were asked the clinical gestalt question "Is the patient likely to be massively transfused?" 10 min after the patients arrival. The performance of clinical gestalt to predict MT was assessed using chi-square tests and ROC analysis to compare gestalt to previously described scoring systems. Results Of the 1245 patients enrolled, 966 met inclusion criteria and 221 (23%) patients received MT. 415 (43%) were predicted to have a MT and 551(57%) were predicted to not have MT. Patients predicted to have MT were younger, more often sustained penetrating trauma, had higher ISS scores, higher heart rates, and lower systolic blood pressures (all p <0.05). Gestalt sensitivity was 65.6% and specificity was 63.8%. PPV and NPV were 34.9% and 86.2% respectively. Conclusion Data from this large multicenter trial demonstrates that predicting the need for MT continues to be a challenge. Because of the increased mortality associated with delayed therapy, a more reliable algorithm is needed to identify and treat these severely injured patients earlier.

    Original languageEnglish (US)
    Pages (from-to)807-813
    Number of pages7
    JournalInjury
    Volume46
    Issue number5
    DOIs
    StatePublished - 2015

    Fingerprint

    Wounds and Injuries
    Mortality
    Blood Pressure
    Injury Severity Score
    Trauma Centers
    Chi-Square Distribution
    ROC Curve
    Multicenter Studies
    Heart Rate
    Therapeutics

    Keywords

    • Gestalt
    • Massive transfusion
    • Trauma

    ASJC Scopus subject areas

    • Emergency Medicine
    • Orthopedics and Sports Medicine

    Cite this

    Clinical gestalt and the prediction of massive transfusion after trauma. / MPH on behalf of the PROMMTT Study Group.

    In: Injury, Vol. 46, No. 5, 2015, p. 807-813.

    Research output: Contribution to journalArticle

    MPH on behalf of the PROMMTT Study Group 2015, 'Clinical gestalt and the prediction of massive transfusion after trauma', Injury, vol. 46, no. 5, pp. 807-813. https://doi.org/10.1016/j.injury.2014.12.026
    MPH on behalf of the PROMMTT Study Group. / Clinical gestalt and the prediction of massive transfusion after trauma. In: Injury. 2015 ; Vol. 46, No. 5. pp. 807-813.
    @article{14e0f72ce5204877acd3d8208b503625,
    title = "Clinical gestalt and the prediction of massive transfusion after trauma",
    abstract = "Introduction Early recognition and treatment of trauma patients requiring massive transfusion (MT) has been shown to reduce mortality. While many risk factors predicting MT have been demonstrated, there is no universally accepted method or algorithm to identify these patients. We hypothesised that even among experienced trauma surgeons, the clinical gestalt of identifying patients who will require MT is unreliable. Methods Transfusion and mortality outcomes after trauma were observed at 10 U.S. Level-1 trauma centres in patients who survived ≥30 min after admission and received ≥1 unit of RBC within 6 h of arrival. Subjects who received ≥10 units within 24 h of admission were classified as MT patients. Trauma surgeons were asked the clinical gestalt question {"}Is the patient likely to be massively transfused?{"} 10 min after the patients arrival. The performance of clinical gestalt to predict MT was assessed using chi-square tests and ROC analysis to compare gestalt to previously described scoring systems. Results Of the 1245 patients enrolled, 966 met inclusion criteria and 221 (23{\%}) patients received MT. 415 (43{\%}) were predicted to have a MT and 551(57{\%}) were predicted to not have MT. Patients predicted to have MT were younger, more often sustained penetrating trauma, had higher ISS scores, higher heart rates, and lower systolic blood pressures (all p <0.05). Gestalt sensitivity was 65.6{\%} and specificity was 63.8{\%}. PPV and NPV were 34.9{\%} and 86.2{\%} respectively. Conclusion Data from this large multicenter trial demonstrates that predicting the need for MT continues to be a challenge. Because of the increased mortality associated with delayed therapy, a more reliable algorithm is needed to identify and treat these severely injured patients earlier.",
    keywords = "Gestalt, Massive transfusion, Trauma",
    author = "{MPH on behalf of the PROMMTT Study Group} and Pommerening, {Matthew J.} and Goodman, {Michael D.} and Holcomb, {John B.} and Wade, {Charles E.} and Fox, {Erin E.} and {Del Junco}, {Deborah J.} and Karen Brasel and Bulger, {Eileen M.} and Cohen, {Mitch J.} and Alarcon, {Louis H.} and Martin Schreiber and Myers, {John G.} and Phelan, {Herb A.} and Peter Muskat and Mohammad Rahbar and Cotton, {Bryan A.}",
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    T1 - Clinical gestalt and the prediction of massive transfusion after trauma

    AU - MPH on behalf of the PROMMTT Study Group

    AU - Pommerening, Matthew J.

    AU - Goodman, Michael D.

    AU - Holcomb, John B.

    AU - Wade, Charles E.

    AU - Fox, Erin E.

    AU - Del Junco, Deborah J.

    AU - Brasel, Karen

    AU - Bulger, Eileen M.

    AU - Cohen, Mitch J.

    AU - Alarcon, Louis H.

    AU - Schreiber, Martin

    AU - Myers, John G.

    AU - Phelan, Herb A.

    AU - Muskat, Peter

    AU - Rahbar, Mohammad

    AU - Cotton, Bryan A.

    PY - 2015

    Y1 - 2015

    N2 - Introduction Early recognition and treatment of trauma patients requiring massive transfusion (MT) has been shown to reduce mortality. While many risk factors predicting MT have been demonstrated, there is no universally accepted method or algorithm to identify these patients. We hypothesised that even among experienced trauma surgeons, the clinical gestalt of identifying patients who will require MT is unreliable. Methods Transfusion and mortality outcomes after trauma were observed at 10 U.S. Level-1 trauma centres in patients who survived ≥30 min after admission and received ≥1 unit of RBC within 6 h of arrival. Subjects who received ≥10 units within 24 h of admission were classified as MT patients. Trauma surgeons were asked the clinical gestalt question "Is the patient likely to be massively transfused?" 10 min after the patients arrival. The performance of clinical gestalt to predict MT was assessed using chi-square tests and ROC analysis to compare gestalt to previously described scoring systems. Results Of the 1245 patients enrolled, 966 met inclusion criteria and 221 (23%) patients received MT. 415 (43%) were predicted to have a MT and 551(57%) were predicted to not have MT. Patients predicted to have MT were younger, more often sustained penetrating trauma, had higher ISS scores, higher heart rates, and lower systolic blood pressures (all p <0.05). Gestalt sensitivity was 65.6% and specificity was 63.8%. PPV and NPV were 34.9% and 86.2% respectively. Conclusion Data from this large multicenter trial demonstrates that predicting the need for MT continues to be a challenge. Because of the increased mortality associated with delayed therapy, a more reliable algorithm is needed to identify and treat these severely injured patients earlier.

    AB - Introduction Early recognition and treatment of trauma patients requiring massive transfusion (MT) has been shown to reduce mortality. While many risk factors predicting MT have been demonstrated, there is no universally accepted method or algorithm to identify these patients. We hypothesised that even among experienced trauma surgeons, the clinical gestalt of identifying patients who will require MT is unreliable. Methods Transfusion and mortality outcomes after trauma were observed at 10 U.S. Level-1 trauma centres in patients who survived ≥30 min after admission and received ≥1 unit of RBC within 6 h of arrival. Subjects who received ≥10 units within 24 h of admission were classified as MT patients. Trauma surgeons were asked the clinical gestalt question "Is the patient likely to be massively transfused?" 10 min after the patients arrival. The performance of clinical gestalt to predict MT was assessed using chi-square tests and ROC analysis to compare gestalt to previously described scoring systems. Results Of the 1245 patients enrolled, 966 met inclusion criteria and 221 (23%) patients received MT. 415 (43%) were predicted to have a MT and 551(57%) were predicted to not have MT. Patients predicted to have MT were younger, more often sustained penetrating trauma, had higher ISS scores, higher heart rates, and lower systolic blood pressures (all p <0.05). Gestalt sensitivity was 65.6% and specificity was 63.8%. PPV and NPV were 34.9% and 86.2% respectively. Conclusion Data from this large multicenter trial demonstrates that predicting the need for MT continues to be a challenge. Because of the increased mortality associated with delayed therapy, a more reliable algorithm is needed to identify and treat these severely injured patients earlier.

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    KW - Massive transfusion

    KW - Trauma

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