Background: We aimed to assess the risk factors for severe COVID-19 and investigate the differences between intensive care unit (ICU) and non-ICU patients. Method: The clinical, radiological, and laboratory characteristics of confirmed COVID-19 patients between March 15, 2020, and May 30, 2020, were evaluated retrospectively. Results: A total of 157 patients were included in the study. The median age of the patients was 47, and 55% were male. Seventeen of them were treated in ICU. All of the patients who were followed up in ICU were over 50 years old, and 70% were over 65 years old. The most common comorbid diseases were hypertension (HT) (20%) and diabetes mellitus (DM) (14%). The rates of HT and DM were significantly higher in ICU patients (p <0.001 and p = 0.003). The most-reported symptom was cough (58%). Lymphocyte and thrombocyte levels of ICU patients were lower than non-ICU patients, and median AST, ALT, blood urea nitrogen (BUN), creatinine and LDH levels were significantly higher (p <0.001). Hydroxychloroquine was initiated to all patients; however, the treatment duration was longer in ICU patients (p <0.001). Favipravir treatment was applied in 94% of the ICU patients, while 15% of the non-ICU patients (p <0.001). A nosocomial infection developed in 58% of the patients in the ICU. Twelve (71%) of the ICU patients received invasive mechanical ventilation, and nine died. The intensive care fatality rate was 59%, while the total fatality rate was 6%. Conclusion: The patients with a confirmed diagnosis of COVID-19 who needed ICU had older age and higher comorbidity. Lymphopenia, thrombocytopenia, liver function test disorder, and high D-dimer, CRP, and troponin levels may be significant for the severity of the disease.

ObjectivesBiological variation (BV) analysis of laboratory tests gets increased attention due to its practical applications. These applications include correct interpretation of laboratory tests, the decision on the availability of reference intervals, contributions to clinical decision-making. It is critical to derive the BV information accurately and reliably. Another crucial step is to perform the statistical analysis of the BV data. Although there are updated and comprehensive guidelines, there is no reliable and comprehensive tool to perform statistical analysis of BV data.MethodsWe presented BioVar, an online tool for statistical analysis of the BV data based on available and updated guidelines.ResultsThis tool can be used (i) to detect outliers, (ii) to control normality assumption, (iii) to check steady-state condition, (iv) to test homogeneity assumptions, (v) to perform subset analysis for genders, (vi) to perform analysis of variance to estimate components of variation and (vii) to identify analytical performance specifications of laboratory tests. Moreover, plots can be created at each step of outlier detection to inspect outliers and compare gender groups visually. An automatic report can be generated and downloaded.ConclusionThe tool is freely available through turcosa.shinyapps.io/biovar/, and source code is available on the Github: github.com/selcukorkmaz/BioVar.

Background: Psychosocial interventions involve anger management training and phone interviews to the family of the patients with breast cancer. Aims:The present study aimed to measure the effect of phone interviews and anger management training provided to patients with breast cancer on the levels of emotional expression and caregiver burden. Study design:The present study was conducted as a randomized-controlled experimental study (experiment-control groups with pretest and posttest design) between 2012 and 2013.