Identification of dynamic prehospital changes with continuous vital signs acquisition

Explainable Prediction Model of the Need for Emergency Hemostasis Using Field Information During Physician-Staffed Helicopter Emergency Medical Service Interventions: A Single-Center, Retrospective, Observational Pilot Study

OBJECTIVE

Early recognition of hemostasis is important to prevent trauma-related deaths. We conducted a pilot study of a predictive model of hemostatic need using factors that can be collected during helicopter emergency medical service (HEMS) interventions until transport hospital selection using cases from our institution.

METHODS

This single-center, retrospective, observational pilot study included 251 trauma patients aged ≥ 18 years treated with HEMS between April 2017 and March 2022, in Nara Medical University. Cardiac arrest and pre-HEMS treatment patients were excluded. Emergency hemostatic surgery prediction models were constructed using the light gradient boosting machine cross-validation method using objective data that could be collected before hospital determination. The accuracy of this model was compared with that of the ground emergency medical service-based model, and factors influencing outcome were visualized using Shapley additive explanations.

RESULTS

The predictive accuracy of the model with HEMS intervention factors was an area under the receiver operating characteristic curve of 0.80, superior to the 0.73 accuracy area under the receiver operating characteristic curve for ground emergency medical services constructed with contact information. Clinically important factors, such as shock index, blood pressure changes, and ultrasound findings, had a significant impact on outcomes, with nonmonotonic effects observed across factors.

CONCLUSION

This pilot study suggests that predictive models of emergency hemostasis can be built using limited prehospital information. To validate this model, a larger, multicenter study is recommended.

Comparison of Changes in Vital Signs During Ground and Helicopter Emergency Medical Services and Hospital Interventions

OBJECTIVE

Few studies have evaluated the effects of helicopter emergency medical services (HEMS) alone. This single-center study compared the changes in vital signs during ground emergency medical services (GEMS), HEMS, and hospital interventions to assess the impact of HEMS interventions.

METHODS

This retrospective observational study included 168 trauma patients older than 18 years of age who received HEMS. Patients with cardiac arrest or those who received medical attention before HEMS were excluded. We assessed 3 intervention phases (GEMS, HEMS, and hospital). The changes in heart rate, systolic blood pressure, respiratory rate, and shock index in response to interventions were calculated and divided by the intervention time, and the changes observed during the interventions were compared.

RESULTS

No changes in vital signs were observed when receiving GEMS. Systolic blood pressure increased and shock index decreased after HEMS, whereas systolic blood pressure decreased and shock index increased during hospital interventions. Heart rate showed no significant change (P = .12), and respiratory rate showed very little change. Systolic blood pressure increased significantly during HEMS compared with the pre- and postintervention periods.

CONCLUSION

Changes in vital signs differed according to the intervention. Systolic blood pressure increased during HEMS but not with GEMS or hospital interventions.

Contactless Vital Signs Acquisition Using Video Photoplethysmography, Motion Analysis and Passive Infrared Thermography Devices During Emergency Department Walk-In Triage in Pandemic Conditions

BACKGROUND

Contactless vital signs (VS) measurement with video photoplethysmography (vPPG), motion analysis (MA), and passive infrared thermometry (pIR) has shown promise.

OBJECTIVES

To compare conventional (contact-based) and experimental contactless VS measurement approaches for emergency department (ED) walk-in triage in pandemic conditions.

METHODS

Patients' heart rates (HR), respiratory rates (RR), and temperatures were measured with cardiorespiratory monitor and vPPG, manual count and MA, and contact thermometers and pIR, respectively.

RESULTS

There were 475 walk-in ED patients studied (95% of eligible). Subjects were 35.2 ± 20.8 years old (range 4 days‒95 years); 52% female, 0.2% transgender; had Fitzpatrick skin type of 2.3 ± 1.4 (range 1‒6), Emergency Severity Index of 3.0 ± 0.6 (range 2‒5), and contact temperature of 36.83°C (range 35.89-39.4°C) (98.3°F [96.6‒103°F]). Pediatric HR and RR data were excluded from analysis due to research challenges associated with pandemic workflow. For a 30-s, unprimed "Triage" window in 377 adult patients, vPPG-MA acquired 377 (100%) HR measurements featuring a mean difference with cardiorespiratory monitor HR of 5.9 ± 12.8 beats/min (R = 0.6833) and 252 (66.8%) RR measurements featuring a mean difference with manual RR of -0.4 ± 2.6 beats/min (R = 0.8128). Subjects' Emergency Severity Index components based on conventional VS and contactless VS matched for 83.8% (HR) and 89.3% (RR). Filtering out vPPG-MA measurements with low algorithmic confidence reduced VS acquired while improving correlation with conventional measurements. The mean difference between contact and pIR temperatures was 0.83 ± 0.67°C (range -1.16-3.5°C) (1.5 ± 1.2°F [range -2.1-6.3°F]); pIR fever detection improved with post hoc adjustment for mean bias.

CONCLUSION

Contactless VS acquisition demonstrated good agreement with contact methods during adult walk-in ED patient triage in pandemic conditions; clinical applications will need further study.

The Use of Big Data via 5G to Alleviate Symptoms of Acute Stress Disorder Caused by Quarantine Measures

This article investigates the role of Big Data in situations of psychological stress such as during the recent pandemic caused by the COVID-19 health crisis. Quarantine measures, which are necessary to mitigate pandemic risk, are causing severe stress symptoms to the human body including mental health. We highlight the most common impact factors and the uncertainty connected with COVID-19, quarantine measures, and the role of Big Data, namely, how Big Data can help alleviate or mitigate these effects by comparing the status quo of current technology capabilities with the potential effects of an increase of digitalization on mental health. We find that, while Big Data helps in the pre-assessment of potentially endangered persons, it also proves to be an efficient tool in alleviating the negative psychological effects of quarantine. We find evidence of the positive effects of Big Data on human health conditions by assessing the effect of internet use on mental health in 173 countries. We found positive effects in 110 countries with 90 significant results. However, increased use of digital media and exclusive exposure to digital connectivity causes negative long-term effects such as a decline in social empathy, which creates a form of psychological isolation, causing symptoms of acute stress disorder.

A Remote Patient-Monitoring System for Intensive Care Medicine: Mixed Methods Human-Centered Design and Usability Evaluation

Background

Continuous monitoring of vital signs is critical for ensuring patient safety in intensive care units (ICUs) and is becoming increasingly relevant in general wards. The effectiveness of health information technologies such as patient-monitoring systems is highly determined by usability, the lack of which can ultimately compromise patient safety. Usability problems can be identified and prevented by involving users (ie, clinicians).

Objective

In this study, we aim to apply a human-centered design approach to evaluate the usability of a remote patient-monitoring system user interface (UI) in the ICU context and conceptualize and evaluate design changes.

Methods

Following institutional review board approval (EA1/031/18), a formative evaluation of the monitoring UI was performed. Simulated use tests with think-aloud protocols were conducted with ICU staff (n=5), and the resulting qualitative data were analyzed using a deductive analytic approach. On the basis of the identified usability problems, we conceptualized informed design changes and applied them to develop an improved prototype of the monitoring UI. Comparing the UIs, we evaluated perceived usability using the System Usability Scale, performance efficiency with the normative path deviation, and effectiveness by measuring the task completion rate (n=5). Measures were tested for statistical significance using a 2-sample t test, Poisson regression with a generalized linear mixed-effects model, and the N-1 chi-square test. P<.05 were considered significant.

Results

We found 37 individual usability problems specific to monitoring UI, which could be assigned to six subcodes: usefulness of the system, response time, responsiveness, meaning of labels, function of UI elements, and navigation. Among user ideas and requirements for the UI were high usability, customizability, and the provision of audible alarm notifications. Changes in graphics and design were proposed to allow for better navigation, information retrieval, and spatial orientation. The UI was revised by creating a prototype with a more responsive design and changes regarding labeling and UI elements. Statistical analysis showed that perceived usability improved significantly (System Usability Scale design A: mean 68.5, SD 11.26, n=5; design B: mean 89, SD 4.87, n=5; P=.003), as did performance efficiency (normative path deviation design A: mean 8.8, SD 5.26, n=5; design B: mean 3.2, SD 3.03, n=5; P=.001), and effectiveness (design A: 18 trials, failed 7, 39% times, passed 11, 61% times; design B: 20 trials, failed 0 times, passed 20 times; P=.002).

Conclusions

Usability testing with think-aloud protocols led to a patient-monitoring UI with significantly improved usability, performance, and effectiveness. In the ICU work environment, difficult-to-use technology may result in detrimental outcomes for staff and patients. Technical devices should be designed to support efficient and effective work processes. Our results suggest that this can be achieved by applying basic human-centered design methods and principles.

Trial Registration

ClinicalTrials.gov NCT03514173; https://clinicaltrials.gov/ct2/show/NCT03514173

Prehospital continuous vital signs predict need for resuscitative endovascular balloon occlusion of the aorta and resuscitative thoracotomy prehospital continuous vital signs predict resuscitative endovascular balloon occlusion of the aorta

BACKGROUND

Rapid triage and intervention to control hemorrhage are key to survival following traumatic injury. Patients presenting in hemorrhagic shock may undergo resuscitative thoracotomy (RT) or resuscitative endovascular balloon occlusion of the aorta (REBOA) as adjuncts to rapidly control bleeding. We hypothesized that machine learning along with automated calculation of continuously measured vital signs in the prehospital setting would accurately predict need for REBOA/RT and inform rapid lifesaving decisions.

METHODS

Prehospital and admission data from 1,396 patients transported from the scene of injury to a Level I trauma center via helicopter were analyzed. Utilizing machine learning and prehospital autonomous vital signs, a Bleeding Risk Index (BRI) based on features from pulse oximetry and electrocardiography waveforms and blood pressure (BP) trends was calculated. Demographics, Injury Severity Score and BRI were compared using Mann-Whitney-Wilcox test. Area under the receiver operating characteristic curve (AUC) was calculated and AUC of different scores compared using DeLong's method.

RESULTS

Of the 1,396 patients, median age was 45 years and 68% were men. Patients who underwent REBOA/RT were more likely to have a penetrating injury (24% vs. 7%, p < 0.001), higher Injury Severity Score (25 vs. 10, p < 0.001) and higher mortality (44% vs. 7%, p < 0.001). Prehospital they had lower BP (96 [70-130] vs. 134 [117-152], p < 0.001) and higher heart rate (106 [82-118] vs. 90 [76-106], p < 0.001). Bleeding risk index calculated using the entire prehospital period was 10× higher in patients undergoing REBOA/RT (0.5 [0.42-0.63] vs. 0.05 [0.02-0.21], p < 0.001) with an AUC of 0.93 (95% confidence interval [95% CI], 0.90-0.97). This was similarly predictive when calculated from shorter periods of transport: BRI initial 10 minutes prehospital AUC of 0.89 (95% CI, 0.83-0.94) and initial 5 minutes AUC of 0.90 (95% CI, 0.85-0.94).

CONCLUSION

Automated prehospital calculations based on vital sign features and trends accurately predict the need for the emergent REBOA/RT. This information can provide essential time for team preparedness and guide trauma triage and disaster management.

LEVEL OF EVIDENCE

Therapeutic/care management, Level IV.

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.

A Review of Data Quality Assessment in Emergency Medical Services

INTRODUCTION

Data quality is an important issue in emergency medicine. The unique characteristics of emergency care services, such as high turn-over and the speed of work may increase the possibility of making errors in the related settings. Therefore, regular data quality assessment is necessary to avoid the consequences of low quality data. This study aimed to identify the main dimensions of data quality which had been assessed, the assessment approaches, and generally, the status of data quality in the emergency medical services.

METHODS

The review was conducted in 2016. Related articles were identified by searching databases, including Scopus, Science Direct, PubMed and Web of Science. All of the review and research papers related to data quality assessment in the emergency care services and published between 2000 and 2015 (n=34) were included in the study.

RESULTS

The findings showed that the five dimensions of data quality; namely, data completeness, accuracy, consistency, accessibility, and timeliness had been investigated in the field of emergency medical services. Regarding the assessment methods, quantitative research methods were used more than the qualitative or the mixed methods. Overall, the results of these studies showed that data completeness and data accuracy requires more attention to be improved.

CONCLUSION

In the future studies, choosing a clear and a consistent definition of data quality is required. Moreover, the use of qualitative research methods or the mixed methods is suggested, as data users' perspectives can provide a broader picture of the reasons for poor quality data.

Prehospital End-tidal Carbon Dioxide Predicts Mortality in Trauma Patients

BACKGROUND

End-tidal carbon dioxide (EtCO₂) measurement has been shown to have prognostic value in acute trauma.

OBJECTIVE

Evaluate the association of prehospital EtCO₂ and in-hospital mortality in trauma patients and to assess its prognostic value when compared to traditional vital signs.

METHODS

Retrospective, cross-sectional study of patients transported by a single EMS agency to a level one trauma center. We evaluated initial out-of-hospital vital signs documented by EMS personnel including EtCO₂, respiratory rate (RR), systolic BP (SBP), diastolic BP (DBP), pulse (P), and oxygen saturation (O₂) and hospital data. The main outcome measure was mortality.

RESULTS

135 trauma patients were included; 9 (7%) did not survive. The mean age of patients was 40 (SD17) [Range 16-89], 97 (72%) were male, 76 (56%) were admitted to the hospital and 15 (11%) went to the ICU. The mean EtCO2 level was 18 mmHg (95%CI 9-28) [Range 5-41] in non-survivors compared to 34 mmHg (95%CI 32-35) [Range 11-51] in survivors. The area under the ROC curve (AUC) for EtCO₂ in predicting mortality was 0.84 (0.67-1.00) (p = 0.001), RR was 0.82 (0.63-1.00), SBP was 0.72 (0.49-0.96), DBP was 0.72 (0.47-0.97), pulse was 0.51 (0.26-0.76), and O₂ was 0.64 (0.37-0.91). Cut-off values at 30 mmHg yielded sensitivity = 89% (51-99), specificity = 68% (59-76), PPV = 13% (6-24) and NPV = 99% (93-100) for predicting mortality. There was no correlation between RR and EtCO₂ (correlation 0.16; p = 0.06).

CONCLUSION

We found an inverse association between prehospital EtCO₂ and mortality. This has implications for improving triage and assisting EMS in directing patients to an appropriate trauma center.

Anomaly Detection Outperforms Logistic Regression in Predicting Outcomes in Trauma Patients

OBJECTIVE

Recent advancements in trauma resuscitation have shown a great benefit of early identification and control of hemorrhage, which is the most common cause of death in injured patients. We introduce a new analytical approach, anomaly detection (AD), as an alternative method to the traditional logistic regression (LR) method in predicting which injured patients receive transfusions, intensive care, and other interventions.

METHODS

We abstracted routinely collected prehospital vital sign data from patient records (adult patients who survived more than 15 minutes after being directly admitted to a level 1 trauma center). The vital signs of the study cohort were analyzed using both LR and AD methods. Predictions on blood transfusions generated by these approaches were compared with hospital records using the respective areas under the receiver operating characteristic curves (AUROC).

RESULTS

Of the patients seen at our trauma center between January 1, 2009, and December 31, 2010, 5,464 were included. AD significantly outperformed LR, identifying which patients would receive transfusions of uncrossmatched blood, transfusion of blood between the time of admission and 6 hours later, the need for intensive care, and in-hospital mortality (mean AUROC = 0.764 and 0.720, respectively). AD and LR provided similar predictions for the patients who would receive massive transfusion. Under the stratified 10 fold times 10 cross-validation test, AD also had significantly lower AUROC variance across subgroups than LR, suggesting AD is a more stable predictions model.

CONCLUSIONS

AD provides enhanced predictions for clinically relevant outcomes in the trauma patient cohort studied and may assist providers in caring for acutely injured patients in the prehospital arena.

Computer Modelling Using Prehospital Vitals Predicts Transfusion and Mortality

OBJECTIVE

Test computer-assisted modeling techniques using prehospital vital signs of injured patients to predict emergency transfusion requirements, number of intensive care days, and mortality, compared to vital signs alone.

METHODS

This single-center retrospective analysis of 17,988 trauma patients used vital signs data collected between 2006 and 2012 to predict which patients would receive transfusion, require 3 or more days of intensive care, or die. Standard transmitted prehospital vital signs (heart rate, blood pressure, shock index, and respiratory rate) were used to create a regression model (PH-VS) that was internally validated and evaluated using area under the receiver operating curve (AUROC). Transfusion records were matched with blood bank records. Documentation of death and duration of intensive care were obtained from the trauma registry.

RESULTS

During the course of their hospital stay, 720 of the 17,988 patients in the study population died (4%), 2,266 (12.6%) required at least a 3-day stay in the intensive care unit (ICU), 1,171 (6.5%) required transfusions, and 210 (1.2%) received massive transfusions. The PH-VS model significantly outperformed any individual vital sign across all outcomes (average AUROC = 0.82), The PH-VS model correctly predicted that 512 of 777 (65.9%) and 580 of 931 (62.3%) patients in the study population would receive transfusions within the first 2 and 6 hours of admission, respectively.

CONCLUSIONS

The predictive ability of individual vital signs to predict outcomes is significantly enhanced with the model. This could support prehospital triage by enhancing decision makers' ability to match critically injured patients with appropriate resources with minimal delays.

Classifying, measuring and improving the quality of data in trauma registries: A review of the literature

INTRODUCTION

Globally, injury is a major cause of death and disability. Improvements in trauma care have been driven by trauma registries. The capacity of a trauma registry to inform improvements in the quality of trauma care is dependent upon the quality of data. The literature on data quality in disease registries is inconsistent and ambiguous; methods used for classifying, measuring, and improving data quality are not standardised. The aim of this study was to review the literature to determine the methods used to classify, measure and improve data quality in trauma registries.

METHODS

A scoping review of the literature was performed. Databases were searched using the term "trauma registry" and its synonyms, combined with multiple terms denoting data quality. There was no restriction on year. Full-length manuscripts were included if the classification, measurement or improvement of data quality in one or more trauma registries was a study objective. Data were abstracted regarding registry demographics, study design, data quality classification, and the reported methods used to measure and improve the pre-defined data quality dimensions of accuracy, completeness and capture.

RESULTS

Sixty-nine publications met the inclusion criteria. Four publications classified data quality. The most frequently described methods for measuring data accuracy (n=47) were checks against other datasets (n=18) and checks of injury coding (n=17). The most frequently described methods for measuring data completeness (n=47) were the percentage of included cases, for a given variable or list of variables, for which there was an observation in the registry (n=29). The most frequently described methods for measuring data capture (n=37) were the percentage of cases in a linked reference dataset that were also captured in the primary dataset being evaluated (n=24). Most publications dealing with the measurement of a dimension of data quality did not specify the methods used; most publications dealing with the improvement of data quality did not specify the dimension being targeted.

CONCLUSION

The classification, measurement and improvement of data quality in trauma registries is inconsistent. To maintain confidence in the usefulness of trauma registries, the metrics and reporting of data quality need to be standardised.

Predicting blood transfusion using automated analysis of pulse oximetry signals and laboratory values

BACKGROUND

Identification of hemorrhaging trauma patients and prediction of blood transfusion needs in near real time will expedite care of the critically injured. We hypothesized that automated analysis of pulse oximetry signals in combination with laboratory values and vital signs obtained at the time of triage would predict the need for blood transfusion with accuracy greater than that of triage vital signs or pulse oximetry analysis alone.

METHODS

Continuous pulse oximetry signals were recorded for directly admitted trauma patients with abnormal prehospital shock index (heart rate [HR] / systolic blood pressure) of 0.62 or greater. Predictions of blood transfusion within 24 hours were compared using Delong's method for area under the receiver operating characteristic (AUROC) curves to determine the optimal combination of triage vital signs (prehospital HR + systolic blood pressure), pulse oximetry features (40 waveform features, O2 saturation, HR), and laboratory values (hematocrit, electrolytes, bicarbonate, prothrombin time, international normalization ratio, lactate) in multivariate logistic regression models.

RESULTS

We enrolled 1,191 patients; 339 were excluded because of incomplete data; 40 received blood within 3 hours; and 14 received massive transfusion. Triage vital signs predicted need for transfusion within 3 hours (AUROC, 0.59) and massive transfusion (AUROC, 0.70). Pulse oximetry for 15 minutes predicted transfusion more accurately than triage vital signs for both time frames (3-hour AUROC, 0.74; p = 0.004) (massive transfusion AUROC, 0.88; p < 0.001). An algorithm including triage vital signs, pulse oximetry features, and laboratory values improved accuracy of transfusion prediction (3-hour AUROC, 0.84; p < 0.001) (massive transfusion AUROC, 0.91; p < 0.001).

CONCLUSION

Automated analysis of triage vital signs, 15 minutes of pulse oximetry signals, and laboratory values predicted use of blood transfusion during trauma resuscitation more accurately than triage vital signs or pulse oximetry analysis alone. Results suggest automated calculations from a noninvasive vital sign monitor interfaced with a point-of-care laboratory device may support clinical decisions by recognizing patients with hemorrhage sufficient to need transfusion.

LEVEL OF EVIDENCE

Epidemiologic/prognostic study, level III.

Development of a prehospital vital signs chart sharing system

OBJECTIVE

Physiological parameters are crucial for the caring of trauma patients. There is a significant loss of prehospital vital signs data of patients during handover between prehospital and in-hospital teams. Effective strategies for reducing the loss remain a challenging research area. We tested whether the newly developed electronic automated prehospital vital signs chart sharing system would increase the amount of prehospital vital signs data shared with a remote trauma center prior to hospital arrival.

METHODS

Fifty trauma patients, transferred to a level I trauma center in Japan, were studied. The primary outcome variable was the number of prehospital vital signs shared with the trauma center prior to hospital arrival.

RESULTS

The prehospital vital signs chart sharing system significantly increased the number of prehospital vital signs, including blood pressure, heart rate, and oxygen saturation, shared with the in-hospital team at a remote trauma center prior to patient arrival at the hospital (P < .0001). There were significant differences in prehospital vital signs during ambulance transfer between patients who had severe bleeding and non-severe bleeding within 24 hours after injury onset.

CONCLUSIONS

Vital signs data collected during ambulance transfer via patient monitors could be automatically converted to easily visible patient charts and effectively shared with the remote trauma center prior to hospital arrival. The prehospital vital signs chart sharing system increased the number of precise vital signs shared prior to patient arrival at the hospital, which can potentially contribute to better trauma care without increasing labor and reduce information loss during clinical handover.

Big Data Analytics in Healthcare

The rapidly expanding field of big data analytics has started to play a pivotal role in the evolution of healthcare practices and research. It has provided tools to accumulate, manage, analyze, and assimilate large volumes of disparate, structured, and unstructured data produced by current healthcare systems. Big data analytics has been recently applied towards aiding the process of care delivery and disease exploration. However, the adoption rate and research development in this space is still hindered by some fundamental problems inherent within the big data paradigm. In this paper, we discuss some of these major challenges with a focus on three upcoming and promising areas of medical research: image, signal, and genomics based analytics. Recent research which targets utilization of large volumes of medical data while combining multimodal data from disparate sources is discussed. Potential areas of research within this field which have the ability to provide meaningful impact on healthcare delivery are also examined.