English abstract
Objective: To develop and validate a clinical score that will identify potential admittance to an intensive care unit (ICU) for a coronavirus disease 2019 (COVID-19) case. Materials and methods: The clinical scoring system was developed using a least absolute shrinkage and selection operator logistic regression. The prediction algorithm was constructed and cross-validated using a development cohort of 313 COVID-19 patients, and was validated using an independent retrospective set of 64 COVID-19 patients. Results: The majority of patients were Omani in nationality (n = 181, 58%). Multivariate logistic regression identified eight independent predictors of ICU admission that were included in the clinical score: hospitalization (OR, 1.079; 95% CI, 1.058–1.100), absolute lymphocyte count (OR, 0.526; 95% CI, 0.379–0.729), C-reactive protein (OR, 1.009; 95% CI, 1.006–1.011), lactate dehydrogenase (OR, 1.0008; 95% CI, 1.0004–1.0012), CURB-65 score (OR, 2.666; 95% CI, 2.212–3.213), chronic kidney disease with an estimated glomerular filtration rate of less than 70 (OR, 0.249; 95% CI, 0.155–0.402), shortness of breath (OR, 3.494; 95% CI, 2.528–6.168), and bilateral infiltrates in chest radiography (OR, 6.335; 95% CI, 3.427–11.713). The mean area under a curve (AUC) for the development cohort was 0.86 (95% CI, 0.85–0.87), and for the validation cohort, 0.85 (95% CI, 0.82–0.88). Conclusion: This study presents a web application for identifying potential admittance to an ICU for a COVID-19 case, according to a clinical risk score based on eight significant characteristics of the patient (http://3.14.27.202/cov19-icu-score/).
