Dina Jalalvand - Radiology department, Taleghani hospital, Shahid Beheshti University of medical sciences, Tehran, Iran
Fatemeh Akbari - Radiology department, Taleghani hospital, Shahid Beheshti University of medical sciences, Tehran, Iran
Ali Pirsalehi - Internal medicine department, school of medicine, Shahid Beheshti University of medical sciences, Tehran, Iran
Pooneh Dehghan - Radiology department, Taleghani hospital, Shahid Beheshti University of medical sciences, Tehran, Iran
Seyed Aria Nejadghaderi - School of medicine, Shahid Beheshti University of medical sciences, Tehran, Iran
Laya Jaliliankhave - School of medicine, Shahid Beheshti University of medical sciences, Tehran, Iran
Taha Hasanzadeh - School of medicine, Shahid Beheshti University of medical sciences, Tehran, Iran
Ghazal Sanadgol - School of medicine, Shahid Beheshti University of medical sciences, Tehran, Iran
Dorsa Shirini - School of medicine, Shahid Beheshti University of medical sciences, Tehran, Iran
Zeinab Eslami - Radiology department, Taleghani hospital, Shahid Beheshti University of medical sciences, Tehran, Iran

Introduction: World Health Organization (WHO) declared a novel HCoVs (COVID-۱۹) to be a public health emergency of international concern on ۳۰ January, ۲۰۲۰. Typical clinical symptoms of patients include fever, dry cough, breathing difficulties (dyspnea). CT is the most sensitive radiological technique for the diagnosis of COVID-۱۹, showing spectrum of lung features. The purpose of this study was to investigate clinical and laboratory outcomes and chest CT futures of patients to recognize prognostic factors for COVID-۱۹.Methods: Clinical and laboratory findings and chest CT futures were evaluated from ۲۲۶ admitted patients with the initial diagnosis of COVID-۱۹ who were recovered or died due to the disease and its complications. The association between vital status and categorical variables was evaluated. The single and multiple logistic models were used for assessing the impact of study variables on the hazard of occurring death.Results: The prediction power of some variables were significant. The highest AUCs were observed for GGO pattern, age, lymphocyte count, Creatinine, CRP, LDH, and Systolic Blood Pressure. This study was reached an accurate for predicting vital status among COVID-۱۹ patients.Conclusion: In this study, we evaluated and presented CT feature parameters as well as clinical and laboratory markers as a model that predicts vital status among COVID-۱۹ patients.

COVID-۱۹, mortality, Computed Tomography, ground-glass opacity