BACKGROUND: Limited information is available about the present characteristics and dynamic clinical changes that occur in patients with COVID-19 during the early phase of the illness .
OBJECTIVE: This study aimed to develop and validate machine learning models based on clinical features to assess the risk of severe disease and triage for COVID-19 patients upon hospital admission .
METHODS: This retrospective multicenter cohort study included patients with COVID-19 who were released from quarantine until April 30 , 2020, in Korea . A total of 5628 patients were included in the training and testing cohorts to train and validate the models that predict clinical severity and the duration of hospitalization, and the clinical severity score was defined at four levels: mild, moderate, severe, and critical .
RESULTS: Out of a total of 5601 patients , 4455 (79.5 %), 330 (5.9 %), 512 (9.1 %), and 301 (5.4 %) were included in the mild, moderate, severe, and critical levels, respectively . As risk factors for predicting critical patients, we selected older age, shortness of breath, a high white blood cell count, low hemoglobin levels, a low lymphocyte count, and a low platelet count . We developed 3 prediction models to classify clinical severity levels . For example, the prediction model with 6 variables yielded a predictive power of> 0.93 for the area under the receiver operating characteristic curve . We developed a web-based nomogram, using these models .
CONCLUSIONS: Our prediction models, along with the web-based nomogram, are expected to be useful for the assessment of the onset of severe and critical illness among patients with COVID-19 and triage patients upon hospital admission.