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. 2013 Oct;75(4):669-75.
doi: 10.1097/TA.0b013e3182a12ba6.

Let Technology Do the Work: Improving Prediction of Massive Transfusion With the Aid of a Smartphone Application

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Free PMC article

Let Technology Do the Work: Improving Prediction of Massive Transfusion With the Aid of a Smartphone Application

Michael Joseph Mina et al. J Trauma Acute Care Surg. .
Free PMC article

Abstract

Background: The use of massive transfusion protocols (MTPs) is now common in civilian trauma settings, and early activation of MTP has been shown to increase survival of MTP recipients. Numerous MTP prediction tools have been developed; however, they are often cumbersome to use efficiently or have traded predictive power for ease of use. We hypothesized that a highly accurate predictor of massive transfusion could be created and incorporated into a smartphone application that would provide an additional tool for clinicians to use in directing the resuscitation of critically injured patients.

Methods: Data from all trauma admissions since the inception of MTP were put in place at Grady Memorial Hospital in Atlanta, Georgia, were collected. A predictive model was developed using the least absolute shrinkage and selection operator (LASSO) and 10-fold cross validation. Data were resampled over 500 iterations, each using a unique and random subset of 80% of the data for model training and 20% for validation.

Results: The trauma registry contained 13,961 cases between 2007 and November 2011, of which 10,900 were complete and 394 received MTP. Of 44 input terms, only the mechanism of injury, heart rate, systolic blood pressure, and base deficit were found to be important predictors of massive transfusion. Our model has an area under the receiver operating curve of 0.96 (against data not used during model training) and accurately predicted MTP status for 97% of all patients. The model accurately discriminated full MTPs from MTP activations that did not meet criteria for massive transfusion. While complex to calculate by hand, our model has been packaged into a mobile application, allowing for efficient use while minimizing potential for user error.

Conclusion: We have developed a highly accurate model for the prediction of massive transfusion that has potential to be easily accessed and used within a simple and efficient mobile application for smartphones.

Level of evidence: Prognostic/epidemiologic study, level III.

Figures

Figure 1
Figure 1
Flow diagram of trauma registry data used in this study.
Figure 2
Figure 2
Model validation and predictive performance. Model sensitivity and specificity (A) were measured for each of 500 development runs and mean area under the curve of the 500 runs was calculated. For each run, 20% of the data was randomly selected and withheld from model training. Model sensitivity and specificity were measured against this 20% for each run. For ease of visualization, 10% of the 500 runs were randomly chosen and plotted in A. Proportions of non-MTP trauma visits (B), MTP activated trauma visits (C), and EBCs (D) are plotted by their predicted MTP likelihood category. Error bars represent 95% confidence intervals using Wilson’s score method with continuity correction.
Figure 3
Figure 3
Mobile application for MTP prediction: Our model has been packaged into a mobile application that can be installed on modern smartphones. The application requires only that the user input only the method of injury, BD, HR, and SBP and outputs a probability of MTP on both a continuous scale (%) and a categorical scale (high, moderate, low, very low).

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