Prehospital Vital Signs Accurately Predict Initial Emergency Department Vital Signs

Incidence, severity and changes of abnormal vital signs in trauma patients: A national population-based analysis

BACKGROUND

Physiological criteria are used to assess the potential severity of injury in the early phase of a trauma patient's care trajectory. Few studies have described the extent of abnormality in vital signs and different combinations of these at a national level. Aim of the study was to identify physiologic abnormalities in trauma patients and describe different combinations of abnormalities and changes between the pre-hospital and emergency department (ED) settings.

METHOD

Norwegian Trauma Registry (NTR) data between 01.01.15 - 31.12.18, where evaluated on the prevalence and characteristics of abnormal physiologic variables. Primary outcome were rates of hypoventilation (respiratory rate [RR] < 10 breaths per min), hyperventilation (RR > 29 breaths per min), hypotension (systolic blood pressure [SBP] < 90 mmHg), and reduced level of consciousness (Glasgow Coma Scale [GCS] < 13).

RESULTS

A total of 24,482 patients were included. Documented values for RR, SBP and GCS were 77.6%, 78.5% and 81.9% in the pre-hospital phase, and the corresponding percentages in the ED were 95.5%, 99.2% and 98.6%, respectively. In the pre-hospital phase, 3,615 (14.8%) patients had at least one abnormal vital sign, whereas the corresponding numbers in the ED, were 3,616 (14.8%) patients. The most frequent combination was low GCS and hyperventilation. A worsened RTS-score from pre-hospital phase to the ED was observed for RR, SBP and GCS in 3.9%, 1.2% and 1.9% of incidents, respectively. Overall 30-day mortality was 3.1% (n=752). Of these, 60.8% had abnormal vital signs, with decreased GCS as the most prevalent (61.3%).

CONCLUSION

Most trauma patients had normal vital signs. According to the RTS-score, there were few deteriorations in RR, SBP and GCS between pre-hospital phase and the ED. The most frequent abnormality was low GCS, with a higher proportion in those who died within 30 days.

Comparing NEWS2, TRISS, and RTS in predicting mortality rate in trauma patients based on prehospital data set: a diagnostic study

BACKGROUND

In the recent years, National Early Warning Score2 (NEWS2) is utilized to predict early on, the worsening of clinical status in patients. To this date the predictive accuracy of National Early Warning Score (NEWS2), Revised Trauma Score (RTS), and Trauma and injury severity score (TRISS) regarding the trauma patients' mortality rate have not been compared. Therefore, the objective of this study is comparing NEWS2, TRISS, and RTS in predicting mortality rate in trauma patients based on prehospital data set.

METHODS

This cross-sectional retrospective diagnostic study performed on 6905 trauma patients, of which 4191 were found eligible, referred to the largest trauma center in southern Iran, Shiraz, during 2022-2023 based on their prehospital data set in order to compare the prognostic power of NEWS2, RTS, and TRISS in predicting in-hospital mortality rate. Patients are divided into deceased and survived groups. Demographic data, vital signs, and GCS were obtained from the patients and scoring systems were calculated and compared between the two groups. TRISS and ISS are calculated with in-hospital data set; others are based on prehospital data set.

RESULTS

A total of 129 patients have deceased. Age, cause of injury, length of hospital stay, SBP, RR, HR, temperature, SpO2, and GCS were associated with mortality (p-value < 0.001). TRISS and RTS had the highest sensitivity and specificity respectively (77.52, CI 95% [69.3-84.4] and 93.99, CI 95% [93.2-94.7]). TRISS had the highest area under the ROC curve (0.934) followed by NEWS2 (0.879), GCS (0.815), RTS (0.812), and ISS (0.774). TRISS and NEWS were superior to RTS, GCS, and ISS (p-value < 0.0001).

CONCLUSION

This novel study compares the accuracy of NEWS2, TRISS, and RTS scoring systems in predicting mortality rate based on prehospital data. The findings suggest that all the scoring systems can predict mortality, with TRISS being the most accurate of them, followed by NEWS2. Considering the time consumption and ease of use, NEWS2 seems to be accurate and quick in predicting mortality based on prehospital data set.

Performance of 21 Early Warning System scores in predicting in-hospital deterioration among undifferentiated admitted patients managed by ambulance services

BACKGROUND

The optimal Early Warning System (EWS) scores for identifying patients at risk of clinical deterioration among those transported by ambulance services remain uncertain. This retrospective study compared the performance of 21 EWS scores to predict clinical deterioration using vital signs (VS) measured in the prehospital or emergency department (ED) setting.

METHODS

Adult patients transported to a single ED by ambulances and subsequently admitted to the hospital between 1 January 2019 and 18 April 2019 were eligible for inclusion. The primary outcome was 30-day mortality; secondary outcomes included 3-day mortality, admission to intensive care or coronary care units, length of hospital stay and emergency call activations. The discriminative ability of the EWS scores was assessed using the area under the receiver operating characteristic curve (AUROC). Subanalyses compared the performance of EWS scores between surgical and medical patient types.

RESULTS

Of 1414 patients, 995 (70.4%) (53.1% male, mean age 68.7±17.5 years) were included. In the ED setting, 30-day mortality was best predicted by VitalPAC EWS (AUROC 0.71, 95% CI (0.65 to 0.77)) and National Early Warning Score (0.709 (0.65 to 0.77)). All EWS scores calculated in the prehospital setting had AUROC <0.70. Rapid Emergency Medicine Score (0.83 (0.73 to 0.92)) and New Zealand EWS (0.88 (0.81 to 0.95)) best predicted 3-day mortality in the prehospital and ED settings, respectively. EWS scores calculated using either prehospital or ED VS were more effective in predicting 3-day mortality in surgical patients, whereas 30-day mortality was best predicted in medical patients. Among the EWS scores that achieved AUROC ≥0.70, no statistically significant differences were detected in their discriminatory abilities to identify patients at risk of clinical deterioration.

CONCLUSIONS

EWS scores better predict 3-day as opposed to 30-day mortality and are more accurate when estimated using VS measured in the ED. The discriminatory performance of EWS scores in identifying patients at higher risk of clinical deterioration may vary by patient type.

Prehospital Respiratory Early Warning Score for airway management in-ambulance: A score comparison

BACKGROUND

Prehospital Respiratory Early Warning Scores to estimate the requirement for advanced respiratory support is needed. To develop a prehospital Respiratory Early Warning Score to estimate the requirement for advanced respiratory support.

METHODS

Multicentre, prospective, emergency medical services (EMS)-delivered, longitudinal cohort derivationvalidation study carried out in 59 ambulances and five hospitals across five Spanish provinces. Adults with acute diseases evaluated, supported and discharged to the Emergency Department with high priority were eligible. The primary outcome was the need for invasive or non-invasive respiratory support (NIRS or IRS) in the prehospital scope at the first contact with the patient. The measures included the following: epidemiological endpoints, prehospital vital signs (respiratory rate, pulse oximetry saturation, fraction of inspired oxygen, systolic and diastolic mean blood pressure, heart rate, tympanic temperature and consciousness level by the GCS).

RESULTS

Between 26 Oct 2018 and 26 Oct 2021, we enrolled 5793 cases. For NIRS prediction, the final model of the logistic regression included respiratory rate and pulse oximetry saturation/fraction of inspired oxygen ratio. For the IRS case, the motor response from the Glasgow Coma Scale was also included. The REWS showed an AUC of 0.938 (95% CI: 0.918-0.958), a calibration-in-large of 0.026 and a higher net benefit as compared with the other scores.

CONCLUSIONS

Our results showed that REWS is a remarkably aid for the decision-making process in the management of advanced respiratory support in prehospital care. Including this score in the prehospital scenario could improve patients' care and optimise the resources' management.

Need for Emergent Intervention within 6 Hours: A Novel Prediction Model for Hospital Trauma Triage

Objective: A tiered trauma team activation system allocates resources proportional to patients' needs based upon injury burden. Previous trauma hospital-triage models are limited to predicting Injury Severity Score which is based on > 10% all-cause in-hospital mortality, rather than need for emergent intervention within 6 hours (NEI-6). Our aim was to develop a novel prediction model for hospital-triage that utilizes criteria available to the EMS provider to predict NEI-6 and the need for a trauma team activation.Methods: A regional trauma quality collaborative was used to identify all trauma patients ≥ 16 years from the American College of Surgeons-Committee on Trauma verified Level 1 and 2 trauma centers. Logistic regression and random forest were used to construct two predictive models for NEI-6 based on clinically relevant variables. Restricted cubic splines were used to model nonlinear predictors. The accuracy of the prediction model was assessed in terms of discrimination.Results: Using data from 12,624 patients for the training dataset (62.6% male; median age 61 years; median ISS 9) and 9,445 patients for the validation dataset (62.6% male; median age 59 years; median ISS 9), the following significant predictors were selected for the prediction models: age, gender, field GCS, vital signs, intentionality, and mechanism of injury. The final boosted tree model showed an AUC of 0.85 in the validation cohort for predicting NEI-6.Conclusions: The NEI-6 trauma triage prediction model used prehospital metrics to predict need for highest level of trauma activation. Prehospital prediction of major trauma may reduce undertriage mortality and improve resource utilization.

Weather and prehospital predictors of trauma patient mortality in a rural American state

Introduction

In rural settings, factors like weather and location can significantly impact total prehospital time and survival after injury. We sought to determine what prehospital conditions affect mortality and morbidity in severely injured patients.

Materials and methods

We retrospectively evaluated adult trauma patients that were admitted to our level 1 trauma center with Glasgow Coma Score (GCS≤ 9), hypotension (SBP≤ 90 mmHg), or both. Weather and prehospital conditions on patient outcomes were evaluated. Weather data was extracted from the National Oceanographic and Atmospheric Administration public database. Prediction models were done using bivariate and multivariate logistic regression analysis.

Results

A total of 442 subjects were captured, Median time on the scene was 15 min  [IQR =10, 20.5], with median time to definitive care 129 min  [IQR= 61, 247]. Hypotension in the field was the greatest predictor of ED mortality (OR=11, P = 0.004), and field hypoxia (OR=3, P = 0.007) was a predictor of in-hospital mortality. Patients with field GCS ≤ 9 had higher odds of ICU admission (OR=2, P = 0.029). Among transfers, increasing prehospital time correlated with ED mortality while injury during warmer weather showed lower odds (OR =0.94, P = 0.019) of mortality. No weather condition predicted mortality for patients that presented directly from the field.

Conclusion

Among severely injured patients being injured during cold weather was associated with higher in-hospital mortality among trauma transfer patients. Prehospital hypotension, hypoxia, and GCS≤9 are also independent predictors of mortality. Future analysis will explore factors impacting transport and field time in order to improve outcomes.

Emergency medical services shock index is the most accurate predictor of patient outcomes after blunt torso trauma

INTRODUCTION

Shock index (SI) and delta shock index (∆SI) predict mortality and blood transfusion in trauma patients. This study aimed to evaluate the predictive ability of SI and ∆SI in a rural environment with prolonged transport times and transfers from critical access hospitals or level IV trauma centers.

METHODS

We completed a retrospective database review at an American College of Surgeons verified level 1 trauma center for 2 years. Adult subjects analyzed sustained torso trauma. Subjects with missing data or severe head trauma were excluded. For analysis, poisson regression and binomial logistic regression were used to study the effect of time in transport and SI/∆SI on resource utilization and outcomes. p < 0.05 was considered significant.

RESULTS

Complete data were available on 549 scene patients and 127 transfers. Mean Injury Severity Score was 11 (interquartile range, 9.0) for scene and 13 (interquartile range, 6.5) for transfers. Initial emergency medical services SI was the most significant predictor for blood transfusion and intensive care unit care in both scene and transferred patients (p < 0.0001) compared with trauma center arrival SI or transferring center SI. A negative ∆SI was significantly associated with the need for transfusion and the number of units transfused. Longer transport time also had a significant relationship with increasing intensive care unit length of stay. Cohorts were analyzed separately.

CONCLUSION

Providers must maintain a high level of clinical suspicion for patients who had an initially elevated SI. Emergency medical services SI was the greatest predictor of injury and need for resources. Enroute SI and ∆SI were less predictive as time from injury increased. This highlights the improvements in en route care but does not eliminate the need for high-level trauma intervention.

LEVEL OF EVIDENCE

Therapeutic/care management, level IV.

Vital and Clinical Signs Gathered Within the First Minutes After a Motorcycle Accident on a Racetrack: an Observational Study

Background

Little is known about vital signs during the very first minutes after an accident. This study aimed to describe the vital signs of motorcycle riders shortly after racetrack crashes and examine the clinical value of these data for the prehospital clinical assessments.

Methods

A retrospective observational cohort based on data from medical records on 104 motorcycle accidents at a racetrack in Sweden, covering the season of 2019 (May 01 until September 17), was conducted. Both race and practice runs were included. In addition, data from the Swedish Trauma Registry were used for patients referred to the hospital. Kruskal-Wallis test and linear regression were calculated in addition to descriptive statistics.

Results

In all, 30 riders (29%) were considered injured. Sixteen riders (15%) were referred to the hospital, and of these, five patients (5% of all riders) had suffered serious injuries. Aside from a decreased level of consciousness, no single vital sign or kinematic component observed within the early minutes after a crash was a strong clinical indicator of the occurrence of injuries. However, weak links were found between highsider or collision crashes and the occurrence of injuries.

Conclusion

Except for a decreased level of consciousness, this study indicates that the clinical value of early measured vital signs might be limited for the pre-hospital clinical assessment in the motorsport environment. Also, an adjustment of general trauma triage protocols might be considered for settings such as racetracks. Using the context with medical professionals at the victim’s side within a few minutes after an accident, that is common in motorsport, offers unique possibilities to increase our understanding of clinical signs and trauma in the early state after an accident.

Field-Triage, Hospital-Triage and Triage-Assessment: A Literature Review of the Current Phases of Adult Trauma Triage

ABSTRACT

Despite major improvements in the United States trauma system over the past two decades, prehospital trauma triage is a significant challenge. Undertriage is associated with increased mortality, and overtriage results in significant resource overuse. The American College of Surgeons Committee on Trauma benchmarks for undertriage and overtriage are not being met. Many barriers to appropriate field triage exist, including lack of a formal definition for major trauma, absence of a simple and widely applicable triage mode, and emergency medical service adherence to triage protocols. Modern trauma triage systems should ideally be based on the need for intervention rather than injury severity. Future studies should focus on identifying the ideal definition for major trauma and creating triage models that can be easily deployed. This narrative review article presents challenges and potential solutions for prehospital trauma triage.

Longer Prehospital Time Decreases Reliability of Vital Signs in the Field: A Dual Center Study

BACKGROUND

Field vital signs are integral in the American College of Surgeons (ASA) Committee on Trauma (COT) triage criteria for trauma team activation (TTA). Reliability of field vital signs in predicting first emergency department (ED) vital signs, however, may depend upon prehospital time. The study objective was to define the effect of prehospital time on correlation between field and first ED vital signs.

METHODS

All highest level TTAs at two Level I trauma centers (2008-2018) were screened. Exclusions were unrecorded prehospital vital signs and those dead on arrival. Demographics, prehospital time (scene time + transport time), injury data, and vital signs were collected. Differences between field and first ED vitals were determined using the paired Student's t test. Propensity score analysis, adjusting for age, sex, injury severity score (ISS), and mechanism of injury compared outcomes among patients with ISS ≥16. Multivariate linear regression determined impact of prehospital time on vital sign differences between field and ED among propensity-matched patients.

RESULTS

After exclusions, 21 499 patients remained. Mean prehospital time was 32 vs. 41 minutes (P < .001). On propensity score analysis, longer prehospital time was associated with significantly greater differences in systolic blood pressure (SBP) (P < .001), pulse pressure (PP) (P = .003), and Glasgow Coma Scale (GCS) (P < .001). On multivariate analysis, linear regression that demonstrated longer prehospital time was associated with greater differences in SBP, heart rate (HR), and PP (P < .001).

CONCLUSIONS

Field vital signs are less likely to reflect initial ED vital signs when prehospital times are longer. Given the reliance of trauma triage criteria on prehospital vital signs, medical providers must be cognizant of this pitfall during the prehospital assessment of trauma patients.