Comparative study of computed tomographic scoring systems and predictors of early mortality in severe traumatic brain injury

Traumatic Brain Injury Outcome Associations With Computed Tomography and Glasgow Coma Scale Score Interactions: A Retrospective Study

Background Numerous investigators have shown that early postinjury Glasgow Coma Scale (GCS) values are associated with later clinical outcomes in patients with traumatic brain injury (TBI), in-hospital mortality, and post-hospital discharge Glasgow Outcome Scale (GOS) results. Following TBI, early GCS, and brain computed tomography (CT) scores have been associated with clinical outcomes. However, only one previous study combined GCS scores with CT scan results and demonstrated an interaction with in-hospital mortality and GOS results. We aimed to determine if interactive GCS and CT findings would be associated with outcomes better than GCS and CT findings alone. Methodology Our study included TBI patients who had GCS scores of 3-12 and required mechanical ventilation for ≥five days. The GCS deficit was determined as 15 minus the GCS score. The mass effect CT score was calculated as lateral ventricular compression plus basal cistern compression plus midline shift. Each value was 1 for present. A prognostic CT score was the mass effect score plus subarachnoid hemorrhage (2 if present).The CT-GCS deficit score was the sum of the GCS deficit and the prognostic CT score. Results One hundred and twelve consecutive TBI patients met the inclusion criteria. Patients with surgical decompression had a lower GCS score (6.0±3.0) than those without (7.7±3.3; Cohen d=0.54). Patients with surgical decompression had a higher mass effect CT score (2.8±0.5) than those without (1.7±1.0; Cohen d=1.4). The GCS deficit was greater in patients not following commands at hospital discharge (9.6±2.6) than in those following commands (6.8±3.2; Cohen d=0.96). The prognostic CT score was greater in patients not following commands at hospital discharge (3.7±1.2) than in those following commands (3.1±1.1; Cohen d=0.52). The CT-GCS deficit score was greater in patients not following commands at hospital discharge (13.3±3.2) than in those following commands (9.9±3.2; Cohen d=1.06). Logistic regression stepwise analysis showed that the failure to follow commands at hospital discharge was associated with the CT-GCS deficit score but not with the GCS deficit. The GCS deficit was greater in patients not following commands at three months (9.7±2.8) than in those following commands (7.4±3.2; Cohen d=0.78). The CT-GCS deficit score was greater in patients not following commands at three months (13.6±3.1) than in those following commands (10.5±3.4; Cohen d=0.94). Logistic regression stepwise analysis showed that failure to follow commands at three months was associated with the CT-GCS deficit score but not with the GCS deficit. The proportion not following commands at three months was greater with a GCS deficit of 9-12 (50.9%) than with a GCS deficit of 3-8 (21.1%; odds ratio=3.9; risk ratio=2.1). The proportion of not following commands at three months was greater with a CT-GCS deficit score of 13-17 (56.0%) than with a CT-GCS deficit score of 4-12 (18.3%; OR=5.7; RR=3.1). Conclusion The mass effect CT score had a substantially better association with the need for surgical decompression than did the GCS score. The degree of association for not following commands at hospital discharge and three months was greater with the CT-GCS deficit score than with the GCS deficit. These observations support the notion that a mass effect and subarachnoid hemorrhage composite CT score can interact with the GCS score to better prognosticate TBI outcomes than the GCS score alone.

Machine learning-based models to predict the need for neurosurgical intervention after moderate traumatic brain injury

Background and Aims

Traumatic brain injury (TBI) is a widespread global health issue with significant economic consequences. However, no existing model exists to predict the need for neurosurgical intervention in moderate TBI patients with positive initial computed tomography scans. This study determines the efficacy of machine learning (ML)-based models in predicting the need for neurosurgical intervention.

Methods

This is a retrospective study of patients admitted to the neuro-intensive care unit of Emtiaz Hospital, Shiraz, Iran, between January 2018 and December 2020. The most clinically important variables from patients that met our inclusion and exclusion criteria were collected and used as predictors. We developed models using multilayer perceptron, random forest, support vector machines (SVM), and logistic regression. To evaluate the models, their F1-score, sensitivity, specificity, and accuracy were assessed using a fourfold cross-validation method.

Results

Based on predictive models, SVM showed the highest performance in predicting the need for neurosurgical intervention, with an F1-score of 0.83, an area under curve of 0.93, sensitivity of 0.82, specificity of 0.84, a positive predictive value of 0.83, and a negative predictive value of 0.83.

Conclusion

The use of ML-based models as decision-making tools can be effective in predicting with high accuracy whether neurosurgery will be necessary after moderate TBIs. These models may ultimately be used as decision-support tools to evaluate early intervention in TBI patients.

Comparison of the predictive value of the Helsinki, Rotterdam, and Stockholm CT scores in predicting 6-month outcomes in patients with blunt traumatic brain injuries

PURPOSE

Despite advances in modern medicine, traumatic brain injuries (TBIs) are still a major medical problem. Early diagnosis of TBI is crucial for clinical decision-making and prognosis. This study aims to compare the predictive value of Helsinki, Rotterdam, and Stockholm CT scores in predicting the 6-month outcomes in blunt TBI patients.

METHODS

This cohort study was conducted on blunt TBI patients of 15 years or older. All of them were admitted to the surgical emergency department of Shahid Beheshti Hospital in Kashan, Iran from 2020 to 2021 and had abnormal trauma-related findings on brain CT images. The patients' demographic data such as age, gender, history of comorbid conditions, mechanism of trauma, Glasgow coma scale, CT images, length of hospital stay, and surgical procedures were recorded. The Helsinki, Rotterdam, and Stockholm CT scores were simultaneously determined according to the existing guidelines. The included patients' 6-month outcome was determined using the Glasgow outcome scale extended. M Data were analyzed by SPSS software version 16.0. Sensitivity, specificity, negative/positive predictive value and the area under the receiver operating characteristic curve were calculated for each test. The Kappa agreement coefficient and Kuder Richardson-20 were used to compare the scoring systems.

RESULTS

Altogether 171 TBI patients met the inclusion and exclusion criteria, with the mean age of (44.9 ± 20.2) years. Most patients were male (80.7%), had traffic related injuries (83.1%) and mild TBIs (64.3%). Patients with lower Glasgow coma scale had higher Helsinki, Rotterdam, and Stockholm CT scores and lower Glasgow outcome scale extended scores. Among all the scoring systems, the Helsinki and Stockholm scores showed the highest agreement in predicting patients' outcomes (kappa = 0.657, p < 0.001). The Rotterdam scoring system had the highest sensitivity (90.1%) in predicting death of TBI patients, whereas the Helsinki scoring system had the highest sensitivity (89.8%) in predicting the 6-month outcome in TBI patients.

CONCLUSION

The Rotterdam scoring system was superior in predicting death in TBI patients, whereas the Helsinki scoring system was more sensitive in predicting the 6-month outcome.

Prediction of Mortality and Morbidity After Severe Traumatic Brain Injury: A Comparison Between Rotterdam and Richmond Computed Tomography Scan Scoring System

OBJECTIVE

Accurate prediction of the morbidity and mortality outcomes of traumatic brain injury patients is still challenging. In the present study, we aimed to compare the predictive value of the Richmond and Rotterdam scoring systems as two novel computed tomography-based predictive models.

METHODS

We retrospectively analyzed 1400 subjects who suffered from severe traumatic brain injury and were admitted to Emtiaz Hospital, a tertiary referral trauma center in Shiraz, south of Iran, from January 2018 to December 2019. We evaluated the 1-month results; considering two primary factors: mortality and morbidity. The patients' condition was the basis for this assessment. We conducted a logistic regression analysis to determine the association between scoring systems and outcomes. To determine the optimal threshold value, we utilized the receiver operating characteristic curve model.

RESULTS

The mean age of participants was 36.61 ± 17.58 years, respectively. Concerning predicting the mortality rate, the area under the curve (AUC) for the Rotterdam score was relatively low 0.64 (95% confidence interval: 0.60, 0.67), while the Richmond score had a higher AUC 0.74 (0.71-0.77), which demonstrated the superiority of this scoring system. Moreover, the Richmond score was more accurate for predicting 1-month morbidity with AUC: 0.71 (0.69, 0.74) versus 0.62 (0.59, 0.65).

CONCLUSIONS

The Richmond scoring system demonstrated more accurate predictions for the present outcomes. The simplicity and predictive value of the Richmond score make this system an ideal option for use in emergency settings and centers with high patient loads.

Prognostic Neuroimaging Biomarkers in Acute Vascular Brain Injury and Traumatic Brain Injury

Prognostic imaging biomarkers after acute brain injury inform treatment decisions, track the progression of intracranial injury, and can be used in shared decision-making processes with families. Herein, key established biomarkers and prognostic scoring systems are surveyed in the literature, and their applications in clinical practice and clinical trials are discussed. Biomarkers in acute ischemic stroke include computed tomography (CT) hypodensity scoring, diffusion-weighted lesion volume, and core infarct size on perfusion imaging. Intracerebral hemorrhage biomarkers include hemorrhage volume, expansion, and location. Aneurysmal subarachnoid biomarkers include hemorrhage grading, presence of diffusion-restricting lesions, and acute hydrocephalus. Traumatic brain injury CT scoring systems, contusion expansion, and diffuse axonal injury grading are reviewed. Emerging biomarkers including white matter disease scoring, diffusion tensor imaging, and the automated calculation of scoring systems and volumetrics are discussed.

Prediction of In-Hospital Outcomes in Patients with Traumatic Brain Injury Using Computed Tomographic Scoring Systems: A Comparison Between Marshall, Rotterdam, and Neuroimaging Radiological Interpretation Systems

OBJECTIVE

This study aimed to compare the prognostic value of Marshall, Rotterdam, and Neuroimaging Radiological Interpretation Systems (NIRIS) in predicting the in-hospital outcomes of patients with traumatic brain injury.

METHODS

We identified 250 patients with traumatic brain injury in a retrospective single-center cohort from 2019 to 2020. Computed tomography (CT) scans were reviewed by two radiologists and scored according to three CT scoring systems. One-month outcomes were evaluated, including hospitalization, intensive care unit admission, neurosurgical procedure, and mortality. Logistic regression analysis was performed to identify scoring systems and outcome relationships. The best cutoff value was calculated using the receiver operating characteristic curve model.

RESULTS

Eighteen patients (7.2%) died in the 1-month follow-up. The mean age and Glasgow Coma Scale of survivors differed significantly from nonsurvivors. Subarachnoid hemorrhage and compressed/absent cisterns were dead patients' most frequent CT findings. All three scoring systems had good discrimination power in mortality prediction (area under the receiver operating characteristic curve of the Marshall, Rotterdam, and NIRIS was 0.78, 0.86, and 0.84, respectively). Regarding outcome, three systems directly correlated with unfavorable outcome prediction.

CONCLUSIONS

The Marshall, Rotterdam, and NIRIS are good predictive models for mortality and outcome prediction, with slight superiority of the Rotterdam in mortality prediction and the Marshall in intensive care unit admission and neurosurgical procedures.

AI-Based Decision Support System for Traumatic Brain Injury: A Survey

Traumatic brain injury (TBI) is one of the major causes of disability and mortality worldwide. Rapid and precise clinical assessment and decision-making are essential to improve the outcome and the resulting complications. Due to the size and complexity of the data analyzed in TBI cases, computer-aided data processing, analysis, and decision support systems could play an important role. However, developing such systems is challenging due to the heterogeneity of symptoms, varying data quality caused by different spatio-temporal resolutions, and the inherent noise associated with image and signal acquisition. The purpose of this article is to review current advances in developing artificial intelligence-based decision support systems for the diagnosis, severity assessment, and long-term prognosis of TBI complications.

Prognostic Models in Severe Traumatic Brain Injury: A Systematic Review and Meta-analysis

This review aimed to analyze the results of investigations that performed external validation or that compared prognostic models to identify the models and their variations that showed the best performance in predicting mortality, survival, and unfavorable outcome after severe traumatic brain injury. Pubmed, Embase, Scopus, Web of Science, Cumulative Index to Nursing and Allied Health Literature, Google Scholar, TROVE, and Open Grey databases were searched. A total of 1616 studies were identified and screened, and 15 studies were subsequently included for analysis after applying the selection criteria. The Corticosteroid Randomization After Significant Head Injury (CRASH) and International Mission for Prognosis and Analysis of Clinical Trials in Traumatic Brain Injury (IMPACT) models were the most externally validated among studies of severe traumatic brain injury. The results of the review showed that most publications encountered an area under the curve ≥ 0.70. The area under the curve meta-analysis showed similarity between the CRASH and IMPACT models and their variations for predicting mortality and unfavorable outcomes. Calibration results showed that the variations of CRASH and IMPACT models demonstrated adequate calibration in most studies for both outcomes, but without a clear indication of uncertainties in the evaluations of these models. Based on the results of this meta-analysis, the choice of prognostic models for clinical application may depend on the availability of predictors, characteristics of the population, and trauma care services.

Rubiano A, G. Kolias A, Silva Paiva W. Evaluation of Computed Tomography Scoring Systems in the Prediction of Short-Term Mortality in Traumatic Brain Injury Patients from a Low- to Middle-Income Country

The present study aims to evaluate the accuracy of the prognostic discrimination and prediction of the short-term mortality of the Marshall computed tomography (CT) classification and Rotterdam and Helsinki CT scores in a cohort of TBI patients from a low- to middle-income country. This is a post hoc analysis of a previously conducted prospective cohort study conducted in a university-associated, tertiary-level hospital that serves a population of >12 million in Brazil. Marshall CT class, Rotterdam and Helsinki scores, and their components were evaluated in the prediction of 14-day and in-hospital mortality using Nagelkerk's pseudo-R² and area under the receiver operating characteristic curve. Multi-variate regression was performed using known outcome predictors (age, Glasgow Coma Scale, pupil response, hypoxia, hypotension, and hemoglobin values) to evaluate the increase in variance explained when adding each of the CT classification systems. Four hundred forty-seven patients were included. Mean age of the patient cohort was 40 (standard deviation, 17.83) years, and 85.5% were male. Marshall CT class was the least accurate model, showing pseudo-R² values equal to 0.122 for 14-day mortality and 0.057 for in-hospital mortality, whereas Rotterdam CT scores were 0.245 and 0.194 and Helsinki CT scores were 0.264 and 0.229. The AUC confirms the best prediction of the Rotterdam and Helsinki CT scores regarding the Marshall CT class, which presented greater discriminative ability. When associated with known outcome predictors, Marshall CT class and Rotterdam and Helsinki CT scores showed an increase in the explained variance of 2%, 13.4%, and 21.6%, respectively. In this study, Rotterdam and Helsinki scores were more accurate models in predicting short-term mortality. The study denotes a contribution to the process of external validation of the scores and may collaborate with the best risk stratification for patients with this important pathology.

Contribution of CT-Scan Analysis by Artificial Intelligence to the Clinical Care of TBI Patients

The gold standard to diagnose intracerebral lesions after traumatic brain injury (TBI) is computed tomography (CT) scan, and due to its accessibility and improved quality of images, the global burden of CT scan for TBI patients is increasing. The recent developments of automated determination of traumatic brain lesions and medical-decision process using artificial intelligence (AI) represent opportunities to help clinicians in screening more patients, identifying the nature and volume of lesions and estimating the patient outcome. This short review will summarize what is ongoing with the use of AI and CT scan for patients with TBI.

Predictive Value of Rotterdam Score and Marshall Score in Traumatic Brain Injury: A Contemporary Review

This article conducts a contemporary comparative review of the medical literature to update and establish evidence as to which framework among Rotterdam and Marshall computed tomography (CT)-based scoring systems predicts traumatic brain injury (TBI) outcomes better. The scheme followed was following the recommendations of the Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines for literature search. The search started on August 15, 2020 and ended on December 31, 2020. The combination terms used were Medical Subject Headings terms, combination keywords, and specific words used for describing various pathologies of TBI to identify the most relevant article in each database. PICO question to guide the search strategy was: “what is the use of Marshall (I) versus Rotterdam score (C) in TBI patients (P) for mortality risk stratification (O).” The review is based on 46 references which included a full review of 14 articles for adult TBI patients and 6 articles for pediatric TBI articles comparing Rotterdam and Marshall CT scores. The review includes 8,243 patients, of which 2,365 were pediatric and 5,878 were adult TBI patients. Marshall CT classification is not ordinal, is more descriptive, has better inter-rater reliability, and poor performance in a specific group of TBI patients requiring decompressive craniectomy. Rotterdam CT classification is ordinal, has better discriminatory power, and a better description of the dynamics of intracranial changes. The two scoring systems are complimentary. A combination of clinical parameters, severity, ischemic and hemodynamic parameters, and CT scoring system could predict the prognosis of TBI patients with significant accuracy. None of the classifications has good evidence for use in pediatric patients.

Utility of the Marshall & Rotterdam Classification Scores in Predicting Outcomes in Trauma Patients

BACKGROUND

Traumatic Brain Injury (TBI) is a leading cause of mortality in the trauma population. Accurate prognosis remains a challenge. Two common Computed Tomography (CT)-based prognostic models include the Marshall Classification and the Rotterdam CT Score. This study aims to determine the utility of the Marshall and Rotterdam scores in predicting mortality for adult patients in coma with severe TBI.

METHOD

Retrospective review of our Level 1 Trauma Center's registry for patients ≥ 18 years of age with blunt TBI and a Glasgow Coma Scale (GCS) of 3-5, with no other significant injuries. Admission Head CT was evaluated for the presence of extra-axial blood (SDH, EDH, SAH, IVH), intra-axial blood (contusions, diffuse axonal injury), midline shift and mass effect on basilar cisterns. Rotterdam and Marshall scores were calculated for all patients; subsequently patients were divided into two groups according to their score (< 4, ≥ 4).

RESULTS

106 patients met inclusion criteria; 75.5% were males (n = 80) and 24.5% females (n = 26). The mean age was 52. The odds ratio (OR) of dying from severe TBI for patients in coma with a Rotterdam score of ≥ 4 compared to < 4 was OR = 17 (P < 0.05). The odds of dying from severe TBI for patients in coma with a Marshall score of ≥ 4 versus < 4 was OR = 11 (P < 0.05).

CONCLUSION

Higher scores in the Marshall classification and the Rotterdam system are associated with increased odds of mortality in adult patients in come from severe TBI after blunt injury. The results of our study support these scoring systems and revealed that a cutoff score of < 4 was associated with improved survival.

Admission Levels of Interleukin 10 and Amyloid β 1-40 Improve the Outcome Prediction Performance of the Helsinki Computed Tomography Score in Traumatic Brain Injury

Background: Blood biomarkers may enhance outcome prediction performance of head computed tomography scores in traumatic brain injury (TBI).

Objective: To investigate whether admission levels of eight different protein biomarkers can improve the outcome prediction performance of the Helsinki computed tomography score (HCTS) without clinical covariates in TBI.

Materials and methods: Eighty-two patients with computed tomography positive TBIs were included in this study. Plasma levels of β-amyloid isoforms 1–40 (Aβ40) and 1–42 (Aβ42), glial fibrillary acidic protein, heart fatty acid-binding protein, interleukin 10 (IL-10), neurofilament light, S100 calcium-binding protein B, and total tau were measured within 24 h from admission. The patients were divided into favorable (Glasgow Outcome Scale—Extended 5–8, n = 49) and unfavorable (Glasgow Outcome Scale—Extended 1–4, n = 33) groups. The outcome was assessed 6–12 months after injury. An optimal predictive panel was investigated with the sensitivity set at 90–100%.

Results: The HCTS alone yielded a sensitivity of 97.0% (95% CI: 90.9–100) and specificity of 22.4% (95% CI: 10.2–32.7) and partial area under the curve of the receiver operating characteristic of 2.5% (95% CI: 1.1–4.7), in discriminating patients with favorable and unfavorable outcomes. The threshold to detect a patient with unfavorable outcome was an HCTS > 1. The three best individually performing biomarkers in outcome prediction were Aβ40, Aβ42, and neurofilament light. The optimal panel included IL-10, Aβ40, and the HCTS reaching a partial area under the curve of the receiver operating characteristic of 3.4% (95% CI: 1.7–6.2) with a sensitivity of 90.9% (95% CI: 81.8–100) and specificity of 59.2% (95% CI: 40.8–69.4).

Conclusion: Admission plasma levels of IL-10 and Aβ40 significantly improve the prognostication ability of the HCTS after TBI.

The prognostic value of the Koret CT score in dogs following traumatic brain injury

Traumatic brain injury (TBI) is a common condition in veterinary medicine with relatively high mortality rate. Recently, a study that correlated abnormal computed tomography (CT) findings with outcome in dogs with head trauma established a prognostic scoring system termed Koret CT score (KCTS). The purpose of this study was to evaluate the accuracy of the KCTS in making short- and long-term prognosis in dogs presented within 72 h of TBI. Thirty-five dogs that were admitted to a hospital during 2010-2019 with TBI and were CT-scanned within 72 h of injury were included in the study. Retrospectively collected data included signalment, modified Glasgow Coma Scale score (MGCS), CT findings, and outcome, i.e. short-term (defined as 10 days) and long-term (6 months) survival. CT images were reviewed and the KCTS was calculated for all dogs. Association between KCTS and outcome was examined. A significant negative association was found between KCTS and both short- and long-term survival. The area under receiver operating characteristic curve for KCTS for short- and long-term survival was 0.9 and 0.87, respectively. Furthermore, the probability of survival in the short term was predicated by the KCTS in an almost linear fashion and a score of 3 points or less on the KCTS was associated with survival with 85% sensitivity and 100% specificity. These results validate the prognostic value of the KCTS in dogs with TBI and provide a complementary tool for serial clinical and neurological evaluation.

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

Traumatic brain injuries are complex situations in which the emergency medical services must quickly determine the risk of deterioration using minimal diagnostic methods. The aim of this study is to analyze whether the use of early warning scores can help with decision-making in these dynamic situations by determining the patients who need the intensive care unit. A prospective, multicentric cohort study without intervention was carried out on traumatic brain injury patients aged over 18 given advanced life support and taken to the hospital. Our study included a total of 209 cases. The total number of intensive-care unit admissions was 50 cases (23.9%). Of the scores analyzed, the National Early Warning Score2 was the best result presented with an area under the curve of 0.888 (0.81–0.94; p < 0.001) and an odds ratio of 25.4 (95% confidence interval (CI):11.2–57.5). The use of early warning scores (and specifically National Early Warning Score2) can help the emergency medical services to differentiate traumatic brain injury patients with a high risk of deterioration. The emergency medical services should use the early warning scores routinely in all cases for the early detection of high-risk situations.

Baseline Predictors of Survival, Neurological Recovery, Cognitive Function, Neuropsychiatric Outcomes, and Return to Work in Patients after a Severe Traumatic Brain Injury: an Updated Review

Introduction

Severe traumatic brain injury (sTBI) is a common cause of death and disability worldwide, with long-term squeal among survivors that include cognitive deficits, psychosocial and neuropsychiatric dysfunction, failure to return to pre-injury levels of work, school and inter-personal relationships, and overall reduced quality of and satisfaction with life.

Aim

The aim of this work is to review the current literature on baseline predictors of outcomes in adults post sTBI.

Method

Most of available literature on baseline predictors of outcomes in adults post sTBI were reviewed and summarized in this work.

Results

Currently, a sizeable number of composite predictors of mortality and overall function exists; however, these instruments tend to over-estimate poor outcomes and fail to address issues like cognition, psychosocial/ neuropsychiatric dysfunction, and return to work or school.

Conclusion

This article reviews currently-identified predictors of all these outcomes.