Abstract：Predicting the risk of progression to severe/critical coronavirus disease 2019(COVID-19) is crucial for optimizing clinical interventions and healthcare resource allocation. This retrospective study analyzed clinical data from 305 hospitalized COVID-19 patients admitted to Huashan Hospital between December 2022 and January 2023, aiming to identify risk factors and evaluate the predictive performance of a multifactorial model for classifying patients into severe/critical categories. Demographic characteristics, comorbidities, and laboratory parameters (including inflammatory markers, coagulation profiles, and cardiac biomarkers) were collected, with disease severity classified according to the Diagnosis and Treatment Protocol for Novel Coronavirus Infection (Trial Version 10). Multivariable logistic regression revealed that advanced age, decreased lymphocyte count, reduced hemoglobin levels, and elevated levels of C-reactive protein (CRP), interleukin-6 (IL-6), D-dimer, and brain natriuretic peptide (BNP) were independent risk factors for severe/critical classification. A combined predictive model incorporating these indicators achieved an area under the curve (AUC) of 0.748, which significantly outperformed individual predictors (AUC range: 0.625-0.681). The findings suggest that integrating age, inflammatory response, coagulation dysfunction, and cardiac injury biomarkers can effectively identify high-risk COVID-19 patients, providing a scientific foundation for early stratified management, precision treatment, and public health policymaking. This study also contributes evidence-based insights for addressing future emerging infectious disease outbreaks.