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Abstract
Recurrence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) positive detection in infected but recovered individuals has been reported. Patients who have recovered from coronavirus disease 2019 (COVID-19) could profoundly impact the health care system. We sought to define the kinetics and relevance of PCR-positive recurrence during recovery from acute COVID-19 to better understand risks for prolonged infectivity and reinfection. A series of 414 patients with confirmed SARS-Cov-2 infection, at The Second Affiliated Hospital of Southern University of Science and Technology in Shenzhen, China from January 11 to April 23, 2020. Statistical analyses were performed of the clinical, laboratory, radiologic image, medical treatment, and clinical course of admission/quarantine/readmission data, and a recurrence predictive algorithm was developed. 16.7% recovered patients with PCR positive recurring one to three times, despite being in strict quarantine. Younger patients with mild pulmonary respiratory syndrome had higher risk of PCR positivity recurrence. The recurrence prediction model had an area under the ROC curve of 0.786. This case series provides characteristics of patients with recurrent SARS-CoV-2 positivity. Use of a prediction algorithm may identify patients at high risk of recurrent SARS-CoV-2 positivity and help to establish protocols for health policy.
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1 The Second Affiliated Hospital of Southern University of Science and Technology, National Clinical Research Center for Infectious Disease, Shenzhen, China (GRID:grid.263817.9); Stanford University School of Medicine, Department of Surgery, Stanford, USA (GRID:grid.168010.e) (ISNI:0000000419368956)
2 Stanford University School of Medicine, Department of Cardiothoracic Surgery, Stanford, USA (GRID:grid.168010.e) (ISNI:0000000419368956); Betty Irene Moore Children’s Heart Center, Lucile Packard Children’s Hospital, Clinical and Translational Research Program, Palo Alto, USA (GRID:grid.414123.1) (ISNI:0000 0004 0450 875X)
3 Stanford University School of Medicine, Department of Surgery, Stanford, USA (GRID:grid.168010.e) (ISNI:0000000419368956)
4 The Second Affiliated Hospital of Southern University of Science and Technology, National Clinical Research Center for Infectious Disease, Shenzhen, China (GRID:grid.263817.9)
5 Stanford University School of Medicine, Department of Pediatrics, Stanford, USA (GRID:grid.168010.e) (ISNI:0000000419368956)
6 West China Hospital, Sichuan University, Translational Medicine Laboratory, Chengdu, China (GRID:grid.412901.f) (ISNI:0000 0004 1770 1022)
7 University of California at Riverside, Department of Bioengineering, Riverside, USA (GRID:grid.266097.c) (ISNI:0000 0001 2222 1582)
8 West China Hospital, Sichuan University, Biomedical Big Data Center, Chengdu, China (GRID:grid.412901.f) (ISNI:0000 0004 1770 1022); Sichuan University, Medical Big Data Center, Chengdu, China (GRID:grid.13291.38) (ISNI:0000 0001 0807 1581)
9 Stanford University, Department of Biomedical Data Science, Stanford, USA (GRID:grid.168010.e) (ISNI:0000000419368956)
10 The University of Hong Kong, Department of Medicine, Hong Kong, China (GRID:grid.194645.b) (ISNI:0000000121742757)
11 Stanford University School of Medicine, Department of Pediatrics, Stanford, USA (GRID:grid.168010.e) (ISNI:0000000419368956); Stanford University School of Medicine, Department of Health Research and Policy, Stanford, USA (GRID:grid.168010.e) (ISNI:0000000419368956)
12 Stanford University School of Medicine, Department of Surgery, Stanford, USA (GRID:grid.168010.e) (ISNI:0000000419368956); Betty Irene Moore Children’s Heart Center, Lucile Packard Children’s Hospital, Clinical and Translational Research Program, Palo Alto, USA (GRID:grid.414123.1) (ISNI:0000 0004 0450 875X)