risk score model for predicting covid-19 progression in iranian patients: development and validation study

Background: the recent novel coronavirus disease 2019 (covid-19) pandemic has underlined the importance of risk score models in public health emergencies. this study aimed to develop a risk prediction score to identify high-risk hospitalized patients for disease progression on admission.methods: this prospective cohort study included 171 covid-19 patients, identified through the reverse transcription polymerase chain reaction test, admitted to bohlool hospital in gonabad city, iran, between april 4 and june 5, 2021. the patients’ demographic, clinical, and laboratory data were collected upon admission, and clinical outcomes were monitored until the end of the study. the discovery dataset (80% of the data) was used to develop the risk score model based on clinical and laboratory features and patient characteristics to predict covid-19 progression. an additive risk score model was developed based on the regression coefficients of the significant variables in a multiple logistic regression model. the performance of the risk score model was evaluated on the validation dataset (20% of the data) using the receiver operating characteristic (roc) curve. statistical analyses were performed with spss software, version 21.results: the mean±sd for age of participants was 59.54±20.52 years, and 48.6% were male. most patients (82.5%) fully recovered or showed improvement, while 5.2% experienced disease progression and 12.3% died. three variables, interleukin-6, neutrophil-to-lymphocyte ratio, and lung involvement, were found to be significant in predicting risk, with a good discriminatory ability, having an area under the roc curve of 0.970 (95% ci, 0.935%, 1.00%) in the discovery set and 0.973 (95% ci, 0.923%, 1.00%) in the validation set.conclusion: the developed risk score model in this study can be used as a clinical diagnostic tool to identify covid-19 patients at higher risk of disease progression and aid in informed decision-making and resource utilization in similar situations, such as respiratory disease outbreaks in the post-corona era.