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

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.

Advanced medical monitoring for the battlefield: A review on clinical applicability of compensatory reserve measurements for early and accurate hemorrhage detection

“…The investigators concluded that the measurement of PPG features predicted the need for early blood transfusion and mortality in the early compensatory phase of care better than currently used combinations of HR, SBP or prehospital SI alone. In a subsequent study, these investigators reported that accuracy of prediction for the need of emergency transfusion with clinical judgment of expert trauma care providers was significantly lower (ROC AUC, 0.76) than the prediction generated from using AWFA obtained from a pulse oximeter (ROC AUC, 0.92) 25 …”

Assessing trauma care provider judgement in the prediction of need for life-saving interventions

“…Mean Injury Severity Scores (ISS) were 13.0 (SD 13.2) [interquartile range, [4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20]. Injuries included 19% penetrating injuries and 78% blunt injuries (Table 1).…”

“…All such providers involved in the care of adult (18 years old) trauma patients eligible for inclusion in two associated studies [15,16] were eligible to participate in this study. Trauma patients were admitted directly from the scene of injury with a pre-hospital abnormal shock index [SI] 0.62 (SI = heart rate/minute [HR]/ systolic blood pressure mmHg [SBP]) called in from the field or who were categorised as Priority 1 (critically ill or injured person requiring immediate attention; or unstable patient with lifethreatening injury or illness) with or without pre-hospital vital signs per initial field triage.…”

A New Time-Window Prediction Model For Traumatic Hemorrhagic Shock Based on Interpretable Machine Learning

“…In terms of model evaluation, the following indicators were used: accuracy, recall, precision, F 1.5 , the area under receiver operating characteristic (AUROC) curves, and precision-recall curves (AUPRC). We compared AUROCs using DeLong's test for the THS prediction models (35)(36)(37), which were performed using R package pROC, version 1.17.0.1. A two-sided 0.05 significance level was applied to general comparisons.…”