Repeated SARS-CoV-2 Positivity: Analysis of 123 Cases.
Szilárd VáncsaFanni DembrovszkyNelli FarkasLajos SzakóBrigitta TeutschStefania BunducRita NagyAndrea PárniczkyBálint ErőssZoltán PéterfiPéter HegyiPublished in: Viruses (2021)
Repeated positivity and reinfection with severe acute respiratory syndrome coronavirus 2 (SARS-COV-2) is a significant concern. Our study aimed to evaluate the clinical significance of repeatedly positive testing after coronavirus disease 2019 (COVID-19) recovery. We performed a systematic literature search following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guideline. With available individual patient data reporting on repeatedly SARS-CoV-2 positive (RSP) patients, case reports, and case series were included in this analysis. We performed a descriptive analysis of baseline characteristics of repeatedly positive cases. We assessed the cases according to the length of their polymerase chain reaction (PCR) negative interval between the two episodes. Risk factors for the severity of second episodes were evaluated. Overall, we included 123 patients with repeated positivity from 56 publications, with a mean repeated positivity length of 47.8 ± 29.9 days. Younger patients were predominant in the delayed (>90 days) recurrent positive group. Furthermore, comparing patients with RSP intervals of below 60 and above 60 days, we found that a more severe disease course can be expected if the repeated positivity interval is shorter. Severe and critical disease courses might predict future repeatedly positive severe and critical COVID-19 episodes. In conclusion, our results show that the second episode of SARS-CoV-2 positivity is more severe if it happens within 60 days after the first positive PCR. On the other hand, the second episode's severity correlates with the first.
Keyphrases
- sars cov
- respiratory syndrome coronavirus
- coronavirus disease
- systematic review
- end stage renal disease
- meta analyses
- newly diagnosed
- early onset
- ejection fraction
- peritoneal dialysis
- prognostic factors
- case report
- patient reported outcomes
- machine learning
- big data
- data analysis
- adverse drug
- cross sectional
- artificial intelligence