Background Prediction of mortality in patients with coronavirus disease 2019 (COVID-19) is a key to improving the clinical outcomes, considering that the COVID-19 pandemic has led to the collapse of healthcare systems in many regions worldwide. This study aimed to identify the factors associated with COVID-19 mortality and to develop a nomogram for predicting mortality using clinical parameters and underlying diseases. Methods This study was performed in 5,626 patients with confirmed COVID-19 between February 1 and April 30, 2020 in South Korea. A Cox proportional hazards model and logistic regression model were used to construct a nomogram for predicting 30-day and 60-day survival probabilities and overall mortality, respectively in the train set. Calibration and discrimination were performed to validate the nomograms in the test set. Results Age ≥ 70 years, male, presence of fever and dyspnea at the time of COVID-19 diagnosis, and diabetes mellitus, cancer, or dementia as underling diseases were significantly related to 30-day and 60-day survival and mortality in COVID-19 patients. The nomogram showed good calibration for survival probabilities and mortality. In the train set, the areas under the curve (AUCs) for 30-day and 60-day survival was 0.914 and 0.954, respectively; the AUC for mortality of 0.959. In the test set, AUCs for 30-day and 60-day survival was 0.876 and 0.660, respectively, and that for mortality was 0.926. The online calculators can be found at https://koreastat.shinyapps.io/RiskofCOVID19/ . Conclusion The prediction model could accurately predict COVID-19-related mortality; thus, it would be helpful for identifying the risk of mortality and establishing medical policies during the pandemic to improve the clinical outcomes.

Background: The emergence of macrolide-resistant Mycoplasma pneumoniae pneumonia (MRMP) has made its treatment challenging. A few guidelines have recommended fluoroquinolones (FQs) as secondline drugs of choice for treating MRMP in children under the age of eight, but concerns about potential adverse events (i.e., Achilles tendinopathy; AT) have been raised. The aim of this study was to investigate the relationship between the use of FQs and the risk of AT in pneumonia in children under eight years of age. Methods: Children hospitalized with pneumonia (total of 2,213,807 episodes) from 2002 to 2017 were enrolled utilizing the Korean National Health Insurance Sharing Service (NHISS) database. The independent risk of FQs for AT was analyzed by a generalized estimating equation with adjustment for age, sex, and underlying diseases. Results: Among 2,213,807 episodes of pneumonia hospitalization, children in a total of 6,229 episodes (0.28%) were treated with FQs (levofloxacin 40.9%, ciprofloxacin 36.1%, moxifloxacin 11.6%, and others 11.4%). The FQ-exposure group showed a 0.19% (12/6,229) incidence of AT within 30 days after the first administration of FQ. The use of FQs increased the risk of AT (OR 3.00; 95% CI: 1.71-5.29), but became null after adjusting for age, sex, and underlying diseases (aOR 0.85; 95% CI: 0.48-1.51). All AT related to the use of FQs occurred after the use of ciprofloxacin or levofloxacin, and not in children under eight years of age.Conclusions: AT was a rare adverse event of FQ use for childhood pneumonia, particularly under eight years of age. Clinicians could consider using FQs as a second-line option in the treatment of childhood pneumonia when there are no alternative therapeutic options.

Background: Human coronaviruses (HCoV) cause mild upper respiratory infections; however, in 2019, a novel coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), emerged, causing an acute respiratory disease pandemic. Coronaviruses exhibit marked epidemiological and clinical differences. Purpose: This study compared the clinical, laboratory, and radiographic findings of children infected with SARS-CoV-2 versus HCoV. Methods: SARS-CoV-2 data were obtained from the Korea Disease Control and Prevention Agency (KDCA) registry and four dedicated coronavirus disease 2019 (COVID-19) hospitals. Medical records of children admitted with a single HCoV infection from January 2015 to March 2020 were collected from 10 secondary/tertiary hospitals. Clinical data included age, sex, underlying disease, symptoms, test results, imaging findings, treatment, and length of hospital stay. Results:We compared the clinical characteristics of children infected with HCoV (n=475) to those of children infected with SARS-CoV-2 (272 from KDCA, 218 from COVID-19 hospitals). HCoV patients were younger than KDCA patients (older than 9 years:3.6% vs. 75.7%; p<0.001) and patients at COVID-19 hospitals (2.0±2.9 vs 11.3±5.3; p<0.001). Patients with SARS-CoV-2 infection had a lower rate of fever (26.6% vs. 66.7%; p<0.001) and fewer respiratory symptoms than those with HCoV infection. Clinical severity, as determined by oxygen therapy and medication usage, was worse in children with HCoV infection. Children and adolescents with SARS-CoV-2 had less severe symptoms. Conclusion:Children and adolescents with COVID-19 had a milder clinical course and less severe disease than those with HCoV in terms of symptoms at admission, examination findings, and laboratory and radiology results.

BACKGROUND Prediction of mortality in patients with coronavirus disease 2019 (COVID-19) is key to improving the clinical outcomes, considering that the COVID-19 pandemic has led to the collapse of healthcare systems in many regions worldwide. OBJECTIVE This study aimed to identify the factors associated with COVID-19 mortality and to develop a nomogram for predicting mortality using clinical parameters and underlying diseases. METHODS This study was performed in 5,626 patients with confirmed COVID-19 between February 1 and April 30, 2020 in South Korea. A Cox proportional hazards model and logistic regression model were used to construct a nomogram for predicting 30-day and 60-day survival probabilities and overall mortality, respectively in the train set. Calibration and discrimination were performed to validate the nomograms in the test set. RESULTS Age ≥70 years; male; presence of fever and dyspnea at the time of COVID-19 diagnosis; and diabetes mellitus, cancer, or dementia as underling diseases were significantly related to 30-day and 60-day survival and mortality in COVID-19 patients. The areas under the curve (AUCs) for 30-day and 60-day survival was 0.914 and 0.954, respectively (C-index, 0.906; 95% CI, 0.883-0.929); the AUC for mortality of 0.926. The nomogram showed good calibration for survival probabilities and mortality. The online calculators can be found at https://koreastat.shinyapps.io/RiskofCOVID19/. CONCLUSIONS The prediction model could accurately predict COVID-19-related mortality; thus, it would be helpful for identifying the risk of mortality and establishing medical policies during the pandemic to improve the clinical outcomes. CLINICALTRIAL not applicable