Prediction Models for the Clinical Severity of Patients With COVID-19 in Korea: Retrospective Multicenter Cohort Study

Abstract

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.

Does COVID-19 Clinical Status Associate with Outcome Severity? An Unsupervised Machine Learning Approach for Knowledge Extraction

“…Other variables have standard deviations that are very large; therefore, it is not practical to extract conclusions from them. A noteworthy result is that the temperature has highly similar mean values and small standard deviations across all clusters; therefore, it might not be affecting the patient's categorization in the degree that was previously thought [59]. In addition, the present study design indicates the temperature has been recorded only once, upon patient admission at the Emergency Department of the hospital.…”

Validation of Machine Learning Models to Predict Adverse Outcomes in Patients with COVID-19: A Prospective Pilot Study

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The risk profile of patients with COVID-19 as predictors of lung lesions severity and mortality—Development and validation of a prediction model

“…Previous prediction studies highlighted the history of comorbidities as one of the important factors associated with mortality risk in patients infected with COVID-19 (Lopez-Pais et al, 2021 ; Oh et al, 2021 ; Ryan et al, 2021 ). Though the rate of comorbidities, respiratory distress, and intubation was statistically higher in patients with severe lung involvement, our models failed to pick these factors as strong predictors of COVID-19 outcomes.…”