Predicting the need for massive transfusion: Prospective validation of a smartphone-based clinical decision support tool

Academic Article

Abstract

  • Background: Improper or delayed activation of a massive transfusion protocol may have consequences to individuals and institutions. We designed a complex predictive algorithm that was packaged within a smartphone application. We hypothesized it would accurately assess the need for massive transfusion protocol activation and assist clinicians in that decision. Methods: We prospectively enrolled patients at an urban, level I trauma center. The application recorded the surgeon's initial opinion for activation and then prompted inputs for the model. The application provided a prediction and recorded the surgeon's final decision on activation. Results: Three hundred and twenty-one patients were enrolled (83% male; 59% penetrating; median Injury Severity Score 9; mean base deficit –4.11). Of 36 massive transfusion protocol activations, 26 had an app prediction of “high” or “moderate” probability. Of these, 4 (15%) patients received <10 u blood as a result of early hemorrhage control. Two hundred and eighty-five patients did not have massive transfusion protocol activated by the surgeon with 27 (9%) patients having “moderate” or “high” likelihood predicted by the application. Twenty-four of these did not require massive transfusion, and all patients had acidosis that unrelated to hemorrhagic shock. For 13 (50%) of the patients with “high” probability, the surgeon correctly altered their initial decision based on this information. The algorithm demonstrated an adjusted accuracy of 0.96 (95% confidence interval [0.93–0.98); P ≤ .001]), sensitivity = 0.99, specificity 0.72, positive predictive value 0.96, negative predictive value 0.99, and area under the receiver operating curve = 0.86. Conclusion: A smartphone-based clinical decision tools can aid surgeons in the decision to active massive transfusion protocol in real time, although it does not completely replace clinician judgment.
  • Authors

    Published In

  • Surgery  Journal
  • Digital Object Identifier (doi)

    Pubmed Id

  • 5687972
  • Author List

  • Dente CJ; Mina MJ; Morse BC; Hensman H; Schobel S; Gelbard RB; Belard A; Buchman TG; Kirk AD; Elster EA
  • Start Page

  • 1574
  • End Page

  • 1580
  • Volume

  • 170
  • Issue

  • 5