Automated continuous vital signs predict use of uncrossed matched blood and massive transfusion following trauma. Issue 6 (June 2016)

• Record Type: Journal Article
• Authors: Parimi, Nehu; Hu, Peter F.; Mackenzie, Colin F.; Yang, Shiming; Bartlett, Stephen T.; Scalea, Thomas M.; Stein, Deborah M.
• Is Part Of: Journal of trauma and acute care surgery. Volume 80:Issue 6(2016:Jun.)
• Publication Date: 2016-06
• Subjects: Massive transfusion -- transfusion prediction -- Noninvasive monitoring -- vital signs -- trauma; Surgical intensive care -- Periodicals; Surgical emergencies -- Periodicals; Wounds and injuries -- Surgery -- Periodicals; 617.026
• DOI: 10.1097/TA.0000000000001047
• Languages: English
• ISSNs: 2163-0755

Abstract

Abstract : BACKGROUND: Recognizing the use of uncross-matched packed red blood cells (UnXRBCs) or predicting the 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 I trauma center during a 4-year period (2009–2012): use of UnXRBC, use of greater than 4 U of packed red blood cells within 4 hours (MT1), and use of equal to or greater than 10 U of packed red blood cells within 24 hours (MT2). Vital sign (VS) features including heart rate, systolic blood pressure, and shock index (heart rate / systolic blood pressure) 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, and Models 3, 4, and 5 used continuous VS features after admission over 5, 10, and 15 minutes, respectively, to predict the use of UnXRBC, MT1, and MT2. Models were evaluated for their predictive performance via area under the receiver operating characteristic (ROC) curve, positive predictive value, and negative predictive value. RESULTS: Ten thousand six hundred thirty-six patients with more than 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 with these two models, predictive ability was significantly improved as duration of VS monitoring increased. Continuous VS for 5 minutes had ROCs of 0.83 (confidence interval [CI], 0.83–0.84), 0.85 (CI, 0.84–0.86), and 0.86 (CI, 0.85–0.88) to predict UnXRBC, MT1, and MT2, respectively. Similarly, continuous VS for 10 minutes had a ROCs 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 the highest ROCs 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 with single prehospital and admission VS identify the use of emergency blood use and can direct earlier blood product administration, potentially saving lives. LEVEL OF EVIDENCE: Therapeutic study, level III. Abstract : Supplemental digital content is available in the text.