NDLI: Comparison of Decision-Assist and Clinical Judgment of Experts for Prediction of Lifesaving Interventions.

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Comparison of Decision-Assist and Clinical Judgment of Experts for Prediction of Lifesaving Interventions.

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Comparison of Decision-Assist and Clinical Judgment of Experts for Prediction of Lifesaving Interventions.

Content Provider	World Health Organization (WHO)-Global Index Medicus
Author	Mackenzie, Colin F. Gao, Cheng Hu, Peter F. Anazodo, Amechi Chen, Hegang Dinardo, Theresa Imle, P. Cristina Hartsky, Lauren Stephens, Christopher Menaker, Jay Fouche, Yvette Murdock, Karen Galvagno, Samuel Alcorta, Richard Shackelford, Stacy
Description	Author Affiliation: Mackenzie CF ( *Department of Anesthesiology, Shock Trauma Anesthesiology Research Center and Charles McMathias National Study Center for Trauma and EMS, and Department of Physiology, University of Maryland School of Medicine)
Abstract	Early recognition of hemorrhage during the initial resuscitation of injured patients is associated with improved survival in both civilian and military casualties. We tested a transfusion and lifesaving intervention (LSI) prediction algorithm in comparison with clinical judgment of expert trauma care providers. We collected 15 min of pulse oximeter photopletysmograph waveforms and extracted features to predict LSIs. We compared this with clinical judgment of LSIs by individual categories of prehospital providers, nurses, and physicians and a combined judgment of all three providers using the Area Under Receiver Operating Curve (AUROC). We obtained clinical judgment of need for LSI from 405 expert clinicians in135 trauma patients. The pulse oximeter algorithm predicted transfusion within 6 h (AUROC, 0.92; P < 0.003) more accurately than either physicians or prehospital providers and as accurately as nurses (AUROC, 0.76; P = 0.07). For prediction of surgical procedures, the algorithm was as accurate as the three categories of clinicians. For prediction of fluid bolus, the diagnostic algorithm (AUROC, 0.9) was significantly more accurate than prehospital providers (AUROC, 0.62; P = 0.02) and nurses (AUROC, 0.57; P = 0.04) and as accurate as physicians (AUROC, 0.71; P = 0.06). Prediction of intubation by the algorithm (AUROC, 0.92) was as accurate as each of the three categories of clinicians. The algorithm was more accurate (P < 0.03) for blood and fluid prediction than the combined clinical judgment of all three providers but no different from the clinicians in the prediction of surgery (P = 0.7) or intubation (P = 0.8). Automated analysis of 15 min of pulse oximeter waveforms predicts the need for LSIs during initial trauma resuscitation as accurately as judgment of expert trauma clinicians. For prediction of emergency transfusion and fluid bolus, pulse oximetry features were more accurate than these experts. Such automated decision support could assist resuscitation decisions, trauma team, and operating room and blood bank preparations.
File Format	HTM / HTML
ISSN	10732322
Issue Number	3
Volume Number	43
e-ISSN	15400514
Journal	Shock
Language	English
Publisher	Lippincott Williams & Wilkins
Publisher Date	2015-03-01
Publisher Place	United States
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Discipline Intensive care medicine Decision Making, Computer-assisted Expert Testimony Hemorrhage Diagnosis Resuscitation Adult Algorithms Area Under Curve Blood Transfusion Female Therapy Humans Judgment Male Middle Aged Oximetry Wounds And Injuries Young Adult Comparative Study Journal Article Research Support, U.s. Gov't, Non-p.h.s. Validation Studies
Content Type	Text
Resource Type	Article
Subject	Emergency Medicine Critical Care and Intensive Care Medicine

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