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/).

SEAMHCRD deterministic compartmental model based on clinical stages of infection for COVID-19 pandemic in Sultanate of Oman.

The present novel coronavirus (COVID-19) infection has engendered a worldwide crisis on an enormous scale within a very short period. The effective solution for this pandemic is to recognize the nature and spread of the disease so that appropriate policies can be framed. Mathematical modelling is always at the forefront to understand and provide an adequate description of the transmission of any disease. In this research work, we have formulated a deterministic compartmental model (SEAMHCRD) including various stages of infection, such as Mild, Moderate, Severe and Critical to study the spreading of COVID-19 and estimated the model parameters by fitting the model with the reported data of ongoing pandemic in Oman. The steady-state, stability and final pandemic size of the model has been proved mathematically. The various transmission as well as transition parameters are estimated during the period from June 4th to July 30th, 2020. Based on the currently estimated parameters, the pandemic size is also predicted for another 100 days. Sensitivity analysis is performed to identify the key model parameters, and the parameter gamma due to contact with the symptomatic moderately infected is found to be more significant in spreading the disease. Accordingly, the corresponding basic reproduction number has also been computed using the Next Generation Matrix (NGM) method. As the value of the basic reproduction number (R0) is 0.9761 during the period from June 4th to July 30th, 2020, the disease-free equilibrium is stable. Isolation and tracing the contact of infected individuals are recommended to control the spread of disease.