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Aims To design a model to predict the early prognosis of patients with traumatic brain injury (TBI) based on parameters that can be quickly obtained in emergency conditions from medical history, physical examination, and supplementary examinations. Methods The medical records of TBI patients who were hospitalized in two medical institutions between June 2015 and June 2021 were collected and analyzed. Patients were divided into the training set, validation set, and testing set. The possible predictive indicators were screened after analyzing the data of patients in the training set. Then prediction models were found based on the possible predictive indicators in the training set. Data of patients in the validation set and the testing set was provided to validate the predictive values of the models. Results Age, Glasgow coma scale score, Apolipoprotein E genotype, damage area, serum C‐reactive protein, and interleukin‐8 (IL‐8) levels, and Marshall computed tomography score were found associated with early prognosis of TBI patients. The accuracy of the early prognosis prediction model (EPPM) was 80%, and the sensitivity and specificity of the EPPM were 78.8% and 80.8% in the training set. The accuracy of the EPPM was 79%, and the sensitivity and specificity of the EPPM were 66.7% and 86.2% in the validation set. The accuracy of the early EPPM was 69.1%, and the sensitivity and specificity of the EPPM were 67.9% and 77.8% in the testing set. Conclusion Prediction models integrating general information, clinical manifestations, and auxiliary examination results may provide a reliable and rapid method to evaluate and predict the early prognosis of TBI patients. By analyzing the admission information and examination results of patients with traumatic brain injury (TBI), the factors that may affect the early prognosis of patients with TBI were obtained. Then these factors are combined by mathematics to establish a prediction model to implement the effective prediction of early prognosis of patients with TBI. The results show that this idea is feasible.

Trauma is one of the most important issues of most healthcare systems accompanying with head trauma in the most cases. We sought to determine the scoring system and initial Computed Tomography (CT) findings predicting the death at hospital discharge (early death) in patients with traumatic brain injury based on Marshall and Rotterdam CT scores. This is a cross sectional study on traumatic neurosurgical patients with mild-to-severe traumatic brain injury admitted to the emergency department of Emam Reza Hospital, Birjand University of Medical Sciences. Patients≥18 years old with TBI during last 24 hours with GCS≤13 were included and exclusion criteria were multiple trauma, penetrating injuries, previous history of anticoagulant therapy, pregnancy, not willingness for participation. Their initial CT and status at hospital discharge, one and three months (dead or alive) were reviewed, and both CT scores were calculated. We examined whether each score is related to death using SPSS11 by The Mann–Whitney U at the level of p≤0.05. Overall, 98 patients were included. Mean age was 43.52±21.29. Most patients were male (63.3%). Mean Marshall and Rotterdam CT scores were 3.2±1.3 and 2.5±1. The mortality at two weeks, one moth and three months were 19.4%, 20.4%, and 20.4%. Rotterdam CT score was significantly different based on type of hematoma. Median GCS score in alive and dead patients on 2 weeks were 10 and 4 (p=0.0001), at one month were 10 and 4 (p=0.0001), and at three months were 10 and 4 (p=0.0001). The median Marshall CT score on 2 weeks were 2 and 4 (p=0.0001), at one month were 2 and 4 (p=0.0001), and at three months were 2 and 4 (p=0.0001). The median Rotterdam CT score on 2 weeks were 2 and 4 (p=0.0001), at one month were 2 and 3 (p=0.001), and at three months were 2 and 3 (p=0.001). The Rotterdam CT score was significantly correlated with mortality at two weeks, one month and three months (p=0.004, p=0.001, and p=0.001, respectively). The Marshall CT score was not significantly correlated with mortality at any time. The Rotterdam CT score was more accurate for prediction of mortality on 2 weeks (ROC80.9), at one month (ROC80.7), and at three months were (ROC80.7) than The Rotterdam CT score (ROC 76, 74.1, and 74.1, respectively). This study concluded that The Marshall CT score was more accurate for prediction of mortality on 2 weeks, at one month, and at three months were than The Marshall CT score with higher ROC. The correlation of the Rotterdam CT score with mortality was significant.

Background Traumatic brain injuries (TBI) are associated with high risk of morbidity and mortality. Early outcome prediction in patients with TBI require reliable data input and stable prognostic models. The aim of this investigation was to analyze different CT classification systems and prognostic calculators in a representative population of TBI-patients, with known outcomes, in a neurointensive care unit (NICU), to identify the most suitable CT scoring system for continued research. Materials and methods We retrospectively included 158 consecutive patients with TBI admitted to the NICU at a level 1 trauma center in Sweden from 2012 to 2016. Baseline data on admission was recorded, CT scans were reviewed, and patient outcome one year after trauma was assessed according to Glasgow Outcome Scale (GOS). The Marshall classification, Rotterdam scoring system, Helsinki CT score and Stockholm CT score were tested, in addition to the IMPACT and CRASH prognostic calculators. The results were then compared with the actual outcomes. Results Glasgow Coma Scale score on admission was 3–8 in 38%, 9–13 in 27.2%, and 14–15 in 34.8% of the patients. GOS after one year showed good recovery in 15.8%, moderate disability in 27.2%, severe disability in 24.7%, vegetative state in 1.3% and death in 29.7%. When adding the variables from the IMPACT base model to the CT scoring systems, the Stockholm CT score yielded the strongest relationship to actual outcome. The results from the prognostic calculators IMPACT and CRASH were divided into two subgroups of mortality (percentages); ≤50% (favorable outcome) and > 50% (unfavorable outcome). This yielded favorable IMPACT and CRASH scores in 54.4 and 38.0% respectively. Conclusion The Stockholm CT score and the Helsinki score yielded the closest relationship between the models and the actual outcomes in this consecutive patient series, representative of a NICU TBI-population. Furthermore, the Stockholm CT score yielded the strongest overall relationship when adding variables from the IMPACT base model and would be our method of choice for continued research when using any of the current available CT score models.

Background Marshall computed tomographic (CT) classification is widely used as a predictor of outcome. However, this grading system lacks the following variables, which are found to be useful predictors: subarachnoid/intraventricular hemorrhage, extradural hematoma, and extent of basal cistern compression. A new classification called the Rotterdam grading system, incorporating the above variables, was proposed later. In the original paper, this system was found to have superior discrimination as compared to Marshall grading, however, Rotterdam grading has not been validated widely. We aimed to compare the discriminatory power of both grading systems. Methods This is a prospective study of patients with moderate and severe TBI (Glasgow coma scale (GCS) 3–12) who presented to our casualty. All the patients were followed up for 2 weeks to determine early mortality. The discriminatory power of each grading system was determined using area under the receiver operating characteristic curve (AUC). Results A total of 134 patients, mean age 38.3 (±15.7) years, were recruited for study. The overall mortality was 11.2 %. The mean GCS of these patients was 9.6 (±2.3). There was good correlation between Marshall and Rotterdam grading, r  = 0.68 (significant at 0.01 level). The Marshall CT classification had reasonable discrimination (AUC - 0.707), and Rotterdam grading had good discrimination (AUC - 0.681). Conclusions Both Marshal and Rotterdam grading systems are good in predicting early mortality after moderate and severe TBI. As the Rotterdam system also includes additional variables like subarachnoid hemorrhage, it may be preferable, particularly in patients with diffuse injury.

Abstract BACKGROUND: Prediction of clinical course and outcome after severe traumatic brain injury (TBI) is important. OBJECTIVE: To examine whether clinical scales (Glasgow Coma Scale [GCS], Injury Severity Score [ISS], and Acute Physiology and Chronic Health Evaluation II [APACHE II]) or radiographic scales based on admission computed tomography (Marshall and Rotterdam) were associated with intensive care unit (ICU) physiology (intracranial pressure [ICP], brain tissue oxygen tension [PbtO2]), and clinical outcome after severe TBI. METHODS: One hundred one patients (median age, 41.0 years; interquartile range [26-55]) with severe TBI who had ICP and PbtO2 monitoring were identified. The relationship between admission GCS, ISS, APACHE II, Marshall and Rotterdam scores and ICP, PbtO2, and outcome was examined by using mixed-effects models and logistic regression. RESULTS: Median (25%–75% interquartile range) admission GCS and APACHE II without GCS scores were 3.0 (3–7) and 11.0 (8–13), respectively. Marshall and Rotterdam scores were 3.0 (3–5) and 4.0 (4–5). Mean ICP and PbtO2 during the patients' ICU course were 15.5 ± 10.7 mm Hg and 29.9 ± 10.8 mm Hg, respectively. Three-month mortality was 37.6%. Admission GCS was not associated with mortality. APACHE II (P = .003), APACHE-non-GCS (P = .004), Marshall (P < .001), and Rotterdam scores (P < .001) were associated with mortality. No relationship between GCS, ISS, Marshall, or Rotterdam scores and subsequent ICP or PbtO2 was observed. The APACHE II score was inversely associated with median PbtO2 (P = .03) and minimum PbtO2 (P = .008) and had a stronger correlation with amount of time of reduced PbtO2. CONCLUSION: Following severe TBI, factors associated with outcome may not always predict a patient's ICU course and, in particular, intracranial physiology.