The objective of this study is to select one of the seven available clinical decision rules for minor head injury, for managing Iranian patients. This was a prospective cohort study evaluating medium- or high-risk minor head injury patients presenting to the Emergency Department. Patients with minor head trauma who were eligible for brain imaging based on seven available clinical decision rules (National Institute for Health and Clinical Excellence (NICE), National Emergency X-Radiography Utilization Study (NEXUS)-II, Neurotraumatology Committee of the World Federation of Neurosurgical Societies (NCWFNS), New Orleans, American College of Emergency Physicians (ACEP) Guideline, Scandinavian, and Canadian computed tomography (CT) head rule) were selected. Subjects were underwent a non-contrast axial spiral head CT scan. The outcome was defined as abnormal and normal head CT scan. Univariate analysis and stepwise linear regression were applied to show the best combination of risk factors for detecting CT scan abnormalities. Five hundred patients with minor head trauma were underwent brain CT scan. The following criteria were derived by stepwise linear regression: Glasgow Coma Scale (GCS) less than 15, confusion, signs of basal skull fracture, drug history of warfarin, vomiting more than once, loss of consciousness, focal neurologic deficit, and age over 65 years. This model has 86.15 % (75.33-93.45 %) sensitivity and 46.44 % (46.67-51.25 %) specificity in detecting minor head injury patients with CT scan abnormalities (95 % confidence interval). Of seven decision rules, only the Canadian CT Head Rule possesses seven of the eight high-risk factors associated with abnormal head CT results which were identified by this study. This study underlines the Canadian CT Head Rule's utility in Iranian minor head injury patients. Our study encourages researchers to evaluate available guidelines in different communities.

Background Narrowing a large set of features to a smaller one can improve our understanding of the main risk factors for in-hospital mortality in patients with COVID-19. This study aimed to derive a parsimonious model for predicting overall survival (OS) among re-infected COVID-19 patients using machine-learning algorithms. Methods The retrospective data of 283 re-infected COVID-19 patients admitted to twenty-six medical centers (affiliated with Shiraz University of Medical Sciences) from 10 June to 26 December 2020 were reviewed and analyzed. An elastic-net regularized Cox proportional hazards (PH) regression and model approximation via backward elimination were utilized to optimize a predictive model of time to in-hospital death. The model was further reduced to its core features to maximize simplicity and generalizability. Results The empirical in-hospital mortality rate among the re-infected COVID-19 patients was 9.5%. In addition, the mortality rate among the intubated patients was 83.5%. Using the Kaplan-Meier approach, the OS (95% CI) rates for days 7, 14, and 21 were 87.5% (81.6-91.6%), 78.3% (65.0-87.0%), and 52.2% (20.3-76.7%), respectively. The elastic-net Cox PH regression retained 8 out of 35 candidate features of death. Transfer by Emergency Medical Services (EMS) (HR=3.90, 95% CI: 1.63-9.48), SpO2≤85% (HR=8.10, 95% CI: 2.97-22.00), increased serum creatinine (HR=1.85, 95% CI: 1.48-2.30), and increased white blood cells (WBC) count (HR=1.10, 95% CI: 1.03-1.15) were associated with higher in-hospital mortality rates in the re-infected COVID-19 patients. Conclusion The results of the machine-learning analysis demonstrated that transfer by EMS, profound hypoxemia (SpO2≤85%), increased serum creatinine (more than 1.6 mg/dL), and increased WBC count (more than 8.5 (×109 cells/L)) reduced the OS of the re-infected COVID-19 patients. We recommend that future machine-learning studies should further investigate these relationships and the associated factors in these patients for a better prediction of OS.

Background: Various studies investigated the effects of benzodiazepines on insulin and blood glucose levels and provided contradictory results. The present study aimed to evaluate the clinical effects of benzodiazepine poisoning on hypoglycemia. Methods: This retrospective cross-sectional study (from 22/June/2018 to 22/December/2018) was conducted on all medical records of adult patients with benzodiazepine poisoning who were referred to Ali-Asghar Hospital. The required data were collected using a data-gathering form and then analyzed. Results: In total, 61 patients were enrolled in this study. Furthermore, 19 (31.2%) patients developed hypoglycemia. Besides, 50 (82%) patients used benzodiazepine for a suicide attempt, i.e. higher in patients with hypoglycemia (P<0.0001). Multivariate logistic regression test data indicated that benzodiazepine consumption for suicide attempt (OR=47.978, P=0.001, 95%CI, 5.313-433.277), and the respiratory rate at the time of suicide (OR=0.549, P=0.023, 95%CI, 0.328-0.920) were predictive factors for hypoglycemia in patients with benzodiazepine poisoning. Conclusion: Our study data suggested that 31% of patients who were poisoned with benzodiazepines developed hypoglycemia. The suicidal use of drugs and respiratory rates were predictive factors for hypoglycemia in these patients.

Background:Patients who are identified to be at a higher risk of mortality from COVID-19 should receive better treatment and monitoring. This study aimed to propose a simple yet accurate risk assessment tool to help decision-making in the management of the COVID-19 pandemic. Methods: From Jul to Nov 2020, 5454 patients from Fars Province, Iran, diagnosed with COVID-19 were enrolled. A multiple logistic regression model was trained on one dataset (training set: n=4183) and its prediction performance was assessed on another dataset (testing set: n=1271). This model was utilized to develop the COVID-19 risk-score in Fars (CRSF). Results: Five final independent risk factors including gender (male: OR=1.37), age (60-80: OR=2.67 and >80: OR=3.91), SpO2 (≤85%: OR=7.02), underlying diseases (yes: OR=1.25), and pulse rate (<60: OR=2.01 and >120: OR=1.60) were significantly associated with in-hospital mortality. The CRSF formula was obtained using the estimated regression coefficient values of the aforementioned factors. The point values for the risk factors varied from 2 to 19 and the total CRSF varied from 0 to 45. The ROC analysis showed that the CRSF values of ≥15 (high-risk patients) had a specificity of 73.5%, sensitivity of 76.5%, positive predictive value of 23.2%, and negative predictive value (NPV) of 96.8% for the prediction of death (AUC=0.824, P<0.0001). Conclusion:This simple CRSF system, which has a high NPV,can be useful for predicting the risk of mortality in COVID-19 patients. It can also be used as a disease severity indicator to determine triage level for hospitalization.