Background: The novel coronavirus disease 2019 (COVID-19) continues to wreak havoc worldwide. This study assessed the ability of chest computed tomography (CT) severity score (CSS) to predict intensive care unit (ICU) admission and mortality in patients with COVID-19 pneumonia. Materials and Methods: A total of 192 consecutive patients with COVID-19 pneumonia aged more than 20 years and typical CT findings and reverse-transcription polymerase chain reaction positive admitted in a tertiary hospital were included. Clinical symptoms at admission and short-term outcome were obtained. A semi-quantitative scoring system was used to evaluate the parenchymal involvement. The association between CSS, disease severity, and outcomes were evaluated. Prediction of CSS was assessed with the area under the receiver-operating characteristic (ROC) curves. Results: The incidence of admission to ICU was 22.8% in men and 14.1% in women. CSS was related to ICU admission and mortality. Areas under the ROC curves were 0.764 for total CSS.Using a stepwise binary logistic regression model, gender, age, oxygen saturation, and CSS had a significant independent relationship with ICU admission and death. Patients with CSS ≥12.5 had about four-time risk of ICU admission and death (odds ratio 1.66, 95% confidence interval 1.66 – 9.25). The multivariate regression analysis showed the superiority of CSS over other clinical information and co-morbidities. Conclusion CSS was a strong predictor of progression to ICU admission and death and there was a substantial role of non-contrast chest CT imaging in the presence of typical features for COVID-19 pneumonia as a reliable predictor of clinical severity and patient’s outcome.

Background: The novel coronavirus disease 2019 (COVID-19) continues to wreak havoc worldwide. This study assessed the ability of chest computed tomography (CT) severity score (CSS) to predict intensive care unit (ICU) admission and mortality in patients with COVID-19 pneumonia. Materials and Methods: A total of 192 consecutive patients with COVID-19 pneumonia aged more than 20 years and typical CT findings and reverse-transcription polymerase chain reaction positive admitted in a tertiary hospital were included. Clinical symptoms at admission and short-term outcome were obtained. A semi-quantitative scoring system was used to evaluate the parenchymal involvement. The association between CSS, disease severity, and outcomes were evaluated. Prediction of CSS was assessed with the area under the receiver-operating characteristic (ROC) curves. Results: The incidence of admission to ICU was 22.8% in men and 14.1% in women. CSS was related to ICU admission and mortality. Areas under the ROC curves were 0.764 for total CSS.Using a stepwise binary logistic regression model, gender, age, oxygen saturation, and CSS had a significant independent relationship with ICU admission and death. Patients with CSS ≥12.5 had about four-time risk of ICU admission and death (odds ratio 1.66, 95% confidence interval 1.66 – 9.25). The multivariate regression analysis showed the superiority of CSS over other clinical information and co-morbidities. Conclusion CSS was a strong predictor of progression to ICU admission and death and there was a substantial role of non-contrast chest CT imaging in the presence of typical features for COVID-19 pneumonia as a reliable predictor of clinical severity and patient’s outcome.

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