Epidemiological and clinical features of asymptomatic patients with SARS-CoV-2 infection.
Tianmin XuRui HuangLi ZhuJian WangJuan ChengBiao ZhangHaiyan ZhaoKang ChenHuaping ShaoChuanwu ZhuChao WuLonggen LiuPublished in: Journal of medical virology (2020)
Few studies reported the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infected patients with completely asymptomatic throughout the disease course. We investigated the epidemiological and clinical features of patients infected by SARS-CoV-2 without any symptoms. Patients with confirmed SARS-CoV-2 infection were retrospectively recruited. The demographic characteristics, clinical data, treatment, and outcomes of SARS-CoV-2 infected patients without any symptoms were analyzed. Fifteen (4.4%) of 342 SARS-CoV-2 infected patients did not develop any symptom during the course of the disease. The median time from exposure to diagnosis was 7.0 days (interquartile range [IQR]: 1.0-15.0 days). Of the 15 patients, 14 patients were diagnosed by tested positive for SARS-CoV-2 in throat swabs, while one patient was only tested positive for SARS-CoV-2 in anal swabs. During hospitalization, only 1 (6.7%) patient developed lymphopenia. Abnormalities of chest computed tomography examinations were detected in 8 (53.4%) patients on admission. As of 8 March 2020, all patients have been discharged. The median time of SARS-CoV-2 tested negative from admission was 7.0 days (IQR: 4.0-9.0 days). Patients without any symptoms but with SARS-CoV-2 exposure should be closely monitored and tested for SARS-CoV-2 both in anal and throat swabs to excluded the infection. Asymptomatic patients infected by SARS-CoV-2 have favorable outcomes.
Keyphrases
- sars cov
- respiratory syndrome coronavirus
- end stage renal disease
- ejection fraction
- chronic kidney disease
- newly diagnosed
- computed tomography
- prognostic factors
- peritoneal dialysis
- emergency department
- type diabetes
- magnetic resonance imaging
- metabolic syndrome
- magnetic resonance
- machine learning
- physical activity
- high resolution
- insulin resistance
- deep learning
- artificial intelligence
- contrast enhanced
- electronic health record
- atomic force microscopy
- glycemic control