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 …

The experience of the early nationwide COVID-19 pandemic in South Korea led to an early
shortage of medical resources. For efficient resource allocation, accurate prediction of the …

Background Early identification of patients with coronavirus disease 2019 (COVID-19) who
are at high risk of mortality is of vital importance for appropriate clinical decision making and …

Purpose We aimed to develop a novel mortality scoring system for inpatients with COVID-19
based on simple demographic factors and laboratory findings. Materials and Methods We …

Objective Our aim was to develop and validate a nomogram for predicting the in-hospital 14-
day (14 d) and 28-day (28 d) survival rates of patients with coronavirus disease 2019 …

Objectives Numerous people have died from coronavirus disease 2019 (COVID-19)
infection. Identifying crucial predictive biomarkers of disease mortality is critical to support …

Background: The global COVID-19 epidemic remains severe, with the cumulative global
death toll reaching more than 207,170 as of May 2, 2020. Purpose: Our research objective is …

Background COVID-19 pandemic has forced physicians to quickly determine the patient's
condition and choose treatment strategies. This study aimed to build and validate a simple …

To establish an effective nomogram for predicting in-hospital mortality of COVID-19, a
retrospective cohort study was conducted in two hospitals in Wuhan, China, with a total of …

Background As the COVID-19 pandemic continues, an initial risk-adapted allocation is
crucial for managing medical resources and providing intensive care. Objective In this study …