Comparison of Decision-Assist and Clinical Judgment of Experts for Prediction of Lifesaving Interventions.
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.
*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; §Departments of Electrical Engineering, Computer Science, University of Maryland, Baltimore County; ∥Department of Epidemiology, ¶Shock Trauma Nursing, and **Shock Trauma Center, University of Maryland School of Medicine; ††USAF C-STARS Baltimore, Maryland Institute for Emergency Medical Services Systems; and ‡‡Department of Surgery, University of Maryland School of Medicine, Baltimore, Maryland.
Colin F
CF
Mackenzie
Cheng
C
Gao
Peter F
PF
Hu
Amechi
A
Anazodo
Hegang
H
Chen
Theresa
T
Dinardo
P Cristina
PC
Imle
Lauren
L
Hartsky
Christopher
C
Stephens
Jay
J
Menaker
Yvette
Y
Fouche
Karen
K
Murdock
Samuel
S
Galvagno
Richard
R
Alcorta
Stacy
S
Shackelford
D000328:Adult; D000465:Algorithms; D019540:Area Under Curve; D001803:Blood Transfusion; D003658:Decision Making, Computer-Assisted; D005104:Expert Testimony; D005260:Female; D006470:Hemorrhage / Q000175:diagnosis* / Q000628:therapy; D006801:Humans; D007600:Judgment; D008297:Male; D008875:Middle Aged; D010092:Oximetry; D012151:Resuscitation; D014947:Wounds and Injuries / Q000628:therapy; D055815:Young Adult