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    Examination of patients admitted to the emergency department with blunt chest trauma
    (2021) Gönüllü, Hayriye; Tekindal, Mustafa Agah; Erdoğan, Arife
    Blunt chest trauma is an important and common cause of morbidity and mortality. It constitutes an important part of the admissions to the emergency department. For this purpose, we evaluated the demographic characteristics, type of trauma, accompanying traumas, trauma scoring systems and results, duration of stay in the emergency department, and hospital outcomes of patients with chest trauma admitted to the emergency department of our hospital. In our study, patients who admitted to the emergency department of our hospital within one year with blunt chest trauma were evaluated retrospectively. Of the 156 patients examined, 114 (73%) were male and the mean age was 52.83±17.9 years. Pneumothorax (35%) and rib fracture (55%) were the most common thoracic injuries. When the duration of hospitalization was examined, the presence of lower extremity and abdomen pathologies, pneumothorax, hemothorax, and (>3) rib fracture prolonged the hospitalization period (p<0.05). Revised Trauma Score (RTS) and Glasgow Coma Score (GCS) were found to be significantly lower in deceased patients (p<0.001). Pneumothorax, hemothorax, (>3) rib fracture and pneumomediastinum increased mortality (p<0.05). Patients with chest trauma can have life-threatening clinics. In the emergency department, it should be evaluated for possible life-threatening pathologies, especially in terms of multi-trauma, and thoracic pathologies and other organ injuries should be managed simultaneously in an efficient and rapid manner. The issues to be considered in the triage, stabilization and follow-up of the patients should be well known.
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    The Effect of Seasonal Factors on the Spread and Mortality of COVID-19: Retrospective Multicenter Study
    (Hacettepe University, 2023) Arıkan, Cüneyt; Yamanoğlu, Adnan; Tekindal, Mustafa Agah; Siliv, Neslihan; Bora, Ejder Saylav; Şener, Aslı; Kanter, Efe
    Objectives: The impact of seasonal factors on the spread of Coronavirus-19 disease (COVID-19) is not yet clear. The aim of this study is to determine the effect of seasonal factors on the spread of COVID-19. Methods: This multicenter retrospective study was performed by collecting 284-day COVID-19 data from two university hospitals in a metropolitan center. Correlations between the seasonal parameters of temperature, humidity, wind, and rainfall and the spread of COVID-19 and its clinical outcomes were evaluated using Spearman’s correlation test. Since no linear relationship was determined between variables exhibiting correlation, all models were tested using non-linear curve estimation regression models. The most powerful of the curve estimation regression models, capable of explaining more than 20% of the changes in COVID-19 parameters, was formulated to explain the expected number of events. Results: A total of 24 2Objectives: The impact of seasonal factors on the spread of Coronavirus-19 disease (COVID-19) is not yet clear. The aim of this study is to determine the effect of seasonal factors on the spread of COVID-19. Methods: This multicenter retrospective study was performed by collecting 284-day COVID-19 data from two university hospitals in a metropolitan center. Correlations between the seasonal parameters of temperature, humidity, wind, and rainfall and the spread of COVID-19 and its clinical outcomes were evaluated using Spearman’s correlation test. Since no linear relationship was determined between variables exhibiting correlation, all models were tested using non-linear curve estimation regression models. The most powerful of the curve estimation regression models, capable of explaining more than 20% of the changes in COVID-19 parameters, was formulated to explain the expected number of events. Results: A total of 24 225 patients were included in the study. The most powerful correlation was between mean daily temperature and daily case numbers (r:-0.643, p<0.00), with case numbers being highest on days when the mean temperature was 7-18℃. Mean temperate was capable of explaining 57% of COVID-19 case numbers (R-Square:0.571, p<0.00), the relationship between them being best explained in the ’S’ curve regression model. The formula ‘’Y=exp(2.07+31.34/x)’’ was obtained for the number of patients expected from the model according to mean temperature. Conclusions: Temperature may be the most effective factor in the spread of COVID-19 and the number of cases may be predicted based on temperature. patients were included in the study. The most powerful correlation was between mean daily temperature and daily case numbers (r:-0.643, p<0.00), with case numbers being highest on days when the mean temperature was 7-18℃. Mean temperate was capable of explaining 57% of COVID-19 case numbers (R-Square:0.571, p<0.00), the relationship between them being best explained in the ’S’ curve regression model. The formula ‘’Y=exp(2.07+31.34/x)’’ was obtained for the number of patients expected from the model according to mean temperature. Conclusions: Temperature may be the most effective factor in the spread of COVID-19 and the number of cases may be predicted based on temperature.