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Mortality risk analysis for patients with severe coronavirus disease 2019 pneumonia
Hui Dai1, Renjun Huang2, Yalei Shang2, Jian'an Huang3, Nan Su3, Daxiong Zeng3, Hongmei Li4, Yonggang Li1
1 Department of Radiology, First Affiliated Hospital of Soochow University; Institute of Medical Imaging, Soochow University, Suzhou, Jiangsu Province, China
2 Department of Radiology, First Affiliated Hospital of Soochow University, Suzhou, Jiangsu Province, China
3 Department Pulmonary and Critical Care Medicine, First Affiliated Hospital of Soochow University, Suzhou, Jiangsu Province, China
4 Department of Epidemiology and Biostatistics, School of Public Health, Medical College of Soochow University, Suzhou, Jiangsu Province, China
Correspondence Address:
Daxiong Zeng
Department of Pulmonary and Critical Care Medicine, First Affiliated Hospital of Soochow University, Suzhou City 215000, Jiangsu Province
China
Hongmei Li
Department of Epidemiology and Biostatistics, School of Public Health, Medical College of Soochow University, Suzhou City 215000, Jiangsu Province
China
Yonggang Li
Department of Radiology, First Affiliated Hospital of Soochow University, Suzhou, Jiangsu 215000
China
 Source of Support: None, Conflict of Interest: None
DOI: 10.4103/RID.RID_44_22
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BACKGROUND: Coronavirus Disease 2019 (COVID-19) is currently a global pandemic. Information about predicting mortality in severe COVID-19 remains unclear.
METHODS: A total of 151 COVID-19 in-patients from January 23 to March 8, 2020, were divided into severe and critically severe groups and survival and mortality groups. Differences in the clinical and imaging data between the groups were analyzed. Factors associated with COVID-19 mortality were analyzed by logistic regression, and a mortality prediction model was developed.
RESULTS: Many clinical and imaging indices were significantly different between groups, including age, epidemic history, medical history, duration of symptoms before admission, routine blood parameters, inflammatory-related factors, Na+, myocardial zymogram, liver and renal function, coagulation function, fraction of inspired oxygen and complications. The proportions of patients with imaging Stage III and a comprehensive computed tomography score were significantly increased in the mortality group. Factors in the prediction model included patient age, cardiac injury, acute kidney injury, and acute respiratory distress syndrome. The area under the receiver operating characteristic curve of the prediction model was 0.9593.
CONCLUSIONS: The clinical and imaging data reflected the severity of COVID-19 pneumonia. The mortality prediction model might be a promising method to help clinicians quickly identify COVID-19 patients who are at high risk of death.
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