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The aim of our study was to evaluate the therapeutic effect of antiviral drugs on coronavirus disease 2019 (COVID‐19) pneumonia. Patients confirmed with COVID‐19 pneumonia were enrolled and divided into seven groups according to the treatment option. Information including age, sex, and duration from illness onset to admission, clinical manifestations, and laboratory data at admission, and length of hospital stay were evaluated. The chest computed tomography (CT) imaging obtained at admission and after a 5‐day treatment cycle were assessed. The clinical symptoms and laboratory tests at discharge were also assessed. At admission, no significant differences were found among the groups, including the duration from illness onset to admission, clinical symptoms, and main laboratory results. No significant differences were found among the groups in terms of the proportion of patients with pneumonia resolution (P =.151) after treatment or the length of hospital stay (P =.116). At discharge, 7 of 184 (4%) patients had a mild cough while their other symptoms had disappeared, and the proportion of patients with abnormal liver function and with increased leukocytes, neutrophils or erythrocyte sedimentation rate among the 184 patients were close to those at admission. According to the results, the inclusion of antiviral drugs in therapeutic regimens based on symptomatic treatment had no significant additional impact on the improvement in COVID‐19 patients. In addition, the results of chest CT imaging, clinical manifestations, and laboratory tests at discharge were not completely consistent. Highlights: Therapeutic effect of commercial antiviral drugs on COVID‐19 pneumonia. [ABSTRACT FROM AUTHOR]

• The study analyzed laboratory features associated with severe/critical symptoms. • A model for predicting the COVID-19 patients who will progress into severe/critical cases was developed and validated. • The study is anticipated to predict severe/critical symptom and thus save medical resources. Despite the death rate of COVID-19 is less than 3%, the fatality rate of severe/critical cases is high, according to World Health Organization (WHO). Thus, screening the severe/critical cases before symptom occurs effectively saves medical resources. In this study, all 336 cases of patients infected COVID-19 in Shanghai to March 12th, were retrospectively enrolled, and divided in to training and test datasets. In addition, 220 clinical and laboratory observations/records were also collected. Clinical indicators were associated with severe/critical symptoms were identified and a model for severe/critical symptom prediction was developed. Totally, 36 clinical indicators significantly associated with severe/critical symptom were identified. The clinical indicators are mainly thyroxine, immune related cells and products. Support Vector Machine (SVM) and optimized combination of age, GSH, CD3 ratio and total protein has a good performance in discriminating the mild and severe/critical cases. The area under receiving operating curve (AUROC) reached 0.9996 and 0.9757 in the training and testing dataset, respectively. When the using cut-off value as 0.0667, the recall rate was 93.33 % and 100 % in the training and testing datasets, separately. Cox multivariate regression and survival analyses revealed that the model significantly discriminated the severe/critical cases and used the information of the selected clinical indicators. The model was robust and effective in predicting the severe/critical COVID cases. [ABSTRACT FROM AUTHOR]