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

Journal Article (Journal Article)

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.

Full Text

Duke Authors

Cited Authors

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

Published Date

  • November 2021

Published In

Volume / Issue

  • 170 / 5

Start / End Page

  • 1574 - 1580

PubMed ID

  • 34112517

Electronic International Standard Serial Number (EISSN)

  • 1532-7361

Digital Object Identifier (DOI)

  • 10.1016/j.surg.2021.04.034

Language

  • eng

Conference Location

  • United States