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Background: Broad-spectrum empirical antimicrobials are frequently prescribed for patients with coronavirus disease 2019 (COVID-19) despite the lack of evidence for bacterial coinfection. Aims: We aimed to cross-sectionally determine the frequency of antibiotics use, type of antibiotics prescribed, and the factors influencing antibiotics use in hospitalized patients with COVID-19 confirmed by polymerase chain reaction. Study Design: The study was a national, multicenter, retrospective, and single-day point prevalence study. Methods: This was a national, multicenter, retrospective, and single-day point-prevalence study, conducted in the 24-h period between 00:00 and 24:00 on November 18, 2020, during the start of the second COVID-19 peak in Turkey. Results: A total of 1500 patients hospitalized with a diagnosis of COVID-19 were included in the study. The mean age ± standard deviation of the patients was 65.0 ± 15.5, and 56.2% (n = 843) of these patients were men. Of these hospitalized patients, 11.9% (n = 178) were undergoing invasive mechanical ventilation or ECMO. It was observed that 1118 (74.5%) patients were receiving antibiotics, of which 416 (37.2%) were prescribed a combination of antibiotics. In total, 71.2% of the patients had neither a clinical diagnosis nor microbiological evidence for prescribing antibiotics. In the multivariate logistic regression analysis, hospitalization in a state hospital ( p < 0.001), requiring any supplemental oxygen ( p = 0.005), presence of moderate/diffuse lung involvement ( p < 0.001), C-reactive protein > 10 ULT coefficient ( p < 0.001), lymphocyte count < 800 ( p = 0.007), and clinical diagnosis and/or confirmation by culture ( p < 0.001) were found to be independent factors associated with increased antibiotic use. Conclusion: The necessity of empirical antibiotics use in patients with COVID-19 should be reconsidered according to their clinical, imaging, and laboratory findings.

Aim: We aimed to examine the diagnostic power of chest computerized tomography (CT) comparing with ‘Clinical Decision’ and RT-PCR results among the patients admitted to the hospital with COVID-19 disease suspicion. Material and Method: This study included 162 patients who applied to the pandemic outpatient clinic between March 11 and April 11, 2020, suspected of new coronavirus infection, and had chest CT and RT-PCR tests at the same time. The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy and positive odds ratio of RT-PCR and chest CT imaging are investigated for the diagnosis of COVID-19. Results: It was found that 56.8% (92 patients) of chest CT scans taken at admission were compatible with viral pneumonia. With the ‘Clinical Decision’, which we accept as the gold standard diagnostic method, 61.1% of the patients (99 patients) were evaluated as COVID-19 positive and treatment was started. According to clinical decision, sensitivity of chest CT was 92.9%. Conclusion: COVID-19 pneumonia is a serious life-threatening condition. Rapid diagnosis and early treatment are very important in terms of reducing mortality and morbidity. The chest CT might create an early diagnosis and treatment opportunity.

Introduction The prognostic nutritional index (PNI) is calculated using total serum lymphocyte counts and albumin levels. We aimed to analyze the role of PNI in predicting intensive care unit (ICU) referral and mortality in patients with Crimean Congo hemorrhagic fever (CCHF). Materials and Methods Our target population was adult (age >18) patients who presented between March 2015 and October 2021 within 5 days of symptom emergence and were diagnosed with CCHF. The predictive value of PNI was analyzed by the receiver operating curve analysis. The patients were categorized based on the severity grading scores (SGS) as mild, moderate, and severe. The relationship between PNI and ICU referral and mortality was analyzed by logistic regression analysis. Results Overall, 115 patients with the diagnosis of CCHF were included. 13.9% (n = 16) of the patients were referred to ICU while 11.3% (n = 13) died. A comparison of the patients with different SGS grades revealed that they were significantly different regarding PNI (p < 0.001). There was a significant negative correlation between PNI and SGS (r = −0.662; p < 0.001). PNI had a PV regarding ICU referral and mortality ([area under the curve [AUC] = 0.723, 95% confidence interval [CI]: 0.609–0.836, p = 0.004 [AUC = 0.738, 95% CI: 0.613–0.863, p = 0.005]). The PNI threshold was 36.1 for ICU referral and mortality. The rates of female patients, hospitalization periods longer than 1 week, platelet apheresis replacement, diabetes mellitus, bleeding history, ICU admission, and mortality were significantly higher in patients with a PNI of lower than 36.1 (p < 0.05). Conclusion PNI can predict ICU referral and mortality in patients admitted due to CCHF.