Predicting blood transfusion using automated analysis of pulse oximetry signals and laboratory values

Stacy Shackelford, Shiming Yang, Peter Hu, Catriona Miller, Amechi Anazodo, Samuel Galvagno, Yulei Wang, Lauren Hartsky, Raymond Fang, Colin Mackenzie, Steven Barker, John Blenko, Chein I. Chang, Hegang Chen, Theresa Dinardo, Joseph DuBose, Yvette Fouche, Linda Goetz, Thomas Grissom, Victor GiustinaGeorge Hagegeorge, Anthony Herrera, John Hess, Cris Imle, Jay Menaker, Karen Murdock, Mayur Narayan, Tim Oates, Jason Pasley, Sarah Saccicchio, Thomas Scalea

Research output: Contribution to journalArticle

10 Scopus citations

Abstract

BACKGROUND: Identification of hemorrhaging trauma patients and prediction of blood transfusion needs in near real time will expedite care of the critically injured. We hypothesized that automated analysis of pulse oximetry signals in combination with laboratory values and vital signs obtained at the time of triage would predict the need for blood transfusion with accuracy greater than that of triage vital signs or pulse oximetry analysis alone. METHODS: Continuous pulse oximetry signals were recorded for directly admitted trauma patients with abnormal prehospital shock index (heart rate [HR]/systolic blood pressure) of 0.62 or greater. Predictions of blood transfusion within 24 hours were compared using Delong's method for area under the receiver operating characteristic (AUROC) curves to determine the optimal combination of triage vital signs (prehospital HR + systolic blood pressure), pulse oximetry features (40 waveform features, O2saturation, HR), and laboratory values (hematocrit, electrolytes, bicarbonate, prothrombin time, international normalization ratio, lactate) in multivariate logistic regression models. RESULTS: We enrolled 1,191 patients; 339 were excluded because of incomplete data; 40 received blood within 3 hours; and 14 received massive transfusion. Triage vital signs predicted need for transfusion within 3 hours (AUROC, 0.59) and massive transfusion (AUROC, 0.70). Pulse oximetry for 15 minutes predicted transfusion more accurately than triage vital signs for both time frames (3-hour AUROC, 0.74; p = 0.004) (massive transfusion AUROC, 0.88;p < 0.001). An algorithm including triage vital signs, pulse oximetry features, and laboratory values improved accuracy of transfusion prediction (3-hour AUROC, 0.84; p < 0.001) (massive transfusion AUROC, 0.91; p < 0.001). CONCLUSION: Automated analysis of triage vital signs, 15 minutes of pulse oximetry signals, and laboratory values predicted use of blood transfusion during trauma resuscitation more accurately than triage vital signs or pulse oximetry analysis alone. Results suggest automated calculations from a noninvasive vital sign monitor interfaced with a point-of-care laboratory device may support clinical decisions by recognizing patients with hemorrhage sufficient to need transfusion. LEVEL OF EVIDENCE: Epidemiologic/prognostic study, level III.

Original languageEnglish (US)
Pages (from-to)S175-S180
JournalJournal of Trauma and Acute Care Surgery
Volume79
Issue number4
DOIs
Publication statusPublished - Jan 1 2015

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Keywords

  • Blood transfusion
  • Massive transfusion
  • Point-of-care laboratory testing
  • Prediction
  • Pulse oximetry

ASJC Scopus subject areas

  • Surgery
  • Critical Care and Intensive Care Medicine

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