Development and validation of the CoV19-OM intensive care unit score: An early ICU identification for laboratory-confirmed SARS-CoV-2 patients from a retrospective cohort study in Oman

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/).