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    Comparison of rapid antibody test and thorax computed tomography results in patients who underwent RT-PCR with the pre-diagnosis of COVID-19
    (Wiley, 2021) Kızıloğlu, İlker; Şener, Aslı; Siliv, Neslihan
    Introduction In this study, it is planned to compare the real-time reverse transcription-polymerase chain reaction (RT-PCR) test, which is the gold standard in the diagnosis of COVID-19, with thorax computed tomography (CT) and rapid antibody test results. Methods Patients who were admitted to the emergency service of Izmir cigli Training and Research Hospital between 01.04.2020 and 31.05.2020 and who were suspected of having COVID-19 infection were included in the study. The medical records of the patients were retrospectively analysed through the hospital data processing database. Age, gender, hospitalisation, status of home quarantine, real-time RT-PCR, thorax CT and rapid antibody test results of the patients were examined. The relationship between RT-PCR, thorax CT and rapid antibody test results was compared statistically. Results A total of 181 patients, 115 (63.5%) male and 66 (36.5%) female, with an average age of 56.4 +/- 18.06 years were included in the study. The nasopharyngeal swab PCR result obtained at the first admission of the patients to the emergency department was positive in 71 (39.2%) patients. Rapid antibody tests performed at hospital admission were positive in 57 (31.5%) patients. Thorax CT was performed in 173 (95.6%) patients who applied to the emergency department, and 112 (64.7%) of them had findings that could be compatible with COVID-19. According to the thorax CT findings in patients, sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) for detecting COVID-19 infection were, respectively, 76.1%, 43.1%, 48.2% and 72.1% (kappa: 0.176, P < .001). According to the rapid antibody test results, sensitivity, specificity, PPV and NPV for detecting COVID-19 infection were 57.5%, 85.5%, 71.9% and 75.8%, respectively (kappa: 0.448, P < .001). In our study, the mortality rate for COVID-19 was found to be 2.8%. Conclusion Rapid antibody test and thorax CT examinations were found to have low diagnostic value in patients who admitted to the emergency department of our hospital and whose first RT-PCR SARS-CoV-2 test was positive. Studies involving larger patient groups are needed for their use alone in diagnosis and screening.
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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.