Purpose SARS-CoV-2 virus dynamics in different hosts and different samples and their relationship with disease severity have not been clearly revealed. The aim of this study is to evaluate the viral loads of 6 different sample types (nasopharyngeal/ oropharyngeal combined, oral cavity, saliva, rectal, urine, and blood) of patients with different ages and clinics, to reveal the relationship between disease course and SARS-CoV-2 viral load, and differences in viral loads of asymptomatic and symptomatic patients. Methods Nasopharyngeal/oropharyngeal, oral cavity, saliva, rectal, urine, and blood samples are collected from patients who were hospitalized with diagnosis of COVID-19 on admission. Laboratory analysis were carried out at Public Health Institute of Turkey Virology Reference and Research Laboratory. Results A total of 360 samples from 60 patients were obtained on admission. Fifteen (25%) of the patients were asymptomatic while 45 (75%) were symptomatic. A significant difference was found between mean ages of asymptomatic vs symptomatic patients (26.4 and 36.4, respectively, p = 0.0248). No PCR positivity were found in blood. Only one asymptomatic patient had positive PCR result for urine sample. Viral loads of asymptomatic patients were found to be significantly higher (p = 0.0141) when compared with symptomatic patients. Viral load had a significant negative trend with increasing age. A significant decrease in viral load was observed with increasing disease severity. Conclusion In conclusion, this study demonstrates that asymptomatic patients have higher SARSCoV-2 viral loads than symptomatic patients and unlike in the few study in the literature, a significant decrease in viral load of nasopharyngeal/ oropharyngeal samples was observed with increasing disease severity. Factors associated with poor prognosis are found to be significantly correlated with low viral load.

Severe COVID‐19 patients in ICU are at high risk for candidemia due to exposure to multiple risk factors for candidemia. We aimed to compare the incidence of candidemia in ICU patients with and without COVID‐19, and to investigate epidemiologic and clinical characteristics of candidemia patients and risk factors for mortality in candidemia patients. This retrospective study was conducted in patients followed in the ICUs of Ankara City Hospital for 2 years, divided into pre‐pandemic and pandemic periods. The incidence (event per 1000 patient‐days) and epidemiology of candidemia, clinical and laboratory characteristics of patients were compared in COVID‐19 and non‐COVID‐19 groups. Candidemia incidence was higher in the COVID‐19 group (2.16, 95% CI 1.77–2.60) than the non‐COVID‐19 group (1.06, 95% CI 0.89–0.125) ( p < .001). A total of 236 candidemia episodes (105 in COVID‐19 patients and 131 in non‐COVID‐19 patients) were detected during the study periods. COVID‐19 cases had a higher rate of corticosteroid use (63.8% vs. 9.9%, p < .001). Epidemiology of candidemia and antifungal susceptibility were similar. Candidemia developed 2 weeks earlier in COVID‐19 groups and resulted in higher mortality (92.5% vs. 79.4%, p .005). One‐third of candidemia patients died before receiving any antifungal treatment, and this rate was higher in the COVID‐19 group. In multivariate logistic regression analysis, corticosteroid use, presence of sepsis and age older than 65 years were independent risk factors for mortality in candidemia patients. Candidemia with high mortality is a more serious problem for COVID‐19 patients due to its increased incidence, earlier occurrence and a higher rate of mortality.

Candidemia is one of the most common nosocomial bloodstream infections in critically ill patients, accounting for 7%-15% of the episodes, and is associated with increased mortality, prolonged hospital stays, and increased cost. 1,2 Non-neutropenic intensive care unit (ICU) patients undergoing multiple invasive procedures, and immunocompromised patients are at high risk for the development of candidemia. 3,4 The COVID-19 pandemic caused an increase in the number of patients who need to be followed up in ICU, creating a

Novel coronavirus infections 2019 (COVID-19) associated hyperinflammatory syndromes are well-defined clinical conditions and have a potential risk for severe infection. Hemophagocytic lymphohistiocytosis (HLH), a rare type of acute progressive hyperinflammatory syndrome, has been reported in a limited number of COVID-19 cases. In this article, we aimed to present a patient with HLH secondary to COVID-19 diagnosed by bone marrow biopsy, and to summarize and review HLH cases associated with COVID-19 in the literature. A 47-year-old male patient presented with complaints of fever, cough, abdominal discomfort, and nausea-vomiting. He had recovered from COVID-19 a month ago and was readmitted to the hospital due to the re-appearance of clinical symptoms after a two-week interval. The patient was diagnosed with HLH secondary to COVID-19 on sixth day of admission and fully recovered with systemic pulse steroid, intravenous immunoglobulin, and plasma exchange therapy. Analysis of literature searches revealed that 22 cases were definitely diagnosed with COVID-19-associated HLH, 16 of them were male. They had been treated with different anti-cytokine drugs, of which nine had died. The increasing number of HLH cases, which have high mortality rates, shows the importance of hyperinflammatory syndromes in COVID-19 patients. Some patients may experience hemophagocytosis in the late period of COVID-19, even while in recovery. Increased awareness and early treatment for HLH triggered by COVID-19 can be a life-saving effort for reducing mortality in severe COVID-19 cases.

Background Early identification of severe COVID-19 patients who will need intensive care unit (ICU) follow-up and providing rapid, aggressive supportive care may reduce mortality and provide optimal use of medical resources. We aimed to develop and validate a nomogram to predict severe COVID-19 cases that would need ICU follow-up based on available and accessible patient values. Methods Patients hospitalized with laboratory-confirmed COVID-19 between March 15, 2020, and June 15, 2020, were enrolled in this retrospective study with 35 variables obtained upon admission considered. Univariate and multivariable logistic regression models were constructed to select potential predictive parameters using 1000 bootstrap samples. Afterward, a nomogram was developed with 5 variables selected from multivariable analysis. The nomogram model was evaluated by Area Under the Curve (AUC) and bias-corrected Harrell's C-index with 95% confidence interval, Hosmer–Lemeshow Goodness-of-fit test, and calibration curve analysis. Results Out of a total of 1022 patients, 686 cases without missing data were used to construct the nomogram. Of the 686, 104 needed ICU follow-up. The final model includes oxygen saturation, CRP, PCT, LDH, troponin as independent factors for the prediction of need for ICU admission. The model has good predictive power with an AUC of 0.93 (0.902–0.950) and a bias-corrected Harrell's C-index of 0.91 (0.899–0.947). Hosmer–Lemeshow test p-value was 0.826 and the model is well-calibrated (p = 0.1703). Conclusion We developed a simple, accessible, easy-to-use nomogram with good distinctive power for severe illness requiring ICU follow-up. Clinicians can easily predict the course of COVID-19 and decide the procedure and facility of further follow-up by using clinical and laboratory values of patients available upon admission.