Lifesaving Interventions In Trauma Patients Research Articles

Relationship between the Injury Severity Score and the need for life-saving interventions in trauma patients in the UK

IntroductionMajor trauma is the third leading cause of avoidable mortality in the UK. Defining which patients require care in a major trauma centre is a critical component of developing, evaluating...

Postponing intubation in spontaneously breathing major trauma patients upon emergency room admission does not impair outcome

BackgroundPre-hospital emergency anaesthesia and tracheal intubation are life-saving interventions in trauma patients. However, there is evidence suggesting that the risks associated with both procedures outweigh the benefits. Thus, we assessed whether induction of anaesthesia and tracheal intubation of trauma patients can be postponed in spontaneously breathing patients until emergency room (ER) admission without increasing mortality.MethodsRetrospective analysis of major trauma patients either intubated on-scene by an emergency medical service (EMS) physician (pre-hospital intubation, PHI) or within the first 10 min after admission at a level 1 trauma centre (emergency room intubation, ERI). Data was extracted from the German Trauma Registry, hospital patient data management and electronic clinical information system.ResultsFrom a total of 946 major trauma cases documented between 2010 and 2017, 294 patients matched the study inclusion criteria. Mortality rate of PHI (N = 258) vs. ERI (N = 36) patients was 26.4% vs. 16.7% (p = 0.3). After exclusion of patients with severe traumatic brain injury and/or pre-hospital cardiac arrest, mortality rate of PHI (N = 100) vs. ERI patients (N = 29) was 6% vs. 17.2%, (p = 0.07). Median on-scene time was significantly (p < 0.01) longer in PHI (30 min; IQR: 21–40) vs. ERI patients (20 min; IQR: 15–28).ConclusionsThere was no statistical difference in mortality rates of spontaneously breathing trauma patients intubated on-scene when compared with patients intubated immediately after hospital admission. Due to the retrospective study design and small case number, further studies evaluating the impact of airway management timing in sufficiently breathing trauma patients are warranted.

Trauma Simulation Training Increases Confidence Levels in Prehospital Personnel Performing Life-Saving Interventions in Trauma Patients.

Introduction. Limited evidence is available on simulation training of prehospital care providers, specifically the use of tourniquets and needle decompression. This study focused on whether the confidence level of prehospital personnel performing these skills improved through simulation training. Methods. Prehospital personnel from Alachua County Fire Rescue were enrolled in the study over a 2- to 3-week period based on their availability. Two scenarios were presented to them: a motorcycle crash resulting in a leg amputation requiring a tourniquet and an intoxicated patient with a stab wound, who experienced tension pneumothorax requiring needle decompression. Crews were asked to rate their confidence levels before and after exposure to the scenarios. Timing of the simulation interventions was compared with actual scene times to determine applicability of simulation in measuring the efficiency of prehospital personnel. Results. Results were collected from 129 participants. Pre- and postexposure scores increased by a mean of 1.15 (SD 1.32; 95% CI, 0.88–1.42; P < 0.001). Comparison of actual scene times with simulated scene times yielded a 1.39-fold difference (95% CI, 1.25–1.55) for Scenario 1 and 1.59 times longer for Scenario 2 (95% CI, 1.43–1.77). Conclusion. Simulation training improved prehospital care providers' confidence level in performing two life-saving procedures.

Data quality of a wearable vital signs monitor in the pre-hospital and emergency departments for enhancing prediction of needs for life-saving interventions in trauma patients

This study was designed to investigate the quality of data in the pre-hospital and emergency departments when using a wearable vital signs monitor and examine the efficacy of a combined model of standard vital signs and respective data quality indices (DQIs) for predicting the need for life-saving interventions (LSIs) in trauma patients. It was hypothesised that prediction of needs for LSIs in trauma patients is associated with data quality. Also, a model utilizing vital signs and DQIs to predict the needs for LSIs would be able to outperform models using vital signs alone. Data from 104 pre-hospital trauma patients transported by helicopter were analysed, including means and standard deviations of continuous vital signs, related DQIs and Glasgow coma scale (GCS) scores for LSI and non-LSI patient groups. DQIs involved percentages of valid measurements and mean deviation ratios. Various multivariate logistic regression models for predicting LSI needs were also obtained and compared through receiver-operating characteristic (ROC) curves. Demographics of patients were not statistically different between LSI and non-LSI patient groups. In addition, ROC curves demonstrated better prediction of LSI needs in patients using heart rate and DQIs (area under the curve [AUC] of 0.86) than using heart rate alone (AUC of 0.73). Likewise, ROC curves demonstrated better prediction using heart rate, total GCS score and DQIs (AUC of 0.99) than using heart rate and total GCS score (AUC of 0.92). AUCs were statistically different (p < 0.05). This study showed that data quality could be used in addition to continuous vital signs for predicting the need for LSIs in trauma patients. Importantly, trauma systems should incorporate processes to regulate data quality of physiologic data in the pre-hospital and emergency departments. By doing so, data quality could be improved and lead to better prediction of needs for LSIs in trauma patients.

Improving the prediction of mortality and the need for life-saving interventions in trauma patients using standard vital signs with heart-rate variability and complexity.

The goal of this study was to determine the effectiveness of using traditional and new vital signs (heart rate variability and complexity [HRV, HRC]) for predicting mortality and the need for life-saving interventions (LSIs) in prehospital trauma patients. Our hypothesis was that statistical regression models using traditional and new vital signs would be superior in predictive performance over models using standard vital signs alone. This study involved 108 prehospital trauma patients transported from the point of injury via helicopter. Heart rate variability and HRC were calculated using criterion standard R-R interval sequences manually verified from the patients' electrocardiograms. Means and standard deviations for vital signs, HRV, HRC, and Glasgow coma scale (GCS) scores were obtained for nonsurvivors versus survivors and LSI versus non-LSI patient groups and then compared using Wilcoxon statistical tests. Receiver-operating characteristic curves were also obtained to compare different regression models for predicting mortality and the need for LSIs. Seventeen patients (16%) died. Eighty-two patients (76%) received a total of 142 LSIs. Receiver-operating characteristic curves demonstrated better prediction of mortality and LSI needs using heart rate and HRC (area under the curve [AUC]; AUCs, 0.86 and 0.86) than using heart rate alone (AUCs, 0.79 and 0.57). Likewise, receiver-operating characteristic curves demonstrated better prediction using total GCS score and HRC (AUCs, 0.82 and 0.97) than using total GCS score (AUCs, 0.81 and 0.91). Similar results were obtained for heart rate and HRV (AUCs, 0.86 and 0.73). The major implication of this study was that traditional and new vital signs (HRV and HRC) should be used simultaneously to improve prediction of mortality and the need for LSIs in prehospital trauma patients during all echelons of trauma care. Improvements in the timely use and diagnostic accuracy of transportable vital signs monitors will require use of traditional and new vital signs from the trauma patient cohort.

Sample entropy predicts lifesaving interventions in trauma patients with normal vital signs

IntroductionHeart rate complexity, commonly described as a “new vital sign,” has shown promise in predicting injury severity, but its use in clinical practice is not yet widely adopted. We previously demonstrated the ability of this noninvasive technology to predict lifesaving interventions (LSIs) in trauma patients. This study was conducted to prospectively evaluate the utility of real-time, automated, noninvasive, instantaneous sample entropy (SampEn) analysis to predict the need for an LSI in a trauma alert population presenting with normal vital signs. MethodsProspective enrollment of patients who met criteria for trauma team activation and presented with normal vital signs was conducted at a level I trauma center. High-fidelity electrocardiogram recording was used to calculate SampEn and SD of the normal-to-normal R-R interval (SDNN) continuously in real time for 2 hours with a portable, handheld device. Patients who received an LSI were compared to patients without any intervention (non-LSI). Multivariable analysis was performed to control for differences between the groups. Treating clinicians were blinded to results. ResultsOf 129 patients enrolled, 38 (29%) received 136 LSIs within 24 hours of hospital arrival. Initial systolic blood pressure was similar in both groups. Lifesaving intervention patients had a lower Glasgow Coma Scale. The mean SampEn on presentation was 0.7 (0.4-1.2) in the LSI group compared to 1.5 (1.1-2.0) in the non-LSI group (P < .0001). The area under the curve with initial SampEn alone was 0.73 (95% confidence interval [CI], 0.64-0.81) and increased to 0.93 (95% CI, 0.89-0.98) after adding sedation to the model. Sample entropy of less than 0.8 yields sensitivity, specificity, negative predictive value, and positive predictive value of 58%, 86%, 82%, and 65%, respectively, with an overall accuracy of 76% for predicting an LSI. SD of the normal-to-normal R-R interval had no predictive value. ConclusionsIn trauma patients with normal presenting vital signs, decreased SampEn is an independent predictor of the need for LSI. Real-time SampEn analysis may be a useful adjunct to standard vital signs monitoring. Adoption of real-time, instantaneous SampEn monitoring for trauma patients, especially in resource-constrained environments, should be considered.

Utility of vital signs, heart rate variability and complexity, and machine learning for identifying the need for lifesaving interventions in trauma patients.

To date, no studies have attempted to utilize data from a combination of vital signs, heart rate variability and complexity (HRV, HRC), as well as machine learning (ML), for identifying the need for lifesaving interventions (LSIs) in trauma patients. The objectives of this study were to examine the utility of the above for identifying LSI needs and compare different LSI-associated models, with the hypothesis that an ML model would be superior in performance over multivariate logistic regression models. One hundred four patients transported from the injury scene via helicopter were selected for the study. A wireless vital signs monitor was attached to the patient's arm and used to capture physiologic data, including HRV and HRC. The power of vital sign measurements, HRV, HRC, and Glasgow Coma Scale score (GCS) to identify patients requiring LSIs was estimated using multivariate logistic regression and ML. Receiver operating characteristic (ROC) curves were also obtained. Thirty-two patients underwent 75 LSIs. After logistic regression, ROC curves demonstrated better identification for LSIs using heart rate (HR) and HRC (area under the curve [AUC] of 0.81) than using HR alone (AUC of 0.73). Likewise, ROC curves demonstrated better identification for LSIs using GCS and HRC (AUC of 0.94) than using GCS and HR (AUC of 0.92). Importantly, ROC curves demonstrated that an ML model using HR, GCS, and HRC (AUC of 0.99) had superior performance over multivariate logistic regression models for identifying the need for LSIs in trauma patients. Development of computer decision support systems should utilize vital signs, HRC, and ML in order to achieve more accurate diagnostic capabilities, such as identification of needs for LSIs in trauma patients.

Development and validation of a machine learning algorithm and hybrid system to predict the need for life-saving interventions in trauma patients

Accurate and effective diagnosis of actual injury severity can be problematic in trauma patients. Inherent physiologic compensatory mechanisms may prevent accurate diagnosis and mask true severity in many circumstances. The objective of this project was the development and validation of a multiparameter machine learning algorithm and system capable of predicting the need for life-saving interventions (LSIs) in trauma patients. Statistics based on means, slopes, and maxima of various vital sign measurements corresponding to 79 trauma patient records generated over 110,000 feature sets, which were used to develop, train, and implement the system. Comparisons among several machine learning models proved that a multilayer perceptron would best implement the algorithm in a hybrid system consisting of a machine learning component and basic detection rules. Additionally, 295,994 feature sets from 82h of trauma patient data showed that the system can obtain 89.8% accuracy within 5min of recorded LSIs. Use of machine learning technologies combined with basic detection rules provides a potential approach for accurately assessing the need for LSIs in trauma patients. The performance of this system demonstrates that machine learning technology can be implemented in a real-time fashion and potentially used in a critical care environment.

Prehospital dynamic tissue oxygen saturation response predicts in-hospital lifesaving interventions in trauma patients

Tissue oximetry (StO2) plus a vascular occlusion test is a noninvasive technology that targets indices of oxygen uptake and delivery. We hypothesize that prehospital tissue oximetric values and vascular occlusion test response can predict the need for in-hospital lifesaving interventions (LSI). We conducted a prospective, blinded observational study to evaluate StO2 slopes to predict the need for LSI. We calculated the DeO2 slope using Pearson's coefficients of regression (r2) for the first 25% of descent and the ReO2 slope using the entire recovery interval. The primary outcome was LSI defined as the need for emergent operation or transfusion in the first 24 hours of hospitalization. We created multivariable logistic regression models using covariates of age, sex, vital signs, lactate, and mental status. We assessed StO2 in a convenience sample of 150 trauma patients from April to November of 2009. In-hospital mortality was 3% (95% confidence interval [CI], 1.1-7.6); 31% (95% CI, 24-39) were admitted to the intensive care unit, 6% (95% CI, 2.8-11.1) had an emergent operation, and 10% (95% CI, 5.7-15.9) required transfusion. Decreasing DeO2 was associated with a higher proportion of patients requiring LSI. In the multivariate model, the association between the need for LSI and DeO2, Glasgow Coma Scale, and age persists. Prehospital DeO2 is associated with need for LSI in our trauma population. Further study of DeO2 is warranted to determine whether it can be used as an adjunct triage criterion or an endpoint for resuscitation.

Manual Vital Signs Reliably Predict Need for Life-Saving Interventions in Trauma Patients

Various types of diagnostic and monitoring techniques are available in the prehospital environment. It is unclear how increasing complexity of diagnostic equipment improves the ability to predict the need for a life-saving intervention (LSI). In this study, we determined whether the addition of diagnostic equipment improved the predictive power of vital signs and scores obtained only by physical examination. Institutional review board approval was obtained for an analysis of 793 prehospital trauma patient records collected during helicopter transport by Emergency Medical Services personnel. Exclusion of severe head injuries and patients with incomplete data resulted in 381 patients available for analysis. Data sets were classified on the basis of the instrumentation requirements for capturing the given measurements and were defined by three groups: Group 1, vital signs obtained with no equipment (radial, femoral, and carotid pulse character; capillary refill; motor and verbal components of the Glasgow Coma Scale [GCS]); Group 2, Group 1 plus eye component of the GCS and pulse oximetry (Spo(2)); and Group 3, Group 2 plus fully automated noninvasive blood pressure measurements, heart rate, end-tidal carbon dioxide, and respiratory rate. LSIs performed during transport and in the hospital were recorded. Data were analyzed using a multivariate logistic regression model to determine which vital signs were the best predictors of LSI. Radial pulse character and GCS verbal and motor components had the best predictive power for the need of a prehospital LSI in Group 1 (receiver operating characteristic [ROC] curve, 0.97). Radial pulse character together with the eye component of the GCS and the motor component of the GCS provided the best prediction of a need for a prehospital LSI for Group 2 (ROC curve, 0.97). Addition of all supplementary vital signs measured by an automated monitor (Group 3) resulted in an ROC curve of 0.97. Given an abnormal radial pulse character (weak or absent) and abnormal GCS verbal and motor components, the probability of needing an LSI was greater than 88%. In this cohort of patients, predicting the need for an LSI could have been achieved from GCS motor and verbal components and radial pulse character without automated monitors. These data show that simple and rapidly acquired manual measurements could be used to effectively triage non-head-injured trauma casualties. Similar results were obtained from manual measurements compared with those recorded from automated medical instrumentation that may be unavailable or difficult to use in the field.