Automated analysis of vital signs to identify @entity1 with XXXX before hospital arrival: a feasibility study.

@entity1 @entity190 @entity130 @entity548
@entity130 outcomes are improved by protocols for @entity548 , typically activated after physician evaluation at a hospital. Previous analysis suggested that prehospital vital signs contained patterns indicating the presence or absence of @entity548 . In an observational study of adults (aged >= 18 years) transported to level I @entity130 centers by helicopter, we investigated the diagnostic performance of the Automated Processing of the Physiological Registry for Assessment of Injury Severity (APPRAISE) system, a computational platform for real-time analysis of vital signs, for identification of @entity548 in @entity130 @entity1 with explicitly @entity548 . We studied 209 subjects prospectively and 646 retrospectively. In our multivariate analysis, prospective performance was not significantly different from retrospective. The APPRAISE system was 76% sensitive for 24-h packed red blood cells of 9 or more units (95% confidence interval, 59% - 89%) and significantly more sensitive (P < 0.05) than any prehospital Shock Index of 1.4 or higher; sensitivity, 59%; initial systolic blood pressure (SBP) less than 110 mmHg, 50%; and any prehospital SBP less than 90 mmHg, 50%. The APPRAISE specificity for 24-h packed red blood cells of 0 units was 87% (88% for any Shock Index >= 1.4, 88% for initial SBP <110 mmHg, and 90% for any prehospital SBP <90 mmHg). Median APPRAISE hemorrhage notification time was 20 min before arrival at the @entity130 center. In conclusion, APPRAISE identified @entity548 before @entity130 center arrival. En route, this capability could allow medics to focus on direct @entity1 care rather than the monitor and, via advance radio notification, could expedite hospital interventions for @entity1 with substantial @entity190 .
