Body Temperature after EMS Transport: Association with Traumatic Brain Injury Outcomes

Burden of fever and hospital mortality in patients admitted to the intensive care unit with isolated traumatic brain injury-A retrospective cohort study using continuous temperature data

BACKGROUND

Fever has been shown to be associated with poor outcomes in patients with traumatic brain injury. Earlier studies have used peak daily temperature to derive the burden of fever. The association between hospital mortality and fever burden calculated as the area under the temperature-time curve for the entire duration of intensive care unit (ICU) stay has not been studied before.

OBJECTIVES

The objective of this study was to investigate the association between the burden of fever and hospital mortality in patients with isolated traumatic brain injury admitted to the ICU.

METHODS

We conducted this retrospective cohort study using an electronic database in a tertiary ICU in Sydney. We included all adult patients admitted to the ICU with isolated traumatic brain injury over 3 years from 1 July 2017 to 30 June 2020. We collected data on demographics, clinical characteristics, and interventions for all patients. We defined the burden of fever as an area under the temperature-time curve above 37 °C. The primary outcome was hospital mortality. We used multivariable logistic regression to determine the association between burden of fever and hospital mortality. We assessed the importance of the burden of fever in a predictive model using machine-learning methods (Bagging and Random Forest).

RESULTS

A total of 88 patients (76% males, mean age: 54 ± 23 years, mean Acute Physiology and Chronic Health Evaluation [APACHE] II score: 15 ± 7) were included in the study, and 18 (20.5%) of the 88 patients died in hospital. Compared to survivors, the nonsurvivors had lower mean Glasgow Coma Scale (GCS) score at the scene, higher mean APACHE II and III scores, and higher rates of intracranial pressure monitoring, surgery, mechanical ventilation, use of vasopressors, and cooling. On multivariable logistic regression, age (odds ratio: 1.05, 95% confidence interval: 1.02-1.09, p = 0.01) was found to be an independent predictor of hospital mortality. A higher GCS score at the scene (odds ratio: 0.81, 95% confidence interval: 0.66-0.98, p = 0.03) was associated with survival. The burden of fever was not associated with hospital mortality. The top three important variables in the predictive model were APACHE III, GCS score at scene, and age.

CONCLUSION

The burden of fever was not an independent predictor of hospital mortality. The results of this study need to be confirmed in a large multicenter study.

Systematic Literature Review of the Association of Fever and Elevated Temperature with Outcomes in Critically Ill Adult Patients

Although most commonly associated with infection, elevated temperature and fever also occur in a variety of critically ill populations. Prior studies have suggested that fever and elevated temperature may be detrimental to critically ill patients and can lead to poor outcomes, but the evidence surrounding the association of fever with outcomes is rapidly evolving. To broadly assess potential associations of elevated temperature and fever with outcomes in critically ill adult patients, we performed a systematic literature review focusing on traumatic brain injury, stroke (ischemic and hemorrhagic), cardiac arrest, sepsis, and general intensive care unit (ICU) patients. Searches were conducted in Embase® and PubMed® from 2016 to 2021, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, including dual-screening of abstracts, full texts, and extracted data. In total, 60 studies assessing traumatic brain injury and stroke (24), cardiac arrest (8), sepsis (22), and general ICU (6) patients were included. Mortality, functional, or neurological status and length of stay were the most frequently reported outcomes. Elevated temperature and fever were associated with poor clinical outcomes in patients with traumatic brain injury, stroke, and cardiac arrest but not in patients with sepsis. Although a causal relationship between elevated temperature and poor outcomes cannot be definitively established, the association observed in this systematic literature review supports the concept that management of elevated temperature may factor in avoidance of detrimental outcomes in multiple critically ill populations. The analysis also highlights gaps in our understanding of fever and elevated temperature in critically ill adult patients.

Guidelines for Prehospital Management of Traumatic Brain Injury 3rd Edition: Executive Summary

Prehospital care markedly influences outcome from traumatic brain injury, yet it remains highly variable. The Brain Trauma Foundation's guidelines informing prehospital care, first published in 2002, have sought to identify and disseminate best practices. Many of its recommendations relate to the management of airway, breathing and circulation, and infrastructure for this care. Compliance with the second edition of these guidelines has been associated with significantly improved survival. A working group developed evidence-based recommendations informing assessment, treatment, and transport decision-making relevant to the prehospital care of brain injured patients. A literature search spanning May 2005 to January 2022 supplemented data contained in the 2nd edition. Identified studies were assessed for quality and used to inform evidence-based recommendations. A total of 122 published articles formed the evidentiary base for this guideline update including 5 providing Class I evidence, 35 providing Class II evidence, and 98 providing Class III evidence for the various topics. Forty evidence-based recommendations were generated, 30 of which were strong and 10 of which were weak. In many cases, new evidence allowed guidelines from the 2nd edition to be strengthened. Development of guidelines on some new topics was possible including the prehospital administration of tranexamic acid. A management algorithm is also presented. These guidelines help to identify best practices for prehospital traumatic brain injury care, and they also identify gaps in knowledge which we hope will be addressed before the next edition.

Prehospital Active and Passive Warming in Trauma Patients

OBJECTIVE

Hypothermia is common among trauma patients and can lead to a serious rise in morbidity and mortality. This study was performed to investigate the effect of active and passive warming measures implemented in the prehospital phase on the body temperature of trauma patients.

METHODS

In a multicenter, multinational prospective observational design, the effect of active and passive warming measures on the incidence of hypothermia was investigated. Adult trauma patients who were transported by helicopter emergency medical services (HEMS) or ground emergency medical services with an HEMS physician directly from the scene of injury were included. Four HEMS/ground emergency medical services programs from Canada, the United States, and the Netherlands participated.

RESULTS

A total of 80 patients (n = 20 per site) were included. Eleven percent had hypothermia on presentation, and the initial evaluation occurred predominantly within 60 minutes after injury. In-line fluid warmers and blankets were the most frequently used active and passive warming measures, respectively. Independent risk factors for a negative change in body temperature were transportation by ground ambulance (odds ratio = 3.20; 95% confidence interval, 1.06-11.49; P = .03) and being wet on initial presentation (odds ratio = 3.64; 95% confidence interval, 0.99-13.36; P = .05).

CONCLUSION

For adult patients transported from the scene of injury to a trauma center, active and passive warming measures, most notably the removal of wet clothing, were associated with a favorable outcome, whereas wet patients and ground ambulance transport were associated with an unfavorable outcome with respect to temperature.

Rapid prediction of secondary neurologic decline after traumatic brain injury: a data analytic approach

Secondary neurologic decline (ND) after traumatic brain injury (TBI) is independently associated with outcome, but robust predictors of ND are lacking. In this retrospective analysis of consecutive isolated TBI admissions to the R. Adams Cowley Shock Trauma Center between November 2015 and June 2018, we aimed to develop a triage decision support tool to quantify risk for early ND. Three machine learning models based on clinical, physiologic, or combined characteristics from the first hour of hospital resuscitation were created. Among 905 TBI cases, 165 (18%) experienced one or more ND events (130 clinical, 51 neurosurgical, and 54 radiographic) within 48 h of presentation. In the prediction of ND, the clinical plus physiologic data model performed similarly to the physiologic only model, with concordance indices of 0.85 (0.824-0.877) and 0.84 (0.812-0.868), respectively. Both outperformed the clinical only model, which had a concordance index of 0.72 (0.688-0.759). This preliminary work suggests that a data-driven approach utilizing physiologic and basic clinical data from the first hour of resuscitation after TBI has the potential to serve as a decision support tool for clinicians seeking to identify patients at high or low risk for ND.

Prediction of Mortality in Geriatric Traumatic Brain Injury Patients Using Machine Learning Algorithms

Background: The number of geriatric traumatic brain injury (TBI) patients is increasing every year due to the population’s aging in most of the developed countries. Unfortunately, there is no widely recognized tool for specifically evaluating the prognosis of geriatric TBI patients. We designed this study to compare the prognostic value of different machine learning algorithm-based predictive models for geriatric TBI. Methods: TBI patients aged ≥65 from the Medical Information Mart for Intensive Care-III (MIMIC-III) database were eligible for this study. To develop and validate machine learning algorithm-based prognostic models, included patients were divided into a training set and a testing set, with a ratio of 7:3. The predictive value of different machine learning based models was evaluated by calculating the area under the receiver operating characteristic curve, sensitivity, specificity, accuracy and F score. Results: A total of 1123 geriatric TBI patients were included, with a mortality of 24.8%. Non-survivors had higher age (82.2 vs. 80.7, p = 0.010) and lower Glasgow Coma Scale (14 vs. 7, p < 0.001) than survivors. The rate of mechanical ventilation was significantly higher (67.6% vs. 25.9%, p < 0.001) in non-survivors while the rate of neurosurgical operation did not differ between survivors and non-survivors (24.3% vs. 23.0%, p = 0.735). Among different machine learning algorithms, Adaboost (AUC: 0.799) and Random Forest (AUC: 0.795) performed slightly better than the logistic regression (AUC: 0.792) on predicting mortality in geriatric TBI patients in the testing set. Conclusion: Adaboost, Random Forest and logistic regression all performed well in predicting mortality of geriatric TBI patients. Prognostication tools utilizing these algorithms are helpful for physicians to evaluate the risk of poor outcomes in geriatric TBI patients and adopt personalized therapeutic options for them.

Pre-hospital mild therapeutic hypothermia for patients with severe traumatic brain injury

BACKGROUND

We aimed to assess the effects of pre-hospital mild therapeutic hypothermia (MTH) on patients with severe traumatic brain injury (sTBI).

METHODS

Eighty-six patients with sTBI were prospectively enrolled into the pre-hospital MTH group and the late MTH group (initiated in hospital). Patients in the pre-hospital MTH group were maintained at a tympanic temperature of 33°C-35°C before admission and continued to be treated with a therapeutic hypothermia device for 4 days. Patients in the late MTH group were treated with the same MTH parameters. Intracranial pressure (ICP), complications and Glasgow Outcome Scale (GOS) scores were monitored.

RESULTS

ICP was significantly lower for patients in the pre-hospital MTH group 24, 48, and 72 h after treatment (17.38 ± 4.88 mmHg, 18.40 ± 4.50 mmHg, and 16.40 ± 4.13 mmHg, respectively) than that in the late MTH group (20.63 ± 3.00 mmHg, 21.80 ± 6.00 mmHg, and 18.81 ± 4.50 mmHg) (P < .05). The favorable prognosis (GOS scores 4-5) rate in the pre-hospital MTH group was higher tha n the late MTH group (65.1% vs. 37.2%, respectively; P < .05) without complications .

CONCLUSION

Pre-hospital MTH for patients with STBI can reduce ICP and improve neurological outcomes.

Review of wearable technologies and machine learning methodologies for systematic detection of mild traumatic brain injuries

Mild traumatic brain injuries (mTBIs) are the most common type of brain injury. Timely diagnosis of mTBI is crucial in making 'go/no-go' decision in order to prevent repeated injury, avoid strenuous activities which may prolong recovery, and assure capabilities of high-level performance of the subject. If undiagnosed, mTBI may lead to various short- and long-term abnormalities, which include, but are not limited to impaired cognitive function, fatigue, depression, irritability, and headaches. Existing screening and diagnostic tools to detect acute andearly-stagemTBIs have insufficient sensitivity and specificity. This results in uncertainty in clinical decision-making regarding diagnosis and returning to activity or requiring further medical treatment. Therefore, it is important to identify relevant physiological biomarkers that can be integrated into a mutually complementary set and provide a combination of data modalities for improved on-site diagnostic sensitivity of mTBI. In recent years, the processing power, signal fidelity, and the number of recording channels and modalities of wearable healthcare devices have improved tremendously and generated an enormous amount of data. During the same period, there have been incredible advances in machine learning tools and data processing methodologies. These achievements are enabling clinicians and engineers to develop and implement multiparametric high-precision diagnostic tools for mTBI. In this review, we first assess clinical challenges in the diagnosis of acute mTBI, and then consider recording modalities and hardware implementation of various sensing technologies used to assess physiological biomarkers that may be related to mTBI. Finally, we discuss the state of the art in machine learning-based detection of mTBI and consider how a more diverse list of quantitative physiological biomarker features may improve current data-driven approaches in providing mTBI patients timely diagnosis and treatment.

Performance of Modified Early Warning Score (MEWS) for Predicting In-Hospital Mortality in Traumatic Brain Injury Patients

The present study aimed to analyze and compare the prognostic performances of the Revised Trauma Score (RTS), Injury Severity Score (ISS), Shock Index (SI), and Modified Early Warning Score (MEWS) for in-hospital mortality in patients with traumatic brain injury (TBI). This retrospective observational study included severe trauma patients with TBI who visited the emergency department between January 2018 and December 2020. TBI was considered when the Abbreviated Injury Scale was 3 or higher. The primary outcome was in-hospital mortality. In total, 1108 patients were included, and the in-hospital mortality was 183 patients (16.3% of the cohort). Receiver operating characteristic curve analyses were performed for the ISS, RTS, SI, and MEWS with respect to the prediction of in-hospital mortality. The area under the curves (AUCs) of the ISS, RTS, SI, and MEWS were 0.638 (95% confidence interval (CI), 0.603–0.672), 0.742 (95% CI, 0.709–0.772), 0.524 (95% CI, 0.489–0.560), and 0.799 (95% CI, 0.769–0.827), respectively. The AUC of MEWS was significantly different from the AUCs of ISS, RTS, and SI. In multivariate analysis, age (odds ratio (OR), 1.012; 95% CI, 1.000–1.023), the ISS (OR, 1.040; 95% CI, 1.013–1.069), the Glasgow Coma Scale (GCS) score (OR, 0.793; 95% CI, 0.761–0.826), and body temperature (BT) (OR, 0.465; 95% CI, 0.329–0.655) were independently associated with in-hospital mortality after adjustment for confounders. In the present study, the MEWS showed fair performance for predicting in-hospital mortality in patients with TBI. The GCS score and BT seemed to have a significant role in the discrimination ability of the MEWS. The MEWS may be a useful tool for predicting in-hospital mortality in patients with TBI.

Temperature management in acute brain injury: A systematic review of clinical evidence

Temperature alterations in neurocritical care settings are common and have a striking effect on brain metabolism leading to or exacerbating neuronal injury. Hyperthermia worsens acute brain injury (ABI) patients outcome. However conclusive evidence linking control of temperature to improved outcome is still lacking. This review article report an update -results from clinical studies published between March 2006 and March 2020- on the relationship between hyperthermia or Target Temperature Management and functional outcome or mortality in ABI patients.

MATERIALS AND METHODS

A systematic search of articles in PubMed and EMBASE database was accomplished. Only complete studies, published in English in peer-reviewed journals were included.

RESULTS

A total of 63 articles into 5 subchapters are presented: acute ischemic stroke (17), subarachnoid hemorrhage (14), brain trauma (14), intracranial hemorrhage (8), and mixed acute brain injury (10). This evidence confirm and extend the negative impact of hyperthermia in ABI patients on worse functional outcome and higher mortality. In particular "early hyperthermia" in AIS patients seems to have a protective role have as promoting factor of clot lysis but no conclusive evidence is available. Normothermic TTM seems to have a positive effect on TBI patients in a reduced mortality rate compared to hypothermic TTM.

CONCLUSIONS

Hyperthermia in ABI patients is associated with worse functional outcome and higher mortality. The use of normothermic TTM has an established indication only in TBI; further studies are needed to define the role and the indications of normothermic TTM in ABI patients.

Predictors of Hypothermia upon Trauma Center Arrival in Severe Trauma Patients Transported to Hospital via EMS

Introduction: Hypothermia in severe trauma patients can increase mortality by 25%. Active warming practices decrease mortality and are recommended in the Advanced Trauma Life Support (ATLS) guidelines. Despite this, many emergency medical services (EMS) vehicles do not carry equipment necessary to perform active warming. The intent of this study was to determine the rate of hypothermia in severe trauma patients upon major trauma center (MTC) arrival, as well as to characterize factors associated with hypothermia in trauma in order to devote potential resources to those at highest risk. Methods: This single-center retrospective chart review included adults (age ≥ 18) in the local trauma registry (trauma team activation or injury severity score ≥12) from January 2009 to June 2016. Logistic regression was used to identify predictors of hypothermia on MTC arrival. Results: A total of 3,070 patient charts were reviewed, of which 159 (5.2%) were hypothermic. Multivariate logistic regression identified 7 factors that were significantly associated with hypothermia on MTC arrival in severe trauma. Risk factors for hypothermia on MTC arrival after severe trauma included: intubation pre-MTC, increased number of co-morbidities, and increased injury severity. Conversely, protective factors against hypothermia were: higher initial systolic blood pressure (SBP), penetrating injury, referral to MTC, and higher ambient outdoor temperatures. Median length of stay in hospital was 7 days for hypothermic patients compared to 4 days for normothermic patients (Δ 3 days; p < 0.001). Only 69.2% of hypothermic patients survived to discharge compared to 93.9% of normothermic patients (Δ 24.7%; χ² = 133.4, p < 0.001). Conclusions: This retrospective study of hypothermia in major trauma patients found a rate of hypothermia of 5%. Factors associated with higher risk of hypothermia include pre-MTC intubation, high ISS, multiple comorbidities, low SBP, non-penetrating mechanism of injury, and being transferred directly to MTC, and colder outdoor temperature. Avoidance of hypothermia is imperative to the management of major trauma patients. Prospective studies are required to determine if prehospital warming in these high-risk patients decreases the rate of hypothermia in major trauma and improves patient outcomes.

Pediatric Major Head Injury: Not a Minor Problem

Traumatic brain injury is a highly prevalent and devastating cause of morbidity and mortality in children. A rapid, stepwise approach to the traumatized child should proceed, addressing life-threatening problems first. Management focuses on preventing secondary injury from physiologic extremes such as hypoxemia, hypotension, prolonged hyperventilation, temperature extremes, and rapid changes in cerebral blood flow. Initial Glasgow Coma Score, hyperglycemia, and imaging are often prognostic of outcome. Surgically amenable lesions should be evacuated promptly. Reduction of intracranial pressure through hyperosmolar therapy, decompressive craniotomy, and seizure prophylaxis may be considered after stabilization. Nonaccidental trauma should be considered when evaluating pediatric trauma patients.