Identification of Serious Adverse Events in Patients with Traumatic Brain Injuries, from Prehospital Care to Intensive-Care Unit, Using Early Warning Scores

Traumatic Brain Injury as an Independent Predictor of Futility in the Early Resuscitation of Patients in Hemorrhagic Shock

This review explores the concept of futility timeouts and the use of traumatic brain injury (TBI) as an independent predictor of the futility of resuscitation efforts in severely bleeding trauma patients. The national blood supply shortage has been exacerbated by the lingering influence of the COVID-19 pandemic on the number of blood donors available, as well as by the adoption of balanced hemostatic resuscitation protocols (such as the increasing use of 1:1:1 packed red blood cells, plasma, and platelets) with and without early whole blood resuscitation. This has underscored the urgent need for reliable predictors of futile resuscitation (FR). As a result, clinical, radiologic, and laboratory bedside markers have emerged which can accurately predict FR in patients with severe trauma-induced hemorrhage, such as the Suspension of Transfusion and Other Procedures (STOP) criteria. However, the STOP criteria do not include markers for TBI severity or transfusion cut points despite these patients requiring large quantities of blood components in the STOP criteria validation cohort. Yet, guidelines for neuroprognosticating patients with TBI can require up to 72 h, which makes them less useful in the minutes and hours following initial presentation. We examine the impact of TBI on bleeding trauma patients, with a focus on those with coagulopathies associated with TBI. This review categorizes TBI into isolated TBI (iTBI), hemorrhagic isolated TBI (hiTBI), and polytraumatic TBI (ptTBI). Through an analysis of bedside parameters (such as the proposed STOP criteria), coagulation assays, markers for TBI severity, and transfusion cut points as markers of futilty, we suggest amendments to current guidelines and the development of more precise algorithms that incorporate prognostic indicators of severe TBI as an independent parameter for the early prediction of FR so as to optimize blood product allocation.

Clinical Research in Prehospital Care: Current and Future Challenges

Prehospital care plays a critical role in improving patient outcomes, particularly in cases of time-sensitive emergencies such as trauma, cardiac failure, stroke, bleeding, breathing difficulties, systemic infections, etc. In recent years, there has been a growing interest in clinical research in prehospital care, and several challenges and opportunities have emerged. There is an urgent need to adapt clinical research methodology to a context of prehospital care. At the same time, there are many barriers in prehospital research due to the complex context, posing unique challenges for research, development, and evaluation. Among these, this review allows the highlighting of limited resources and infrastructure, ethical and regulatory considerations, time constraints, privacy, safety concerns, data collection and analysis, selection of a homogeneous study group, etc. The analysis of the literature also highlights solutions such as strong collaboration between emergency medical services (EMS) and hospital care, use of (mobile) health technologies and artificial intelligence, use of standardized protocols and guidelines, etc. Overall, the purpose of this narrative review is to examine the current state of clinical research in prehospital care and identify gaps in knowledge, including the challenges and opportunities for future research.

Modified Rapid Emergency Medicine Score-Lactate (mREMS-L) performance to screen non-anticipated 30-day-related-mortality in emergency department

BACKGROUND

The aim of this study was to compare the ability to predict 30-day in-hospital mortality of lactate versus the modified Rapid Emergency Medicine Score (mREMS) versus the arithmetic sum of the mREMS plus the numerical value of lactate (mREMS-L).

METHODS

A prospective, multicentric, emergency department delivery, pragmatic study was conducted. To determine the predictive capacity of the scales, lactate was measured and the mREMS and mREMS-L were calculated in adult patients (aged>18 years) transferred with high priority by ambulance to the emergency department in five hospitals of Castilla y Leon between 1 January 2020 and 31 December 2021. The area under the receiver operating characteristic (ROC) curve of each of the scales was calculated in terms of mortality for 30 days.

RESULTS

A total of 5371 participants were included, and the in-hospital mortality rate at 30 days was of 11.4% (615 cases). The best cut-off point determined in the mREMS was 7.0 points (sensitivity of 67% and specificity of 84%), and for lactate, the cut-off point was 1.4 mmol/L (sensitivity of 88% and specificity of 67%). Finally, the combined mREMS-L showed a cut-off point of 7.9 (sensitivity of 83% and a specificity of 83%). The area under the ROC curve of the mREMS, lactate and mREMS-L for 30-day mortality was 0.851, 0.853, and 0.903, respectively (p < 0.001 in all cases).

CONCLUSIONS

The new score generated, mREMS-L, obtained better statistical results than its components (mREMS and lactate) separately.

The accuracy of the National Early Warning Score 2 in predicting early death in prehospital and emergency department settings: a systematic review and meta-analysis

Background

Many studies have explored the accuracy of the National Early Warning Score 2 (NEWS2) in predicting mortality in prehospital and emergency settings, but their findings are inconsistent. Whether NEWS2 is reliable for the pre-examination and triage of patients in prehospital settings and emergency departments remains debatable. Hence, this study aimed to evaluate the accuracy of NEWS2 in predicting mortality in prehospital settings and emergency departments.

Methods

We searched PubMed, Embase, Cochrane Library, Web of Science, CNKI, Wan Fang Data, Vip Database and SinoMed from the inception of each database to January 2023. The inclusion criteria: (I) patients in the prehospital settings or emergency departments; (II) the NEWS2 for predicting 2-day mortality, 30-day mortality, and in-hospital mortality; (III) sufficient data, such as sensitivity, specificity, overall survival, and deaths, were provided for the study; (IV) the type of study was accuracy prediction study. Two authors independently extracted data, including authors, year of publication, country of origin, study design, sample size, threshold cutoff values of NEWS2, and mortality. The PROBAST was used to assess the risk of bias in the included studies.

Results

Thirty studies with 185,835 participants were included. Among the 30 included studies, 13 have a high risk of bias, and 17 have a low risk of bias. The pooled sensitivity, specificity and AUC of 2-day mortality (early mortality), 30-day mortality and in-hospital mortality were 0.81 vs. 0.76 vs. 0.72 (95% CI: 0.61, 0.80), 0.81 vs. 0.69 vs. 0.78 (95% CI: 0.49, 0.93) and 0.88 vs. 0.80 vs. 0.78 (95% CI: 0.74, 0.82), respectively.

Conclusions

NEWS2 has excellent sensitivity and specificity in predicting early mortality in patients in the prehospitals setting and emergency departments. Nonetheless, it has poor performance in predicting in-hospital mortality and 30-day mortality. Our findings underpin the use of NEWS2 as a pre-examination and triage tool to predict early death in the prehospital settings and emergency departments. To improve the predictive accuracy, it should be used to monitor patients continuously rather than at a single point-in-time.

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.

Performance of the Modified Rapid Emergency Medicine Score in Patients With and Without Traumatic Brain Injury

ABSTRACT

BACKGROUND: The modified Rapid Emergency Medicine Score (mREMS) is a recently published index to estimate the severity of trauma patients; however, little is known about its performance in patients with different types of trauma. This study verified the predictive capacity of mREMS in-hospital mortality in patients of blunt and penetrating trauma with and without traumatic brain injury (TBI) and the performance of this index compared with the Rapid Emergency Medicine Score, Injury Severity Score, New Injury Severity Score, and Trauma and Injury Severity Score. METHODS: This is a retrospective, correlational study that analyzed trauma patients 18 years or older, who attended at a hospital in Rio de Janeiro, Brazil. The receiver operating characteristic (ROC) curve was applied in the analyses. RESULTS: The sample consisted of 987 patients, 359 (36.4%) with TBI (225 blunt and 134 penetrating trauma). Regarding mREMS, the area under the ROC curve for TBI patients for in-hospital mortality was 0.506 (95% confidence interval [CI], 0.404-0.609) for penetrating injuries and 0.486 (95% CI, 0.402-0.571) for blunt injuries; the values in patients without TBI were 0.629 (95% CI, 0.554-0.703) and 0.618 (95% CI, 0.552-0.684), respectively. In relation to the other indices the mREMS presented the lowest area under the curve/ROC for penetrating and blunt TBI, and the Rapid Emergency Medicine Score for extracranial injuries. CONCLUSION: The mREMS showed no prognostic capacity for patients with TBI, and it presented the worst performance in relation to the Injury Severity Score, New Injury Severity Score, and Trauma and Injury Severity Score to discriminate cases of in-hospital mortality when considering trauma patients with and without TBI.

Comparison of Nine Early Warning Scores for Identification of Short-Term Mortality in Acute Neurological Disease in Emergency Department

(1) Background: The aim was screening the performance of nine Early Warning Scores (EWS), to identify patients at high-risk of premature impairment and to detect intensive care unit (ICU) admissions, as well as to track the 2-, 7-, 14-, and 28-day mortality in a cohort of patients diagnosed with an acute neurological condition. (2) Methods: We conducted a prospective, longitudinal, observational study, calculating the EWS [Modified Early Warning Score (MEWS), National Early Warning Score (NEWS), VitalPAC Early Warning Score (ViEWS), Modified Rapid Emergency Medicine Score (MREMS), Early Warning Score (EWS), Hamilton Early Warning Score (HEWS), Standardised Early Warning Score (SEWS), WHO Prognostic Scored System (WPSS), and Rapid Acute Physiology Score (RAPS)] upon the arrival of patients to the emergency department. (3) Results: In all, 1160 patients were included: 808 patients were hospitalized, 199 cases (17%) required ICU care, and 6% of patients died (64 cases) within 2 days, which rose to 16% (183 cases) within 28 days. The highest area under the curve for predicting the need for ICU admissions was obtained by RAPS and MEWS. For predicting mortality, MREMS obtained the best scores for 2- and 28-day mortality. (4) Conclusions: This is the first study to explore whether several EWS accurately identify the risk of ICU admissions and mortality, at different time points, in patients with acute neurological disorders. Every score analyzed obtained good results, but it is suggested that the use of RAPS, MEWS, and MREMS should be preferred in the acute setting, for patients with neurological impairment.

Early detection of intensive care needs and mortality risk by use of five early warning scores in patients with traumatic injuries: An observational study

OBJECTIVE

The purpose of this research was to evaluate the predictive capacity of five Early Warning Scores in relation to the clinical evolution of adult patients with different types of trauma.

RESEARCH METHODOLOGY

We conducted a longitudinal, prospective, observational study, calculating the Early Warning Scores [Modified Early Warning Score (MEWS), National Early Warning Score 2 (NEWS-2), VitalPAC Early Warning Score (ViEWS), Modified Rapid Emergency Medicine Score (MREMS), and Rapid Acute Physiology Score (RAPS)] upon arrival of patients to the emergency department.

SETTING

In total, 445 cases of traumatic injuries were included in the study.

MAIN OUTCOME MEASURES

The predictive capacity was verified with the data on admission to intensive care units (ICU) and mortality at two, seven and 30 days.

RESULTS

201 patients were hospitalized and 244 were discharged after being attended in the emergency department. 91 cases (20.4%) required ICU care and 4.7% of patients died (21 patients) within two days, 6.5% (29 patients) within seven days and 9.7% (43 patients) within 30 days. The highest area under the curve for predicting the need for ICU care was obtained by the National Early Warning Score 2 and the VitalPAC Early Warning Score. For predicting mortality, the Modified Rapid Emergency Medicine Score obtained the best scores for two-day mortality, seven-day mortality and 30-day mortality.

CONCLUSIONS

Every Early Warning Score analyzed in this study obtained good results in predicting adverse effects in adult patients with traumatic injuries, creating an opportunity for new clinical applications in the emergency department.

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.

Comparison of the Performance of 24 Early Warning Scores with the Updated National Early Warning Score (NEWS2) for Predicting Unplanned Intensive Care Unit (ICU) Admission in Postoperative Brain Tumor Patients: A Retrospective Study at a Single Center

BACKGROUND There have been few studies to evaluate early warning score (EWS) systems, or track and trigger systems (TTS), to identify early clinical deterioration in patients following brain tumor surgery who are admitted to the Intensive Care Unit (ICU). The National Early Warning Score (NEWS2) is an established method used in the U.K. National Health Service to improve care for in-hospital patients. This retrospective study from a single center aimed to compare the performance of NEWS2 with 24 other types of EWS to evaluate unplanned ICU admissions within 72 h after brain tumor surgery. MATERIAL AND METHODS A total of 326 patients with brain tumors were included in the study. Patients who experienced unplanned ICU transfer after surgery (69 cases) were diagnostically matched with patients who did not require intensive care (257 controls). We collected the physiological variables to calculate the area under the receiver operator characteristic curve (AUROC), sensitivity, specificity, Youden index values, cutoff values, positive predictive values, and negative predictive values. RESULTS The NEWS2 identified postoperative brain tumor patients with AUROC (0.860, p=0.000). The Patient-At-Risk (PAR) score was higher than NEWS2 in terms of AUROC value (0.870, P=0.000), Youden index (0.589 vs 0.542). CONCLUSIONS The findings showed that although the NEWS 2 performed well when used to evaluate unplanned ICU admissions within 72 h of postoperative brain tumor patients, the PAR score was also an accurate EWS.