Objective To develop and validate a clinical score that will identify potential admittance in an intensive care unit (ICU) for a coronavirus disease 2019 (COVID-19) case. Materials and methods The clinical scoring is built using Least Absolute Shrinkages and Selection Operator logistic regression. The prediction algorithm was constructed and cross-validated using the development cohort of 313 COVID-19 patients and was validated using independent retrospective set of 64 COVID-19 patients. Results Majority of patients are Omani in nationality (n=181, 58%). Multivariate logistic regression identified 8 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 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 of area under a curve (AUC) from the development cohort was 0.86 (95%CI, 0.85-0.87) and of the validation cohort was 0.85 (95%CI, 0.82-0.88). Conclusion We provided a web application for identifying potential admittance in an ICU for COVID-19 case by clinical risk score based on 8 significant characteristics of patient.
Objective To develop and validate a clinical score that will identify potential admittance in an intensive care unit (ICU) for a coronavirus disease 2019 (COVID-19) case. Materials and methods The clinical scoring is built using Least Absolute Shrinkages and Selection Operator logistic regression...
مادة فرعية
International Journal of Infectious Diseases