To investigate the characteristic findings between laboratory-confirmed and clinically suspected patients with COVID-19. In this retrospective study, we included patients admitted to the Xiangya Hospital from Jan 24 to Feb 10, 2020. Two researchers separately collected and sorted out the patients' epidemiological, demographic, clinical, laboratory, and radiologic findings. SPSS was performed to analyze the collected data. 241 patients were admitted, including 28 (45.5; IQR, 34.0-52.5) confirmed and 213 (42.0; IQR, 30.0-57.0) suspected patients. The prevalence of COVID-19 disease in males was significantly higher than in females (64.3% vs. 35.7%, P = 0.033). Before admission of the confirmed and suspected undiagnosed cases, the onset of symptoms is often manifested as respiratory symptoms such as fever (35.7% vs. 27.7%) and cough (30.7% vs. 32.1%). Twenty patients (71.4%) had an exposure history to high-risk areas, and 14 patients (50.0%) traveled or lived in a high-risk area in the confirmed group, which was significantly different from the suspected group. The pulmonary imaging of the patients in the confirmed group was primarily manifested as ground-glass opacity (89.3%). A total of 499 nucleic acid testing (NAT) was performed to determine the 28 COVID-19 positive throat swabs among the 241 patients. Whether there is a history of high-risk area exposure in the epidemiological investigation is essential in distinguishing the suspected patients from the confirmed patients. Multiple nucleic acid tests were used as the basis for the diagnosis of COVID-19, and during CT examination, ground-glass opacity was used as a COVID-19 indicator. Trail registration Trail registration number. 202012195, Date of registration: 2020.12.22 "retrospectively registered".
Keyphrases
- coronavirus disease
- end stage renal disease
- newly diagnosed
- ejection fraction
- chronic kidney disease
- sars cov
- healthcare
- emergency department
- magnetic resonance imaging
- prognostic factors
- pulmonary embolism
- computed tomography
- high resolution
- risk factors
- mass spectrometry
- machine learning
- data analysis
- sleep quality
- image quality