Automated prediction of early blood transfusion and mortality in trauma patients

Colin F. Mackenzie, Yulei Wang, Peter F. Hu, Shih Yu Chen, Hegang H. Chen, George Hagegeorge, Lynn G. Stansbury, Stacy Shackelford, Amechi Anazodo, Steven Barker, John Blenko, Chein I. Chang, Theresa Dinardo, Joseph DuBose, Raymond Fang, Yvette Fouche, Linda Goetz, Tom Grissom, Victor Giustina, Anthony HerreraJohn Hess, Cris Imle, Matthew Lissauer, Jay Menaker, Karen Murdock, Mayur Narayan, Tim Oates, Sarah Saccicchio, Thomas Scalea, Robert Sikorski, Lynn Smith, Deborah Stein, Chris Stephens

Research output: Contribution to journalArticle

26 Scopus citations

Abstract

Background: Prediction of blood transfusion needs and mortality for trauma patients in near real time is an unrealized goal. We hypothesized that analysis of pulse oximeter signals could predict blood transfusion and mortality as accurately as conventional vital signs (VSs). Methods: Continuous VS data were recorded for direct admission trauma patients with abnormal prehospital shock index (SI = heart rate [HR] / systolic blood pressure) greater than 0.62. Predictions of transfusion during the first 24 hours and in-hospital mortality using logistical regression models were compared with DeLong's method for areas under receiver operating characteristic curves (AUROCs) to determine the optimal combinations of prehospital SI and HR, continuous photoplethysmographic (PPG), oxygen saturation (SpO2), and HR-related features. Results: We enrolled 556 patients; 37 received blood within 24 hours; 7 received more than 4 U of red blood cells in less than 4 hours or "massive transfusion" (MT); and 9 died. The first 15 minutes of VS signals, including prehospital HR plus continuous PPG, and SpO2 HR signal analysis best predicted transfusion at 1 hour to 3 hours, MT, and mortality (AUROC, 0.83; p < 0.03) and no differently (p = 0.32) from a model including blood pressure. Predictions of transfusion based on the first 15 minutes of data were no different using 30 minutes to 60 minutes of data collection. SI plus PPG and SpO2 signal analysis (AUROC, 0.82) predicted 1-hour to 3-hour transfusion, MT, and mortality no differently from pulse oximeter signals alone. Conclusion: Pulse oximeter features collected in the first 15 minutes of our trauma patient resuscitation cohort, without user input, predicted early MT and mortality in the critical first hours of care better than the currently used VS such as combinations of HR and systolic blood pressure or prehospital SI alone. Level of Evidence: Therapeutic/prognostic study, level II.

Original languageEnglish (US)
Pages (from-to)1379-1385
Number of pages7
JournalJournal of Trauma and Acute Care Surgery
Volume76
Issue number6
DOIs
StatePublished - Jun 2014

Keywords

  • Automated decision assist
  • blood transfusion
  • mortality
  • photopletysmograph

ASJC Scopus subject areas

  • Surgery
  • Critical Care and Intensive Care Medicine

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    Mackenzie, C. F., Wang, Y., Hu, P. F., Chen, S. Y., Chen, H. H., Hagegeorge, G., Stansbury, L. G., Shackelford, S., Anazodo, A., Barker, S., Blenko, J., Chang, C. I., Dinardo, T., DuBose, J., Fang, R., Fouche, Y., Goetz, L., Grissom, T., Giustina, V., ... Stephens, C. (2014). Automated prediction of early blood transfusion and mortality in trauma patients. Journal of Trauma and Acute Care Surgery, 76(6), 1379-1385. https://doi.org/10.1097/TA.0000000000000235