February 6th, Kahramanmaras earthquakes and the disaster management algorithm of adult emergency medicine in Turkey: An experience review.

This compilation covers emergency medical management lessons from the February 6th Kahramanmaras earthquakes. The objective is to review relevant literature on emergency services patient management, focusing on Koenig's 1996 Simple Triage and Rapid Treatment (START) and Secondary Assessment of Victim Endpoint (SAVE) frameworks. Establishing a comprehensive seismic and mass casualty incident (MCI) protocol chain is the goal. The prehospital phase of seismic MCIs treats hypovolemia and gets patients to the nearest hospital. START-A plans to expedite emergency patient triage and pain management. The SAVE algorithm is crucial for the emergency patient secondary assessment. It advises using Glasgow Coma Scale, Mangled Extremity Severity Score, Burn Triage Score, and Safe Quake Score for admission, surgery, transfer, discharge, and outcomes. This compilation emphasizes the importance of using diagnostic tools like bedside blood gas analyzers and ultrasound devices during the assessment process, drawing from 6 February earthquake research. The findings create a solid framework for improving emergency medical response strategies, making them applicable in similar situations.

Effect of a cervical collar on optic nerve sheath diameter in trauma patients.

BACKGROUND: As advocated in advanced trauma life support and prehospital trauma life support protocols, cervical immobilization is applied until cervical spine injury is excluded. This study aimed to show the difference in optic nerve sheath diameter (ONSD) between patients with and without a cervical collar using computed tomography (CT). METHODS: This was a single-center, retrospective study examining trauma patients who presented to the emergency department between January 1, 2021, and December 31, 2021. The ONSD on brain CT of the trauma patients was measured and analyzed to determine whether there was a difference between the ONSD with and without the cervical collar. RESULTS: The study population consisted of 169 patients. On CT imaging of patients with (n=66) and without (n=103) cervical collars, the mean ONSD in the axial plane were 5.43 ± 0.50 mm and 5.04 ± 0.46 mm respectively for the right eye and 5.50 ± 0.52 mm and 5.11 ± 0.46 mm respectively for the left eye. The results revealed an association between the presence of a cervical collar and the mean ONSD, which was statistically significant (P<0.001) for both the right and left eyes. CONCLUSION: A cervical collar may be associated with increased ONSD. The effect of this increase in the ONSD on clinical outcomes needs to be investigated, and the actual need for cervical collar in the emergency department should be evaluated on a case-by-case basis.

The Diagnostic Accuracy of the Hounsfield Unit Value in Pulmonary Embolism.

Objective: Pulmonary embolism (PE) a vascular disease. Computed tomography pulmonary angiography (CTPA) is the radiological imaging technique used to diagnose PE. In this study, we aimed to demonstrate the diagnostic accuracy of Hounsfield Unit (HU) value for PE based on the hypothesis that acute thrombosis causes an increase in HU value on computed tomography (CT). Methods: This research was as a single-center, retrospective study. Patients presenting to the emergency department (ED) diagnosed with PE on CTPA were enrolled as the study group. In addition, patients admitted to the same emergency department who were not diagnosed with PE and had non-contrast CT scans were included as the control group. A receiver operating curve (ROC) was produced to the diagnostic accuracy of HU values in predicting PE. Results: The study population (N=74) consisted of a study group (N=46) and a control group (N=28). The sensitivity and specificity of HU value for predicting PE on thoracic CT were found 61.5% and 96.4% at a value of 54.8 (Area Under the Curve (AUC):0.690) for right main pulmonary artery; 65.0% and 96.4% at a value of 55.9 (AUC:0.736) for left main pulmonary artery; 44.4% and 96.4% at a value of 62.7 (AUC:0.615) for right interlobar artery; and 60.0% and 92.9% at a value of 56.7 (AUC:0.736) for left interlobar artery. Conclusion: HU values may exhibit high diagnostic specificity on CT, for thrombi up to the interlobar level. An HU value exceeding 54.8 up to the interlobar level may raise suspicion of the presence of PE.

Predicting COVID-19 Outcomes: Machine Learning Predictions Across Diverse Datasets.

Background The COVID-19 infection has spread rapidly since its emergence and has affected a large part of the global population. With the increasing number of cases, researchers are trying to predict the prognosis of patients by using different data with artificial intelligence methods such as machine learning (ML). In this study, we aimed to predict mortality risk in COVID-19 patients using ML algorithms with different datasets. Methodology In this retrospective study, we evaluated the fever, oxygen saturation, laboratory results, thorax computed tomography (CT) findings, and comorbid diseases at admission to the hospital of 404 patients whose diagnosis was confirmed by the reverse transcription polymerase chain reaction test. Different datasets were created by combining the data. The Synthetic Minority Oversampling Technique was used to reduce the imbalance in the dataset. K-nearest neighbors, support vector machine, stochastic gradient descent, random forest, neural network, naive Bayes, logistic regression, gradient boosting, XGBoost, and AdaBoost models were used to create the ML algorithm, and the accuracy rates of mortality prediction were compared. Results When the dataset was created with CT parenchyma score, pulmonary artery and inferior vena cava diameters, and laboratory results, mortality was predicted with an accuracy of 98.4% with the gradient boosting model. Conclusions The study demonstrates that patient prognosis can be accurately predicted using simple measurements from thorax CT scans and laboratory findings.