The importance of vital signs in the triage of injured patients

Evolving the SCAT5 for Ruling Out Higher-Severity Traumatic Brain Injuries-Can Decision Rules Developed for Emergency Settings Help?

BACKGROUND: Decision rules (eg, Canadian computed tomography head rule [CCHR] for adults and Pediatric Emergency Care Applied Research Network [PECARN] rule for children/adolescents) are used in emergency settings (emergency room [ER] rules) to assess traumatic brain injuries (TBIs). The rules have a high-sensitivity and near-perfect negative predictive value that help to rule out more severe TBI. CLINICAL QUESTION: Which criteria should be added to the Sport Concussion Assessment Tool 5 (SCAT5) to reach the sensitivity of the ER rules and improve the utility of the SCAT5 for screening for higher-severity head and brain injuries? KEY RESULTS: We performed a comparative analysis of the SCAT5 with the CCHR and PECARN rules. We compared the presence (yes or no) and comparative "face value" sensitivity (lower, identical, or higher) of the SCAT5 criteria to the ER rules criteria. Loss of consciousness, vomiting, severe/increasing headache, and seizure are SCAT "red flags" with similar or higher sensitivity compared to ER rules criteria. Five criteria had lower sensitivity or were absent from the SCAT. Emergency room rules include any abnormality on the Glasgow Coma Scale (GCS<15), but only a "deterioration of the state of consciousness" is considered a "red flag" in the SCAT5. Persistent retrograde amnesia for more than 30 minutes, age>65, severity of the mechanism of injury, and signs of skull fractures are not mentioned in the SCAT5. CLINICAL APPLICATION: We identified 5 criteria that could inform the evolution the SCAT5 to improve its ability to rule out more severe TBI in a sideline assessment context. J Orthop Sports Phys Ther 2023;53(3):113-119. Epub: 9 December 2022. doi:10.2519/jospt.2022.11301.

Deep Learning Methods for Remote Heart Rate Measurement: A Review and Future Research Agenda

Heart rate (HR) is one of the essential vital signs used to indicate the physiological health of the human body. While traditional HR monitors usually require contact with skin, remote photoplethysmography (rPPG) enables contactless HR monitoring by capturing subtle light changes of skin through a video camera. Given the vast potential of this technology in the future of digital healthcare, remote monitoring of physiological signals has gained significant traction in the research community. In recent years, the success of deep learning (DL) methods for image and video analysis has inspired researchers to apply such techniques to various parts of the remote physiological signal extraction pipeline. In this paper, we discuss several recent advances of DL-based methods specifically for remote HR measurement, categorizing them based on model architecture and application. We further detail relevant real-world applications of remote physiological monitoring and summarize various common resources used to accelerate related research progress. Lastly, we analyze the implications of research findings and discuss research gaps to guide future explorations.

Battlefield Vital Sign Monitoring in Role 1 Military Treatment Facilities: A Thematic Analysis of After-Action Reviews from the Prehospital Trauma Registry

INTRODUCTION

The Prehospital Trauma Registry (PHTR) captures after-action reviews (AARs) as part of a continuous performance improvement cycle and to provide commanders real-time feedback of Role 1 care. We have previously described overall challenges noted within the AARs. We now performed a focused assessment of challenges with regard to hemodynamic monitoring to improve casualty monitoring systems.

MATERIALS AND METHODS

We performed a review of AARs within the PHTR in Afghanistan from January 2013 to September 2014 as previously described. In this analysis, we focus on AARs specific to challenges with hemodynamic monitoring of combat casualties.

RESULTS

Of the 705 PHTR casualties, 592 had available AAR data; 86 of those described challenges with hemodynamic monitoring. Most were identified as male (97%) and having sustained battle injuries (93%), typically from an explosion (48%). Most were urgent evacuation status (85%) and had a medical officer in their chain of care (65%). The most common vital sign mentioned in AAR comments was blood pressure (62%), and nearly one-quarter of comments stated that arterial palpation was used in place of blood pressure cuff measurements.

CONCLUSIONS

Our qualitative methods study highlights the challenges with obtaining vital signs-both training and equipment. We also highlight the challenges regarding ongoing monitoring to prevent hemodynamic collapse in severely injured casualties. The U.S. military needs to develop better methods for casualty monitoring for the subset of casualties that are critically injured.

Wearable Sensors Incorporating Compensatory Reserve Measurement for Advancing Physiological Monitoring in Critically Injured Trauma Patients

Vital signs historically served as the primary method to triage patients and resources for trauma and emergency care, but have failed to provide clinically-meaningful predictive information about patient clinical status. In this review, a framework is presented that focuses on potential wearable sensor technologies that can harness necessary electronic physiological signal integration with a current state-of-the-art predictive machine-learning algorithm that provides early clinical assessment of hypovolemia status to impact patient outcome. The ability to study the physiology of hemorrhage using a human model of progressive central hypovolemia led to the development of a novel machine-learning algorithm known as the compensatory reserve measurement (CRM). Greater sensitivity, specificity, and diagnostic accuracy to detect hemorrhage and onset of decompensated shock has been demonstrated by the CRM when compared to all standard vital signs and hemodynamic variables. The development of CRM revealed that continuous measurements of changes in arterial waveform features represented the most integrated signal of physiological compensation for conditions of reduced systemic oxygen delivery. In this review, detailed analysis of sensor technologies that include photoplethysmography, tonometry, ultrasound-based blood pressure, and cardiogenic vibration are identified as potential candidates for harnessing arterial waveform analog features required for real-time calculation of CRM. The integration of wearable sensors with the CRM algorithm provides a potentially powerful medical monitoring advancement to save civilian and military lives in emergency medical settings.