Literature DB >> 25664983

Automated analysis of vital signs to identify patients with substantial bleeding before hospital arrival: a feasibility study.

Jianbo Liu1, Maxim Y Khitrov, Jonathan D Gates, Stephen R Odom, Joaquim M Havens, Marc A de Moya, Kevin Wilkins, Suzanne K Wedel, Erin O Kittell, Jaques Reifman, Andrew T Reisner.   

Abstract

Trauma outcomes are improved by protocols for substantial bleeding, typically activated after physician evaluation at a hospital. Previous analysis suggested that prehospital vital signs contained patterns indicating the presence or absence of substantial bleeding. In an observational study of adults (aged ≥18 years) transported to level I trauma 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 substantial bleeding in trauma patients with explicitly hemorrhagic injuries. 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 trauma center. In conclusion, APPRAISE identified bleeding before trauma center arrival. En route, this capability could allow medics to focus on direct patient care rather than the monitor and, via advance radio notification, could expedite hospital interventions for patients with substantial blood loss.

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Year:  2015        PMID: 25664983     DOI: 10.1097/SHK.0000000000000328

Source DB:  PubMed          Journal:  Shock        ISSN: 1073-2322            Impact factor:   3.454


  1 in total

1.  Using support vector machines on photoplethysmographic signals to discriminate between hypovolemia and euvolemia.

Authors:  Natasa Reljin; Gary Zimmer; Yelena Malyuta; Kirk Shelley; Yitzhak Mendelson; David J Blehar; Chad E Darling; Ki H Chon
Journal:  PLoS One       Date:  2018-03-29       Impact factor: 3.240

  1 in total

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