Automated continuous vital signs predict use of uncrossed matched blood (UnXRBC) and massive transfusion (MT) following trauma.

Introduction: Recognizing the use of uncross-matched packed red blood cells (UnXRBC) or predicting need for massive transfusion (MT) in injured patients with hemorrhagic shock can be challenging. A validated predictive model could accelerate decision making regarding transfusion. Methods: Three transfusion outcomes were evaluated in adult trauma patients admitted to a level one trauma center over a four-year period (2009-2012): use of UnXRBC, use of >4 units of packed red blood cells (PRBC) within 4 hours (MT1) and use of >=10 units of PRBC within 24 hours (MT2). Vital Signs (VS) features including heart rate (HR), systolic blood pressure (SBP), and shock index (SI=HR/SBP) were calculated for 5, 10 and 15 minutes after admission. Five models were then constructed. Model 1 used preadmission VS, Model 2 used admission VS, Models 3, 4 and 5 used continuous VS features after admission over 5, 10 and 15 minutes, respectively to predict use of UnXRBC, MT1 and MT2. Models were evaluated for their predictive performance via area under the receiver operating characteristic curve (AUROC), positive predictive value (PPV), and negative predictive value (NPV). Results: Ten thousand six hundred and thirty six patients with over 5 million continuous VS data points during the first 15 minutes after admission were analyzed. Model using preadmission and admission VS had similar ability to predict UnXRBC, MT1 or MT2. Compared to these two models, predictive ability was significantly improved as duration of VS monitoring increased. Continuous VS for 5 minute had an ROC of 0.83 with confidence interval (CI) of 0.83-0.84, ROC of 0.85 (CI 0.84-0.86) and ROC of 0.86 (CI 0.85-0.88) to predict UnXRBC, MT1 and MT2, respectively. Similarly, continuous VS for 10 minutes had an ROC of 0.86 (CI 0.85-0.86), 0.87 (CI 0.86-0.88) and 0.88 (CI 0.87-0.90) to predict UnXRBC, MT1 and MT2, respectively. Continuous VS for 15 minutes achieved highest ROC of 0.87 (CI 0.87-0.88), 0.89 (CI 0.88-0.90) and 0.91 (CI 0.91-0.92) to predict UnXRBC, MT1 and MT2, respectively. Conclusion: Models using continuous VS collected after admission improve prediction for the use of UnXRBC or MT in patients with hemorrhagic shock. Decision models derived from automated continuous VS in comparison to single prehospital and admission VS identifies the use of emergency blood use and can direct earlier blood product administration, potentially saving lives. Level of Evidence: Level III (C) 2016 Lippincott Williams & Wilkins, Inc.

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