Prognostic value of FOUR and GCS scores in determining mortality in patients with traumatic brain injury

Predicting hospital outcomes in concussion and TBI: A mixed-effects analysis utilizing the nationwide readmissions database

BACKGROUND AND OBJECTIVES

Traumatic brain injury (TBI) is characterized by a wide range in severity. This variation presents a challenge for predicting outcomes and making management decisions, particularly for patients sustaining less severe injury. We present a novel statistical model for the prediction of hospital outcomes in two propensity-matched cohorts to optimize TBI patient management and counseling.

METHODS

Hospitalized patients diagnosed with TBI were selected from the Nationwide Readmissions Database (NRD) from 2010 to 2019 using ICD-9 and ICD-10 codes. Using propensity score matching for baseline characteristics, patients were sorted by GCS score into two cohorts: 1188 patients with mild to moderate TBI (mTBI, GCS > 8) and 1219 patients with severe TBI (sTBI, GCS ≤ 8). Mixed-effects modeling was implemented, and model performance was evaluated using the Area Under the Curve (AUC). Any variance in ROC model prediction between cohorts was compared using DeLong's test.

RESULTS

After bivariate analysis, the mean length of stay (LOS), hospital cost, and mortality were significantly lower in the mTBI cohort relative to sTBI. GCS scores within the range of 9-15 were predictive of LOS (p < 0.01), with a trend towards significance in the prediction of non-routine discharge (p = 0.06).

CONCLUSION

Using an advanced mixed-effects model, our study found that GCS is an accurate predictor of hospital outcomes after a TBI diagnosis. These results provide insight that may aid in the development of preventative strategies, management decisions, and patient counseling to ensure a safe return to daily life for patients diagnosed with concussion.

Developing practical machine learning survival models to identify high-risk patients for in-hospital mortality following traumatic brain injury

Machine learning (ML) offers precise predictions and could improve patient care, potentially replacing traditional scoring systems. A retrospective study at Emtiaz Hospital analyzed 3,180 traumatic brain injury (TBI) patients. Nineteen variables were assessed using ML algorithms to predict outcomes. Data preparation addressed missing values and balancing methods corrected imbalances. Model building involved training-test splits, survival analysis, and ML algorithms like Random Survival Forest (RSF) and Gradient Boosting. Feature importance was examined, with patient risk stratification guiding survival analysis. The best-performing model, RSF with ROS resampling, achieved the highest mean AUC of 0.80, the lowest IBS of 0.11, and IPCW c-index of 0.79, maintaining strong predictive ability over time. Top predictors for in-hospital mortality included age, GCS, pupil condition, PTT, IPH, and Rotterdam score, with high variations in predictive abilities over time. A risk stratification cut-off value of 63.34 separated patients into low and high-risk categories, with Kaplan-Meier curves showing significant survival differences. Our high-performing predictive model, built on first-day features, enables time-dependent risk assessment for tailored interventions and monitoring. Our study highlights the feasibility of AI tools in clinical settings, offering superior predictive accuracy and enhancing patient care for TBI cases.

Predictors of mortality for blunt trauma patients in intensive care: A retrospective cohort study

Background

Major trauma places substantial demand on critical care services, is a leading cause of death in under 40-year-olds and causes significant morbidity and mortality across all age groups. Various factors influence patient outcome and predefining these could allow prognostication. The aim of this study was to identify predictors of mortality from major trauma in intensive care.

Methods

This was a retrospective study of adult trauma patients admitted to general intensive care between January 2018 and December 2019. We assessed the impact on mortality of patient demographics, patterns of injury, injury scores (Glasgow Coma Score (GCS), Charlson's comorbidity index (CCI), Acute Physiology and Health Evaluation II (APACHE II), Injury Severity Score (ISS) and Probability of Survival Score (Ps19)), number of surgeries and mechanism of injury using logistic regression.

Results

A total of 414 patients were included with a median age of 54 years (IQR 34-72). Overall mortality was 18.6%. The most common mechanism of injury was traffic collision (46%). Non-survivors were older, had higher ISS scores with lower GCS on admission and lower probability of survival scores. Factors independently predictive of mortality were age 70-80 (OR 3.267, p = 0.029), age >80 (OR 27.043, p < 0.001) and GCS < 15 (OR 8.728, p < 0.001). Ps19 was the best predictor of mortality (p <0.001 for each score category), with an AUROC of 0.90.

Conclusions

The significant mortality predictors were age, fall from <2 metres, injury of head or limbs, GCS <15 and Ps19. Contrary to previous studies, CCI and APACHE II did not significantly predict mortality. Although Ps19 was found to be the best current prognostic score, trauma prognostication would benefit from a single validated scoring system incorporating both physiological variables and injury patterns.

Copeptin's role in traumatic brain injury: The promising quest for a new biomarker

OBJECTIVE

Traumatic brain injury (TBI) necessitates reliable biomarkers to improve patient care. This study explored copeptin as a potential biomarker in TBI and its relation to vasopressin (ADH) in such patients.

METHODS

A cross-sectional study was conducted on 50 TBI patients. Exclusion criteria included specific medical conditions and recent traumatic events. Copeptin and ADH testing were performed within 30 days post-trauma. Patient data, Glasgow Coma Scale (GCS) scores, imaging results, and the need for surgical intervention were obtained from medical charts.

RESULTS

Copeptin levels negatively correlated with GCS scores (ρ = - 0.313, p = 0.027), indicating a potential association with trauma severity. Copeptin levels (mean: 3.22 pmol/L, median 2.027 pmol/L, SD = 3.15) tended to be lower than those found in the normal population, suggesting possible neuroendocrine dysfunction post-TBI. ADH levels (mean: 67.93 pmol/L, median 56.474 pmol/L SD = 47.67) were higher than the normal range and associated with the need for surgery (p = 0.048). Surprisingly, copeptin and ADH levels negatively correlated (r = - 0.491; p < 0.001), potentially due to differences in degradation processes and physiological variations in TBI patients.

CONCLUSION

Copeptin shows potential as a predictive biomarker for assessing TBI severity and predicting patient outcome. However, its complex relationship with ADH in TBI requires further investigation. Careful interpretation is needed due to potential variations in excretion dynamics and metabolism. Larger studies on TBI patient cohorts are essential to validate copeptin as a reliable biomarker and improve patient care in TBI.

Analysis of Relevant Predictive Indicators for Postoperative Condition Change in Brain Tumor Patients

BACKGROUND

Novice nurse need more guidance and professional confidence. This study aimed to explore early relevant predictive indicators for postoperative condition changes in brain tumor patients, which can be used to map patients' condition changes for novice nurses.

METHODS

The authors prospectively collected clinical data, including nursing records of cerebral tumor patients after operation from July 2020 to June 2021 in the Department of Neurosurgery. Univariant analysis and multivariable logistic regression analysis were performed to identify independent predictors, the satisfaction questionnaire for teaching nurses is used to evaluate the new nurse's performance during perioperative period, psychometric scale is used to assess nurses' psychological stress.

RESULTS

Nine hundred ninety-five cerebral tumor patients were enrolled in this study, and 115 (11.6%) patients' conditions were changed. Relevant predictive indicators, including vision, myodynamia, urine volume, nausea, vomit, pain, Glasgow Coma Scale (GCS), and Modified Early Warning System (MEWS), among them GCS, MEWS, and vision can basically cover the above indicators through simulation and calculation of the integrated model was set as Z=22*vision+1*GCS+3*MEWS. The satisfaction rate of teaching nurses has increased from 78.125 to 86.25%. Novice nurses' psychological stress was dropped from 30.637% to 19.844%.

CONCLUSIONS

GCS and MEWS. GCS, MEWS and vision can map patients' condition change after cerebral tumor operation. The warning model has reduced their psychological stress, and the teaching nurse expressed that the effective evaluation of postoperative patient conditions by novice nurses reduces their nursing and teaching work greatly.

Comparison of the Prognostic Accuracy of Full Outline of Unresponsiveness (FOUR) Score with Glasgow Coma Scale (GCS) Score among Patients with Traumatic Brain Injury in a Tertiary Care Center

Objectives  The Glasgow Coma Scale (GCS) is widely used and considered the gold standard in assessing the consciousness of patients with traumatic brain injury. However, some significant limitations, like the considerable variations in interobserver reliability and predictive validity, were the reason for developing the Full Outline of Unresponsiveness (FOUR) score. The current study aims to compare the prognostic accuracy of the FOUR score with the GCS score for in-hospital mortality and morbidity among patients with traumatic brain injury. Materials and Methods  A prospective cohort study was conducted, where 237 participants were selected by consecutive sampling from a tertiary care center. These patients were assessed with the help of GCS and FOUR scores within 6 hours of admission, and other clinical parameters were also noted. The level of consciousness was checked every day with the help of GCS and FOUR scores until their last hospitalization day. Glasgow Outcome Scale was used to assess their outcome on the last day of hospitalization. The GCS and FOUR scores were compared, and data were analyzed by descriptive and inferential statistics. The chi-square test, independent Student's t -test, and receiver operating characteristic analysis were used for inferential analysis. Results  The area under the curve (AUC) for the GCS score at the 6th hour for predicting mortality was 0.865 with a cutoff value of 5.5, and it yields a sensitivity of 87% and a specificity of 64%. The AUC for FOUR scores at the 6th hour for predicting the mortality was 0.893, with a cutoff value of 5.5, and it yields a sensitivity of 87% and a specificity of 73%. Conclusion  The current study shows that, as per the AUC of GCS and FOUR scores, their sensitivity was equal, but specificity was higher in the FOUR score. So, the FOUR score has higher accuracy than the GCS score in the prediction of mortality among traumatic brain injury patients.

Acute kidney injury should not be neglected - optimization of quick Pitt bacteremia score for predicting mortality in critically ill patients with bloodstream infection: a retrospective cohort study

Background

Considering the therapeutic difficulties and mortality associated with bloodstream infection (BSI), it is essential to investigate other potential factors affecting mortality in critically ill patients with BSI and examine the utility of the quick Pitt bacteremia (qPitt) score to improve the survival rate.

Objectives

To improve the predictive accuracy of the qPitt scoring system by evaluating the five current components of qPitt and including other potential factors influencing mortality in critically ill patients with BSI.

Design

This was a retrospective cohort study.

Methods

Medical information from the Medical Information Mart for Intensive Care IV database was used in this retrospective cohort study. The risk factors associated with mortality were examined using a multivariate logistic regression model. The area under the receiver operating characteristic curve (AUC) was used to assess the discriminatory capability of the prediction models.

Results

In total, 1240 eligible critically ill patients with BSI were included. After adjustment for age, community-onset BSI, indwelling invasive lines, and Glasgow Coma Scale (GCS) ⩽ 8, acute kidney injury (AKI) was identified as a notable risk factor for 14-day mortality. Except for altered mental status, the four other main components of the original qPitt were significantly associated with 14-day mortality. Hence, we established a modified qPitt (m-qPitt) by adding AKI and replacing altered mental status with GCS ⩽ 8. The AUCs for m-qPitt and qPitt were 0.723 [95% confidence interval (CI): 0.683-0.759] and 0.708 (95% CI: 0.669-0.745) in predicting 14-day mortality, respectively. Moreover, m-qPitt also had acceptable performance and discrimination power [0.700 (95% CI: 0.666-0.732)] in predicting 28-day mortality.

Conclusion

AKI significantly influenced the survival of critically ill patients with BSIs. Compared with the original qPitt, our new m-qPitt was proven to have a better predictive performance for mortality in critically ill patients with BSI. Further studies should be conducted to validate the practicality of m-qPitt.

Machine Learning Algorithm Predicts Mortality Risk in Intensive Care Unit for Patients with Traumatic Brain Injury

BACKGROUND

Numerous mortality prediction tools are currently available to assist patients with moderate to severe traumatic brain injury (TBI). However, an algorithm that utilizes various machine learning methods and employs diverse combinations of features to identify the most suitable predicting outcomes of brain injury patients in the intensive care unit (ICU) has not yet been well-established.

METHOD

Between January 2016 and December 2021, we retrospectively collected data from the electronic medical records of Chi Mei Medical Center, comprising 2260 TBI patients admitted to the ICU. A total of 42 features were incorporated into the analysis using four different machine learning models, which were then segmented into various feature combinations. The predictive performance was assessed using the area under the curve (AUC) of the receiver operating characteristic (ROC) curve and validated using the Delong test.

RESULT

The AUC for each model under different feature combinations ranged from 0.877 (logistic regression with 14 features) to 0.921 (random forest with 22 features). The Delong test indicated that the predictive performance of the machine learning models is better than that of traditional tools such as APACHE II and SOFA scores.

CONCLUSION

Our machine learning training demonstrated that the predictive accuracy of the LightGBM is better than that of APACHE II and SOFA scores. These features are readily available on the first day of patient admission to the ICU. By integrating this model into the clinical platform, we can offer clinicians an immediate prognosis for the patient, thereby establishing a bridge for educating and communicating with family members.

Comparison of Glasgow Coma Scale and Full Outline of UnResponsiveness score for prediction of in-hospital mortality in traumatic brain injury patients: a systematic review and meta-analysis

BACKGROUND

Currently, Glasgow Coma Scale (GCS) is used to assess patients' level of consciousness. Although this tool is highly popular in clinical settings, it has various limitations that reduce its applicability in certain situations. This had led researchers to look for alternative scoring systems. This study aims to compare the value of GCS and Full Outline of UnResponsiveness (FOUR) score for prediction of mortality in traumatic brain injury (TBI) patients through a systematic review and meta-analysis.

METHOD

Online databases of Medline, Embase, Scopus, and Web of Science were searched until the end of July 2022 for studies that had compared GCS and FOUR score in TBI patients. Interested outcomes were mortality and unfavorable outcome (mortality + disability). Findings are reported as area under the curve (AUC) sensitivity, specificity, and diagnostic odds ratio.

RESULTS

20 articles (comprised of 2083 patients) were included in this study. AUC of GCS and FOUR score for prediction of in-hospital mortality after TBI was 0.92 (95% CI 0.80-0.91) and 0.91 (95% CI 0.88-0.93) respectively. The diagnostic odds ratio of the two scores for prediction of in-hospital mortality after TBI was 44.51 (95% CI 23.58-84.03) for GCS and 45.16 (95% CI 24.25-84.09) for FOUR score. As for prediction of unfavorable outcome after TBI, AUC of GCS and FOUR score were 0.95 (95% CI 0.93 to 0.97) and 0.93 (95% CI 0.91-0.95), respectively. The diagnostic odds ratios for prediction of unfavorable outcome after TBI were 66.31 (95% CI 35.05-125.45) for GCS and 45.39 (95% CI 23.09-89.23) for FOUR score.

CONCLUSION

Moderate level of evidence showed that the value of GCS and FOUR score in the prediction of in-hospital mortality and unfavorable outcome is comparable. The similar performance of these scores in assessment of TBI patients gives the medical staff the option to use either one of them according to the situation at hand.

Prediction performance of the machine learning model in predicting mortality risk in patients with traumatic brain injuries: a systematic review and meta-analysis

PURPOSE

With the in-depth application of machine learning(ML) in clinical practice, it has been used to predict the mortality risk in patients with traumatic brain injuries(TBI). However, there are disputes over its predictive accuracy. Therefore, we implemented this systematic review and meta-analysis, to explore the predictive value of ML for TBI.

METHODOLOGY

We systematically retrieved literature published in PubMed, Embase.com, Cochrane, and Web of Science as of November 27, 2022. The prediction model risk of bias(ROB) assessment tool (PROBAST) was used to assess the ROB of models and the applicability of reviewed questions. The random-effects model was adopted for the meta-analysis of the C-index and accuracy of ML models, and a bivariate mixed-effects model for the meta-analysis of the sensitivity and specificity.

RESULT

A total of 47 papers were eligible, including 156 model, with 122 newly developed ML models and 34 clinically recommended mature tools. There were 98 ML models predicting the in-hospital mortality in patients with TBI; the pooled C-index, sensitivity, and specificity were 0.86 (95% CI: 0.84, 0.87), 0.79 (95% CI: 0.75, 0.82), and 0.89 (95% CI: 0.86, 0.92), respectively. There were 24 ML models predicting the out-of-hospital mortality; the pooled C-index, sensitivity, and specificity were 0.83 (95% CI: 0.81, 0.85), 0.74 (95% CI: 0.67, 0.81), and 0.75 (95% CI: 0.66, 0.82), respectively. According to multivariate analysis, GCS score, age, CT classification, pupil size/light reflex, glucose, and systolic blood pressure (SBP) exerted the greatest impact on the model performance.

CONCLUSION

According to the systematic review and meta-analysis, ML models are relatively accurate in predicting the mortality of TBI. A single model often outperforms traditional scoring tools, but the pooled accuracy of models is close to that of traditional scoring tools. The key factors related to model performance include the accepted clinical variables of TBI and the use of CT imaging.

Development and validation of a novel poisoning agitation-sedation score for predicting the need for endotracheal intubation and mechanical ventilation in acutely poisoned patients with disturbed consciousness

BACKGROUND

Accurate assessment of disturbed consciousness level (DCL) is crucial for predicting acutely poisoned patients' outcomes.

AIM

Development of a novel Poisoning Agitation-Sedation Score (PASS) to predict the need for endotracheal intubation (ETI) and mechanical ventilation (MV) in acutely poisoned patients with DCL. Validation of the proposed score on a new set of acutely poisoned patients with DCL.

METHODS

This study was conducted on 187 acutely poisoned patients with DCL admitted to hospital from June 2020 to November 2021 (Derivation cohort). Patients' demographics, toxicological data, neurological examination, calculation of the Glasgow Coma Scale (GCS), Full Outline of Unresponsiveness (FOUR) score, Richmond Agitation-Sedation Scale (RASS), and outcomes were gathered for developing a new score. The proposed score was externally validated on 100 acutely poisoned patients with DCL (Validation cohort).

RESULTS

The PASS assessing sedation consists of FOUR (reflexes and respiration) and GCS (motor) and provides a significantly excellent predictive power (AUC = 0.975) at a cutoff ≤9 with 100% sensitivity and 92.11% specificity for predicting the need for ETI and MV in sedated patients. Additionally, adding RASS (agitation) to the previous model exhibits significantly good predictive power (AUC = 0.893), 90.32% sensitivity, and 73.68% specificity at a cutoff ≤14 for predicting the need for ETI and MV in disturbed consciousness patients with agitation.

CONCLUSION

The proposed PASS could be an excellent, valid and feasible tool to predict the need for ETI and MV in acutely poisoned disturbed consciousness patients with or without agitation.

Development and validation of a nomogram to predict the 30-day mortality risk of patients with intracerebral hemorrhage

Background

Intracerebral hemorrhage (ICH) is a stroke syndrome with an unfavorable prognosis. Currently, there is no comprehensive clinical indicator for mortality prediction of ICH patients. The purpose of our study was to construct and evaluate a nomogram for predicting the 30-day mortality risk of ICH patients.

Methods

ICH patients were extracted from the MIMIC-III database according to the ICD-9 code and randomly divided into training and verification cohorts. The least absolute shrinkage and selection operator (LASSO) method and multivariate logistic regression were applied to determine independent risk factors. These risk factors were used to construct a nomogram model for predicting the 30-day mortality risk of ICH patients. The nomogram was verified by the area under the receiver operating characteristic curve (AUC), integrated discrimination improvement (IDI), net reclassification improvement (NRI), and decision curve analysis (DCA).

Results

A total of 890 ICH patients were included in the study. Logistic regression analysis revealed that age (OR = 1.05, P < 0.001), Glasgow Coma Scale score (OR = 0.91, P < 0.001), creatinine (OR = 1.30, P < 0.001), white blood cell count (OR = 1.10, P < 0.001), temperature (OR = 1.73, P < 0.001), glucose (OR = 1.01, P < 0.001), urine output (OR = 1.00, P = 0.020), and bleeding volume (OR = 1.02, P < 0.001) were independent risk factors for 30-day mortality of ICH patients. The calibration curve indicated that the nomogram was well calibrated. When predicting the 30-day mortality risk, the nomogram exhibited good discrimination in the training and validation cohorts (C-index: 0.782 and 0.778, respectively). The AUCs were 0.778, 0.733, and 0.728 for the nomogram, Simplified Acute Physiology Score II (SAPSII), and Oxford Acute Severity of Illness Score (OASIS), respectively, in the validation cohort. The IDI and NRI calculations and DCA analysis revealed that the nomogram model had a greater net benefit than the SAPSII and OASIS scoring systems.

Conclusion

This study identified independent risk factors for 30-day mortality of ICH patients and constructed a predictive nomogram model, which may help to improve the prognosis of ICH patients.

The Richmond Acute Subdural Hematoma Score: A Validated Grading Scale to Predict Postoperative Mortality

BACKGROUND

Traumatic acute subdural hematomas (aSDHs) are common, life-threatening injuries often requiring emergency surgery.

OBJECTIVE

To develop and validate the Richmond acute subdural hematoma (RASH) score to stratify patients by risk of mortality after aSDH evacuation.

METHODS

The 2016 National Trauma Data Bank (NTDB) was queried to identify adult patients with traumatic aSDHs who underwent craniectomy or craniotomy within 4 h of arrival to an emergency department. Multivariate logistic regression modeling identified risk factors independently associated with mortality. The RASH score was developed based on a factor's strength and level of association with mortality. The model was validated using the 2017 NTDB and the area under the receiver operating characteristic curve (AUC).

RESULTS

A total of 2516 cases met study criteria. The patients were 69.3% male with a mean age of 55.7 yr and overall mortality rate of 36.4%. Factors associated with mortality included age between 61 and 79 yr (odds ratio [OR] = 2.3, P < .001), age ≥80 yr (OR = 6.3, P < .001), loss of consciousness (OR = 2.3, P < .001), Glasgow Coma Scale score of ≤8 (OR = 2.6, P < .001), unilateral (OR = 2.8, P < .001) or bilateral (OR = 3.9, P < .001) unresponsive pupils, and midline shift >5 mm (OR = 1.7, P < .001). Using these risk factors, the RASH score predicted progressively increasing mortality ranging from 0% to 94% for scores of 0 to 8, respectively (AUC = 0.72). Application of the RASH score to 3091 cases from 2017 resulted in similar accuracy (AUC = 0.74).

CONCLUSION

The RASH score is a simple and validated grading scale that uses easily accessible preoperative factors to predict estimated mortality rates in patients with traumatic aSDHs who undergo surgical evacuation.

Prediction of Mortality in the Medical Intensive Care Unit with Serial Full Outline of Unresponsiveness Score in Elderly Patients

Background

Advanced age is one of the key risk factors for mortality and morbidity in intensive care units. The full outline of unresponsiveness (FOUR) score has been developed and introduced to address the limitations of the Glasgow Coma Scale (GCS). The current study aimed to evaluate the ability of the FOUR score in predicting the outcomes (survivors, nonsurvivors).

Materials and methods

This observational study of 168 consecutive elderly patients admitted to medical intensive care during the 14 months carried out prospectively. FOUR score in the 24, 48, and 72 hours of admission, and demographic characteristics of all elderly patients were calculated, then recorded. The receiver operating characteristic (ROC) curve, logistic regression, and Hosmer-Lemeshow test were used (95% confidence interval) for statistical analysis.

Results

FOUR scores in 24, 48, and 72 hours between survivors and nonsurvivors (p <0.0001, p <0.0001, and p <0.0001, respectively) were statistically different. The discrimination power of FOUR score 24 hours of admission was excellent [area under ROC (AUC): 85.7% [standard error (SE)]: 2.8%]; it was acceptable for 48 and 72 hours of admission [AUC: 76.3% (SE: 3.6%), AUC: 75/0% (SE: 3.8%), respectively]. The FOUR score of 24 and 48 hours (x² = 10.06, p = 0.261, x² = 6.82, p = 0.448, respectively) showed acceptable calibration.

Conclusions

The FOUR score is a suitable scoring system for prognostication of outcomes in critically ill elderly patients. The FOUR score 24 hours of admission was superior in terms of discrimination power than 48 and 72 hours, but better calibration power belonged to FOUR score 48 hours.

How to cite this article

Ramazani J, Hosseini M. Prediction of Mortality in the Medical Intensive Care Unit with Serial Full Outline of Unresponsiveness Score in Elderly Patients. Indian J Crit Care Med 2022;26(1):94–99.

Correlation between bispectral index and prognosis of patients with acute cerebral infarction

INTRODUCTION

This study aimed to investigate the clinical value of bispectral index (BIS) monitoring in assessing the consciousness and prognosis of acute cerebral infarction (ACI) patients.

METHODS

In total, 64 patients who suffered from ACI with consciousness disturbance were enrolled in this study. Glasgow Coma Scale (GCS) was performed to evaluate the consciousness level of ACI patients, and BIS was used to monitor the depth of anesthesia and sedation. Then, patients were divided into good prognosis, poor prognosis and death groups according to modified Rankin score (mRS). Discrimination analysis of BIS values and GCS score for the prediction of prognosis was performed using the receiver operator characteristic (ROC) curve.

RESULTS

GCS score and BIS values showed statistically significant differences among the three groups. Spearman rank correlation analysis revealed a significant positive correlation between BIS values and GCS score, while BIS values was negatively related with mRS. The ROC curve of prognosis prediction showed strong prognostic power, with area under the curves (AUCs) between 0.830 and 0.917. Moreover, the AUC of BISmean score was higher than that of BISmax, BISmin and GCS, and BISmean of 74 was the best cut-off point for good prognosis.

CONCLUSION

BIS directly reflects the degree of consciousness disturbance in ACI patients, and thus accurately predicts the prognosis, indicating potential application values of BIS in clinical practice.

Combination of Neutrophil-to-Lymphocyte Ratio and Admission Glasgow Coma Scale Score Is Independent Predictor of Clinical Outcome in Diffuse Axonal Injury

BACKGROUND

The neutrophil-to-lymphocyte ratio (NLR) is an independent predictor of clinical outcome of different diseases, such as acute ischemic stroke, intracerebral hemorrhage, malignant tumor, and traumatic brain injury. However, the prognostic value of NLR plus admission Glasgow Coma Scale score (NLR-GCS) is still unclear in patients with diffuse axonal injury (DAI). Therefore this study assessed the relationship between the NLR-GCS and 6-month outcome of DAI patients.

METHODS

The clinical characteristics of DAI patients admitted to our department between January 2014 and January 2020 were retrospectively analyzed. The candidate risk factors were screened by using univariate analysis, and the independence of resultant risk factors was evaluated by the binary logistic regression analysis and least absolute shrinkage and selection operator regression analysis. The predictive value of NLR-GCS in an unfavorable outcome was assessed by the receiver operating characteristics curve analysis.

RESULTS

A total of 93 DAI patients were included. Binary logistic regression analysis and least absolute shrinkage and selection operator regression analysis showed the level of NLR on admission was an independent risk factor of unfavorable outcomes in DAI patients. The ROC curve analysis showed that the predictive capacity of the combination of NLR and admission GCS score and combination of NLR and coma duration outperformed NLR, admission GCS score, and coma duration alone.

CONCLUSIONS

The higher NLR level on admission is independently associated with unfavorable outcomes of DAI patients at 6 months. Furthermore, the combination of NLR and admission GCS score provides the superior predictive capacity to either NLR or GCS alone.

The effect of family-centered sensory and affective stimulation on comatose patients with traumatic brain injury: A systematic review and meta-analysis

BACKGROUND

Sensory stimulation has been used in the early rehabilitation of comatose patients with traumatic brain injury, but the effect of sensory stimulation involving family members is unclear.

OBJECTIVES

To evaluate the effects of family-centered sensory and affective stimulation on comatose patients with traumatic brain injury and explore the factors that affect the outcomes.

DESIGN

A systematic review with a meta-analysis.

DATA SOURCES

Electronic databases including PubMed, Web of Science, Google Scholar, Cochrane Library, CINAHL, China National Knowledge Infrastructure, and WanFang were searched from October 2019 to May 2020.

REVIEW METHODS

Two reviewers independently assessed eligibility of potential studies and extracted data. Quality of included studies was assessed according to the evaluation criteria of Cochrane Evaluation Manual 5.1.0. Outcome measures of the meta-analysis were the Glasgow Coma Scale scores, the Western Neuro Sensory Stimulation Profile scores, awakening time, and satisfaction rate. To explore whether there was a difference in the effect between variants of the intervention, variables as subgroups were time to start intervention, type of intervention, duration of each intervention, daily frequency of intervention, days of intervention, and patient's area.

RESULT

Seventeen randomized controlled trials were included in the review and meta-analysis. Most studies were of medium quality. The improvement of the Glasgow Coma Scale score is significantly greater with the intervention implemented within 24 h compared to the intervention implemented 24 h later (mean difference 3.91, 95% confidence interval 3.44-4.38 vs. mean difference 1.90, 95% confidence interval 1.69-2.12, respectively). The results of subgroup analyses show that auditory stimulation combined with tactile stimulation and multi-sensory stimulation are associated with better outcomes than a single use of auditory stimulation. Studies from Asia report more positive outcomes than those from America (mean difference 1.94, 95% confidence interval 1.73-2.16 vs. mean difference 0.44, 95% confidence interval -0.87-1.75). And the improvement of the Glasgow Coma Scale score with the stimulation performed by family members is greater than that with the stimulation implemented by nurses (mean difference 2.17, 95% confidence interval 1.67-2.66). Besides, it is associated with better awakening time, awakening rate, and satisfaction rate compared to routine care.

CONCLUSION

Early family-centered sensory and affective stimulation is more effective than routine care and nurse-implemented sensory stimulation in improving the level of consciousness and cognition of comatose patients with traumatic brain injury, and multi-sensory stimulation is more effective than single stimulation. More studies with larger sample size and high quality in different countries are warranted.

Critical Appraisal of Traumatic Brain Injury and Its Management

Critical appraisal of traumatic brain injury (TBI) management has always been marred with a conflict of differential approaches, with claims and counterclaims of outcomes among the research groups. We performed this study to review the management of TBI from risk factors to outcomes including the comorbidities and final clinical status. In conjunction with the aforesaid stabilization of TBI cases, prophylactic and definitive surgical approaches and other supporting interventions will ultimately decide the final outcomes in the long run. Improvements in the quality of care for patients with severe TBI, with the reduction in mortality, have been demonstrated in high-income areas due to improvements in the health care system and not just in one isolated intervention. In the management of TBI, a fast and high index of suspicion is the key to success, from the initial assessment to the final rehabilitation of the cases, from the victim of risk factors to the victims of situation. The research groups feel that TBI prophylactic measures and primary care mitigation models are as important as definitive care, starting from prehospital care to dedicated care.

Comparison of full outline of unresponsiveness score and Glasgow Coma Scale in Medical Intensive Care Unit

Context:

The Glasgow Coma Scale (GCS) is the most commonly used scale, and Full Outline of Unresponsiveness (FOUR) score is new validated coma scale as an alternative to GCS in the evaluation of the level of consciousness.

Aim:

The aim of the current study was to evaluate FOUR score and GCS ability in predicting the outcomes (Survivors, nonsurvivors) in Medical Intensive Care Unit (MICU).

Setting and Design:

This was an observational and prospective study of 300 consecutive patients admitted to the MICU during a 14 months’ period.

Materials and Methods:

FOUR score, GCS score, and demographic characteristics of all patients were recorded in the first admission 24 h.

Statistical Analysis Used:

A receiver operator characteristic (ROC) curve, Hosmer–Lemeshow test, and Logistic regression were used in the statistical analysis (95% confidence interval).

Results:

Data analysis showed a significant statistical difference in FOUR score and GCS score between survivors and nonsurvivors (P < 0.0001, P < 0.0001; respectively). The discrimination power was good for both FOUR score and GCS (area under ROC curve: 87.3% (standard error [SE]: 2.1%), 82.6% [SE: 2.3%]; respectively). The acceptable calibration was seen just for FOUR score (χ² = 8.059, P = 0.428).

Conclusions:

Both FOUR score and GCS are valuable scales for predicting outcomes in patients are admitted to the MICU; however, the FOUR score showed better discrimination and calibration than GCS, so it is superior to GCS in predicting outcomes in this patients population.

Usefulness of serum glucose and potassium ratio as a predictor for 30-day death among patients with severe traumatic brain injury

BACKGROUND

Serum glucose and potassium ratio (GPR) was recently found to be related to outcome of aneurysmal subarachnoid hemorrhage. This retrospectively study was to investigate the association of serum GPR with mortality in severe traumatic brain injury (sTBI).

METHODS

Clinical data were retrospectively reviewed of isolated sTBI patients admitted within 12 h after trauma between January 2014 and January 2019. We analyzed relationships between admission serum GPR and post-traumatic 30-day mortality in addition to admission Glasgow coma scale (GCS) scores. Discriminative ability was evaluated using area under receiver operating characteristic curve (AUC).

RESULTS

A total of 146 patients, of whom 37 (25.3%) died within 30 days following trauma, were included. Admission serum GPR emerged as an independent predictor for 30-day mortality (odds ratio, 5.256; 95% confidence interval (CI), 1.111-14.856) and overall survival (hazard ratio, 4.822; 95% CI, 1.157-12.870), with an AUC of 0.777 (95% CI, 0.693-0.835), which was equivalent to that of GCS scores (AUC, 0.831; 95% CI, 0.760-0.888; P = 0.179). There was a significant correlation between admission serum GPR and GCS scores (r² = 0.293).

CONCLUSIONS

Serum GPR in cases of sTBI is substantially associated with trauma severity and 30-day mortality. Therefore, the potential value of serum GPR for predicting short-term mortality of sTBI patients is favorable.

The current significance of the FOUR score: A systematic review and critical analysis of the literature

BACKGROUND

The Full Outline of Un-Responsiveness Score (FOURs) is a scale for clinical assessment of consciousness that was introduced to overcome disadvantages of the widely accepted Glasgow Coma Scale (GCS).

OBJECTIVE

To carry out a systematic review and critical analysis of the available literature on the clinical application of FOURs and perform a comparison to GCS, in terms of reliability and predictive value.

METHODS

Initial search retrieved a total of 147 papers. After applying strict inclusion criteria and further article selection to overcome data heterogeneity, a statistical comparison of inter-rater reliability, in-hospital mortality and long-term outcome prediction between the two scales in the adult and pediatric population was done.

RESULTS

Even though FOURs is more complicated than GCS, its application remains quite simple. Its reliability, validity and predictive value have been supported by an increasing number of studies, especially in critical care. A statistically significant difference (p = .034) in predicting in-hospital mortality in adults, in favor of FOURs when compared to GCS, was found. However, whether it poses a clinically significant advantage in detecting patients' deterioration and outcome prediction, compared to other scaling systems, remains unclear.

CONCLUSIONS

Further studies are needed to discern the FOURs' clinical usefulness, especially in patients in non-critical condition, with milder disorders of consciousness.

Using components of the Glasgow coma scale and Rotterdam CT scores for mortality risk stratification in adult patients with traumatic brain injury: A preliminary study

OBJECTIVE

The Glasgow Coma Scale (GCS) and Rotterdam Computed Tomography Score (RCTS) are widely used to predict outcomes after traumatic brain injury (TBI). The objective of this study was to determine whether the GCS and RCTS components can be used to predict outcomes in patients with traumatic intracranial hemorrhage (IH) after TBI.

PATIENTS AND METHODS

Between May 2009 and July 2017, 773 patients with IH after TBI were retrospectively reviewed. Data on initial GCS, RCTS according to initial brain CT, and status at hospital discharge and last follow-up were collected. Logistic regression analysis was performed to evaluate the relationship between GCS and RCTS components with outcomes after TBI.

RESULTS

Among the 773 patients, the overall in-hospital mortality rate was 14.0%. Variables independently associated with outcomes were the verbal (V-GCS) and motor components of GCS (M-GCS), epidural mass lesion (E-RCTS) and intraventricular or subarachnoid hemorrhage components of RCTS (H-RCTS) (p < 0.0001). The new TBI score was obtained with the following calculation: [V-GCS + M-GCS] - [E-RCTS + H-RCTS].

CONCLUSION

The new TBI score includes both clinical status and radiologic findings from patients with IH after TBI. The new TBI score is a useful tool for assessing TBI patients with IH in that it combines the GCS and RCTS components that increases area under the curve for predicting in-hospital mortality and unfavorable outcomes and eliminates the paradoxical relationship with outcomes which was observed in GCS score. It allows a practical method to stratify the risk of outcomes after TBI.

The Full Outline of UnResponsiveness (FOUR) Score and Its Use in Outcome Prediction: A Scoping Systematic Review of the Adult Literature

BACKGROUND

The Full Outline of UnResponsivness (FOUR) score is a neurological assessment score. Its theoretical benefit over preexisting scores is its evaluation of brainstem reflexes and respiratory pattern which may allow better assessment of patients with severe neurologic impairment.

OBJECTIVE

Our goal was to perform a scoping systematic review on the available literature for FOUR score and outcome prediction in critically ill patients. The primary outcome of interest was patient global outcome, as assessed by any of: mortality, modified Rankin Score, Glasgow Outcome Score, or any other functional or neuropsychiatric outcome. Information on interobserver reliability was also extracted.

METHODS

MEDLINE and five other databases were searched. Inclusion criteria were: humans, adults, and children; prospective randomized controlled trial; prospective cohort, cohort/control, case series, prospective, and retrospective studies. Two reviewers independently screened the results. Full texts for citations passing this initial screen were obtained. Inclusion and exclusion criteria were applied to each article to obtain final articles for review. Results on adult populations are presented here. Data are reported following the preferred reporting items for systematic reviews and meta-analyses guidelines.

RESULTS

The initial search yielded 1709 citations. Of those used, 49 were based on adult and 6 on pediatric populations. All but 8 retrospective adult studies were performed prospectively. Patient categories included traumatic brain injury, intraventricular hemorrhage, intracerebral hemorrhage, subarachnoid hemorrhage, ischemic stroke, general/combined neurology and neurosurgery, post-cardiac arrest, medicine/general critical illness, and patients in the emergency department. A total of 9092 adult patients were studied. Fourteen studies demonstrated good interobserver reliability of the FOUR score. Nine studies demonstrated prognostic value of the FOUR score in predicting mortality and functional outcomes. Thirty-two studies demonstrated equivalency or superiority of the FOUR score compared to Glasgow Coma Score in prediction of mortality and functional outcomes.

CONCLUSIONS

The FOUR score has been shown to be a useful outcome predictor in many patients with depressed level of consciousness. It displays good inter-rater reliability among physicians and nurses.

Demographics and clinical characteristics of acute traumatic brain injury patients in the different Neuroimaging Radiological Interpretation System (NIRIS) categories

PURPOSE

To characterize the demographics, clinical and imaging findings, and outcomes of traumatic brain injury (TBI) patients in each of NeuroImaging Radiological Interpretation System (NIRIS) categories.

MATERIAL AND METHODS

We considered all consecutive patients transported to Stanford Hospital's emergency department by ambulance or helicopter between November 2015 and April 2017. We retained adult patients (> 18 years old) for whom a trauma alert was triggered and who underwent a non-contrast head computer tomography (CT) because of suspected TBI. We reviewed the non-contrast CT scans in these patients for the NIH TBI common data elements (CDEs). We recorded, then assessed differences in terms of demographics, clinical characteristics, imaging CDEs, and outcomes in patients from the different NIRIS categories.

RESULTS

In all, 1152 patients were included in this study. Patients with NIRIS 0 imaging findings were significantly younger than patients in other NIRIS categories (P<0.001). Motor vehicle accidents and falls from height were the most common mechanisms of injury across NIRIS categories. GCS scores decreased with increasing NIRIS category imaging findings and were significantly lower in patients with NIRIS 4 imaging findings (P<0.001). Significant differences in NIRIS categories were observed for all imaging CDEs (P<0.001), in agreement with the definition of the different NIRIS categories. Mortality increased progressively with increasing NIRIS severity.

CONCLUSIONS

TBI patients in different NIRIS categories have different clinical characteristics, hospital courses and outcomes. This natural history assessment of patients from different NIRIS categories could thus serve as a reference standard for future TBI clinical trials.

The Relationship of the FOUR Score to Patient Outcome: A Systematic Review

The Full Outline of UnResponsiveness (FOUR) score assessment of consciousness replaces the Glasgow Coma Scale (GCS) verbal component with assessment of brainstem reflexes. A comprehensive overview studying the relationship between a patient's FOUR score and outcome is lacking. We aim to systematically review published literature reporting the relationship of FOUR score to outcome in adult patients with impaired consciousness. We systematically searched for records of relevant studies: CENTRAL, MEDLINE, EMBASE, Scopus, Web of Science, ClinicalTrials.gov, and OpenGrey. Prospective, observational studies of patients with impaired consciousness were included where consciousness was assessed using FOUR score, and where the outcome in mortality or validated functional outcome scores was reported. Consensus-based screening and quality appraisal were performed. Outcome prognostication was synthesized narratively. Forty records (37 studies) were identified, with overall low (n = 2), moderate (n = 25), or high (n = 13) risk of bias. There was significant heterogeneity in patient characteristics. FOUR score showed good to excellent prognostication of in-hospital mortality in most studies (area under curve [AUC], >0.80). It was good at predicting poor functional outcome (AUC, 0.80–0.90). There was some evidence that motor and eye components (also GCS components) had better prognostic ability than brainstem components. Overall, FOUR score relates closely to in-hospital mortality and poor functional outcome. More studies with standardized design are needed to better characterize it in different patient groups, confirm the differences between its four components, and compare it with the performance of GCS and its recently described derivative, the GCS-Pupils, which includes pupil response as a fourth component.

Modified Glasgow Coma Scale Using Serum Factors as a Prognostic Model in Traumatic Brain Injury

BACKGROUND

Traumatic brain injury (TBI) is a major cause of death and disability. This study evaluated a possible relationship between serum factors at admission and the outcome of TBI. We propose a statistically validated scale for patients with TBI that combines serum factors and the Glasgow Coma Scale (GCS).

METHODS

Between May 2011 and July 2016, 219 patients underwent decompressive craniectomy for TBI. We assessed laboratory data on admission, and correlations with GSC and Glasgow Outcome Scale were investigated. The modified GCS was developed from a multivariable logistic regression model, which was validated with the backward stepwise method.

RESULTS

Of 219 patients with TBI enrolled in our study, 175 were men (79.9%) and 44 were women (20.1%) with a mean age of 49.1 ± 11.5 years. Initial serum values of hemoglobin, platelets, prothrombin time, and lactate dehydrogenase were associated with in-hospital mortality. The factor score was derived by adding the following points: hemoglobin (≥13.0 g/dL = 0, <13.0 g/dL = 1), platelets (≥150 × 10³/mm³ = 0, <150 × 10³/mm³ = 1), prothrombin time (<13.2 seconds = 0, ≥13.2 seconds = 1), and lactate dehydrogenase (<271 U/L = 0, ≥271 U/L = 1). The modified GCS score (GCS score [range, 6-15] - FS [range, 0-4]) was calculated.

CONCLUSIONS

The modified GCS score using serum factors extended the information provided about patient outcomes to be comparable to more complex methods. The modified GCS score may be useful to predict in-hospital mortality in patients with TBI.

Preoperative serum lactate cannot predict in-hospital mortality after decompressive craniectomy in traumatic brain injury

PURPOSE

Despite the utility of serum lactate for predicting clinical courses, little information is available on the topic after decompressive craniectomy. This study was conducted to determine the ability of perioperative serum lactate levels to predict in-hospital mortality in traumatic brain-injury patients who received emergency or urgent decompressive craniectomy.

METHODS

The medical records of 586 consecutive patients who underwent emergency or urgent decompressive craniectomy due to traumatic brain injuries from January 2007 to December 2014 were retrospectively analyzed. Pre- and intraoperative serum lactate levels and base deficits were obtained from arterial blood gas analysis results.

RESULTS

The overall mortality rate after decompressive craniectomy was 26.1 %. Mean preoperative serum lactate was significantly higher in the non-survivors (P = 0.034) than the survivors but had no significance for predicting in-hospital mortality in the multivariate regression analysis (P = 0.386). Rather, preoperative Glasgow Coma Score was a significant predictor for in-hospital mortality (hazard ratio 0.796, 95 % confidence interval 0.755-0.836, P < 0.001).

CONCLUSION

Preoperative lactate level is not an independent predictor of in-hospital mortality after decompressive craniectomy in traumatic brain-injury patients.