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.
Humans ; Male ; Young Adult ; Adult ; Middle Aged ; Aged ; Female ; Cohort Studies ; Tomography, X-Ray Computed/methods* ; Brain Injuries, Traumatic/diagnosis* ; Brain Injuries ; Prognosis ; Glasgow Coma Scale ; Wounds, Nonpenetrating/diagnostic imaging* ; Brain

Objective: To analyze the correlation of bispectral index (BIS) with the prognosis of patients with acute severe carbon monoxide poisoning (ASCMP) and its predictive value of adverse outcomes. Methods: In March 2021, 106 ASCMP patients who were treated in Harrison International Peace Hospital Affiliated to Hebei Medical University from January 2019 to December 2020 were taken as research objects. All patients underwent 24-hour BIS monitoring after admission, and were divided into good prognosis group (n=75) and poor prognosis group (n=31) according to the prognosis of the patients' cranial nerve function after 60 d. The general conditions, Acute Physiology and Chronic Health Evaluation Ⅱ (APACHEⅡ) score, Glasgow Coma Scale (GCS) score at admission and 24-hour BIS mean were compared between the two groups. Pearson correlation analysis was used to analyze the correlations between the 24-hour BIS mean and GCS score at admission, APACHEⅡ score and coma time. The receiver operating characteristic (ROC) curve was drawn to analyze the predictive value of 24-hour BIS mean, GCS score at admission, APACHEⅡ score and coma time on adverse outcome of ASCMP patients. Results: The coma time and APACHEⅡ score of the patients in the poor prognosis group were significantly higher than those in the good prognosis group, the GCS score at admission and 24-hour BIS mean were significantly lower than those in the good prognosis group (P<0.05) . Pearson correlation analysis showed that the 24-hour BIS mean was positively correlated with the GCS score at admission, and negatively correlated with the APACHEⅡ score, coma time (r=0.675, -0.700, -0.565, P<0.001) . The 24-hour BIS mean had the highest predictive value for adverse outcome of ASCMP patients, with a cut-off value of 74, the area under the curve was 0.883 (95%CI: 0.814-0.951, P<0.001) , and the sensitivity and specificity were 73.3% and 87.1%, respectively. Conclusion: The 24-hour BIS mean has a good correlation with the acute brain nerve injury, the severity of the disease and coma time of patients with ASCMP. And it has a high predictive value for the adverse outcome in patients with ASCMP.
APACHE ; Brain Injuries ; Carbon Monoxide Poisoning/diagnosis* ; Coma ; Humans ; Prognosis ; ROC Curve ; Retrospective Studies ; Sensitivity and Specificity

Acute methanol poisoning harms the optic nerve and central nervous system, can cause irreversible damage, even coma or death in severe cases. This article reported four cases of methanol poisoning. 3 patients mistakenly ingested industrial alcohol containing methanol, the most serious patient suffered from coma, vision loss and other symptoms, the blood methanol concentration was 869.3 μg/ml. Another patient was poisoning caused by inhalation of methanol, with symptoms such as total blindness in the right eye and decreased visual acuity in the left eye. After active supportive treatment, 2 patients had partial recovery of visual acuity, and 2 patients had no sequelae. This article discussed the clinical features, treatment and prognosis of optic nerve damage caused by methanol poisoning, in order to raise awareness of this disease.
Coma ; Follow-Up Studies ; Humans ; Methanol ; Optic Nerve ; Optic Nerve Injuries ; Poisoning/therapy*

Objective To investigate the pathogen distribution,imaging characteristics,and risk factors of pulmonary infection with multi-drug resistant organism (MDRO) in patients with severe craniocerebral injury,and establish and verify the risk prediction model. Methods A total of 230 patients with severe craniocerebral injury complicated with pulmonary infection were collected retrospectively.According to the 7∶3 ratio,they were randomly assigned into a modeling group (161 patients) and a validation group (69 patients).The risk factors of MDRO pulmonary infection were predicted with the data of the modeling group for the establishment of the risk prediction model.The data of the validation group was used to validate the performance of the model. Results Among the 230 patients,68 patients developed MDRO pulmonary infection.The isolated drug-resistant bacteria mainly included multi-drug resistant Acinetobacter baumannii,multi-drug resistant Klebsiella pneumoniae,multi-drug resistant Pseudomonas aeruginosa,and methicillin-resistant Staphylococcus aureus,which accounted for 45.21%,23.29%,16.44%,and 15.07%,respectively.The imaging characteristics included pleural effusion,lung consolidation,and ground-glass shadow,which accounted for 72.06%,63.24%,and 45.59%,respectively.Multivariate Logistic regression analysis showed that the independent risk factors for MDRO pulmonary infection included age ≥60 years (P=0.003),history of diabetes (P=0.021),history of chronic obstructive pulmonary disease (P=0.038),mechanical ventilation ≥7 d (P=0.001),transfer from other hospitals (P=0.008),and coma (P=0.002).A risk scoring model was established with the β value (rounded to the nearest integer) corresponding to each index in the regression equation.Specifically,the β values of age ≥60 years,history of diabetes,history of chronic obstructive pulmonary disease,mechanical ventilation ≥7 d,transfer from other hospitals,and coma were 1,1,1,2,2,and 1,respectively (value ≥4 indicated a high-risk population).The areas under the receiver operating characteristic curve of the modeling group and validation group were 0.845 and 0.809,respectively. Conclusions Multi-drug resistant Acinetobacter baumannii is the most common pathogen of MDRO pulmonary infection in patients with severe craniocerebral injury.Pleural effusion,lung consolidation,and ground-glass shadow were the most common imaging characteristics.The established risk model has high discriminant validity in both the modeling group and the validation group.
Coma ; Craniocerebral Trauma ; Drug Resistance, Multiple, Bacterial ; Humans ; Methicillin-Resistant Staphylococcus aureus ; Middle Aged ; Pleural Effusion ; Pneumonia ; Pulmonary Disease, Chronic Obstructive ; Retrospective Studies

In subarachnoid hemorrhage following traumatic brain injury (TBI), the high intracisternal pressure drives the cerebrospinal fluid into the brain parenchyma, causing cerebral edema. Basal cisternostomy involves opening the basal cisterns to atmospheric pressure and draining cerebrospinal fluid in an attempt to reverse the edema. We describe a case of basal cisternostomy combined with decompressive craniectomy. A 35-year-old man with severe TBI following a road vehicle accident presented with acute subdural hematoma, Glasgow coma scale score of 6, fixed pupils and no corneal response. Opening of the basal cisterns and placement of a temporary cisternal drain led to immediate relaxation of the brain. The patient had a Glasgow coma scale score of 15 on postoperative day 6 and was discharged on day 10. We think basal cisternostomy is a feasible and effective procedure that should be considered in the management of TBI.
Adult ; Brain ; Brain Edema ; Brain Injuries, Traumatic/surgery* ; Decompressive Craniectomy/methods* ; Glasgow Coma Scale ; Humans ; Male ; Treatment Outcome

PURPOSE: Traumatic brain injury (TBI) generally causes mortality and disability, particularly in children. Machine learning (ML) is a computer algorithm, applied as a clinical prediction tool. The present study aims to assess the predictability of ML for the functional outcomes of pediatric TBI. METHODS: A retrospective cohort study was performed targeting children with TBI who were admitted to the trauma center of southern Thailand between January 2009 and July 2020. The patient was excluded if he/she (1) did not undergo a CT scan of the brain, (2) died within the first 24 h, (3) had unavailable complete medical records during admission, or (4) was unable to provide updated outcomes. Clinical and radiologic characteristics were collected such as vital signs, Glasgow coma scale score, and characteristics of intracranial injuries. The functional outcome was assessed using the King's Outcome Scale for Childhood Head Injury, which was thus dichotomized into favourable outcomes and unfavourable outcomes: good recovery and moderate disability were categorized as the former, whereas death, vegetative state, and severe disability were categorized as the latter. The prognostic factors were estimated using traditional binary logistic regression. By data splitting, 70% of data were used for training the ML models and the remaining 30% were used for testing the ML models. The supervised algorithms including support vector machines, neural networks, random forest, logistic regression, naive Bayes and k-nearest neighbor were performed for training of the ML models. Therefore, the ML models were tested for the predictive performances by the testing datasets. RESULTS: There were 828 patients in the cohort. The median age was 72 months (interquartile range 104.7 months, range 2-179 months). Road traffic accident was the most common mechanism of injury, accounting for 68.7%. At hospital discharge, favourable outcomes were achieved in 97.0% of patients, while the mortality rate was 2.2%. Glasgow coma scale score, hypotension, pupillary light reflex, and subarachnoid haemorrhage were associated with TBI outcomes following traditional binary logistic regression; hence, the 4 prognostic factors were used for building ML models and testing performance. The support vector machine model had the best performance for predicting pediatric TBI outcomes: sensitivity 0.95, specificity 0.60, positive predicted value 0.99, negative predictive value 1.0; accuracy 0.94, and area under the receiver operating characteristic curve 0.78. CONCLUSION The ML algorithms of the present study have a high sensitivity; therefore they have the potential to be screening tools for predicting functional outcomes and counselling prognosis in general practice of pediatric TBIs.
Bayes Theorem ; Brain Injuries, Traumatic/therapy* ; Child ; Female ; Glasgow Coma Scale ; Humans ; Machine Learning ; Prognosis ; Retrospective Studies

PURPOSE: Patients' gender, which can be one of the most important determinants of traumatic brain injury (TBI) outcomes, is also likely to interact with many other outcome variables of TBI. This multicenter descriptive study investigated gender differences in epidemiological, clinical, treatment, mortality, and variable characteristics in adult TBI patients. METHODS: The selection criteria were defined as patients who had been diagnosed with TBI and were admitted to the hospital between January 1, 2016 and December 31, 2018. A total of 4468 adult TBI patients were enrolled at eight University Hospitals. Based on the list of enrolled patients, the medical records of the patients were reviewed and they were registered online at each hospital. The registered patients were classified into three groups according to the Glasgow coma scale (GCS) score: mild (13-15), moderate (9-12), and severe (3-8), and the differences between men and women in each group were investigated. The risk factors of moderated and severe TBI compared to mild TBI were also investigated. RESULTS: The study included 3075 men and 1393 women and the proportion of total males was 68.8%. Among all the TBI patients, there were significant differences between men and women in age, past history, and GCS score. While the mild and severe TBI groups showed significant differences in age, past history, and clinical symptoms, the moderate TBI group showed significant differences in age, past history, cause of justice, and diagnosis. CONCLUSION To the best of our knowledge, this multicenter study is the first to focus on gender differences of adult patients with TBI in Korea. This study shows significant differences between men and women in many aspects of adult TBI. Therefore, gender differences should be strongly considered in TBI studies.
Adult ; Brain Injuries ; Brain Injuries, Traumatic/epidemiology* ; Female ; Glasgow Coma Scale ; Humans ; Male ; Prospective Studies ; Sex Factors

BACKGROUND AND OBJECTIVES. Several reports have shown that coexistence of diabetes mellitus and COVID-19 is one of the risk factors for poor outcome and increased mortality. Rapid metabolic deterioration with development of diabetic ketoacidosis (DKA) or hyperglycemic hyperosmolar syndrome (HHS) may result due to the acute insulin secretory capacity loss, stress condition and the cytokine storm. In this review, we aim to describe the prevalence of hyperglycemic crises(DKA/HHS) in patients with COVID-19 infection as well as their clinical outcomes. METHODS. An intensive search was done using the WebMD, PubMed, Medline and Google Scholar databases for articles published between December 2019 to October 2020 that identified the number of patients who developed DKA and/or HHS among those who were admitted for COVID-19. Their clinical outcomes were likewise described. RESULTS. This review included 4 articles in which individual quality was assessed. A total of 1282 patients were admitted for COVID-19 and the prevalence of DKA was 1.32%. HHS was not reported in any of the studies. Five (29.4%) of the patients with DKA and COVID-19 died and 12 (70.6%) recovered. CONCLUSIONS. A significant number of COVID-19 patients developed DKA and it is associated with a high mortality rate. This reimposes the need for an appropriate algorithm for the optimal management of concomitant COVID 19 and hyperglycemic crises to avoid morbidity and mortality. Additionally, there is paucity of large-scale studies describing the prevalence of DKA/HHS in patients with COVID-19.
Diabetic Ketoacidosis ; COVID-19 ; Water-Electrolyte Imbalance ; Acid-Base Imbalance ; Coma

Myxedema coma is associated with decreased mental status and hyponatremia among patients with diagnosed or undiagnosed hypothyroidism. The diagnosis is challenging in the absence of universally accepted diagnostic criteria, but should be considered as a differential even in cases with competing established diagnoses. All patients should receive intensive care level treatment. Even with optimal treatment, mortality is very high.
Myxedema ; Coma

OBJECTIVE: To study the value of amplitude-integrated EEG (aEEG), Full Outline of Unresponsiveness (FOUR), and Glasgow Coma Scale (GCS) in evaluating the prognosis of children with disturbance of consciousness in the pediatric intensive care unit (PICU). METHODS: A total of 164 children with disturbance of consciousness who were admitted to the PICU of Children's Hospital Affiliated to Soochow University were enrolled as subjects. According to prognosis, they were divided into a poor prognosis group with 111 children and a good prognosis group with 53 children. The results of aEEG monitoring, FOUR score, and GCS score on days 1 and 5 of admission were collected. The association between evaluation methods and prognosis was analyzed. The receiver operating characteristic (ROC) curve was used to evaluate the value of aEEG, FOUR, and GCS in predicting prognosis. RESULTS: The children with no improvement or abnormal aggravation of aEEG on day 5 tended to have a poor prognosis. The results of aEEG was positively correlated with prognosis (r=0.689, P<0.001), and FOUR and GCS were negatively correlated with prognosis (r=-0.655 and -0.554 respectively, P<0.001). The areas under the ROC curve (AUC) of aEEG, FOUR, and GCS were 0.894, 0.903, and 0.840 respectively, and there was no significant difference in the AUC between the three indices (P>0.05), while aEEG combined with FOUR had an AUC of 0.945, which was significantly larger than that of each index alone (P<0.05). CONCLUSIONS Both aEEG and FOUR can be used as effective tools to predict the prognosis of children with disturbance of consciousness, and a combination of aEEG and FOUR can improve the predictive value.
Child ; Consciousness ; Electroencephalography ; Glasgow Coma Scale ; Humans ; Prognosis ; ROC Curve