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23rd October 2020
This information is also important to inform the requirements for in-patient and outpatient isolation and it is necessary to gain a better insight of the potential significance of positive PCR tests over longer periods of time. Now researchers from the Division of Infectious Diseases, Oregon University, have undertaken a review of published data to determine the duration of viral shedding among those infected with COVID-19 and their findings have implications for the risk of transmission. The team queries public databases including PubMed, LitCoVID, the WHO COVID-19 literature repository and Google scholar for relevant articles. In each case, the articles were reviewed and assessed in terms of the design, population, healthcare setting, diagnostic testing methods and patient symptoms and illness severity.
A total of 77 studies were eligible for analysis and included prospective case studies, retrospective series, case reports, point prevalence studies and position statements. Only 59 studies were peer-reviewed, 6 were pre-prints and 13 researcher letters or a letter to the editor of a journal and 70 of the studies described hospitalised patients. All of the studies reported PCR-based assessment of viral shedding and 12 studies reported viral culture data. In terms of viral shedding, the data revealed that the duration ranged from a minimum of 1 day to 83 days although the pooled median duration of RNA shedding from respiratory samples based on 28 studies was 18.4 days (95% CI 15.5–21.3 days). When stratifying by disease severity, the median duration of RNA shedding was 17.2 days (95% CI 14–20.5 days) for those with mild to moderate disease and 19.8 days (95% CI 16.2–23.5 days) for those with severe disease. In general terms, the authors found that viral loads were highest within 1–2 weeks of illness onset but declined gradually although this rarely extended past 25 days. In discussing their results, the authors noted that while PCR positive tests can be prolonged, viral culture data suggested that viable virus samples could only be obtained from between 6 days prior to symptom onset but no later than 20 days after.
Fontana L et al. Understanding viral shedding of SARS-CoV-2: review of current literature. Infect Control Hosp Epidemiol 2020;1-35. doi:10.1017/ice.2020.1273
19th October 2020
In two of the previous cases, the level of symptoms were much less upon re-infection; however, for the latest case study, symptoms were much worse with the second infection.
The case was reported by a team from the Nevada Institute of Personalised Medicine, USA and relates to a 25-year old man with no underlying health issues or use of immunosuppressant therapy. His initial infection was towards the end of March and a positive PCR test for COVID-19 was obtained on the 18 April 2020 though his symptoms resolved after a month. The patient continued to feel well until the end of May 2020 when he re-presented to health services and found to be hypoxic with shortness of breath and admitted to hospital and required ongoing oxygen support while hospitalised. A second PCR COVID-19 test in early June was also positive although an antibody test was also performed which detected IgG and IgM antibodies against COVID-19. Viral material from the second PCR test was collected and subjected to genomic sequencing.
The genomic analysis revealed that the two viral samples were significantly genetically distinct. In addition, the authors performed a survey of COVID-19 strains identified within the Nevada area which revealed that both genotypes were in circulation. Thus the patient was infected with a different strain of COVID-19 on both occasions although the latter infection appeared to be more virulent. In commenting on their findings, the authors noted that the results had important implications for vaccine development in that the initial infection was unable to generate a sufficient immune response to against the subsequent episode of infectivity. However, a recognised limitation was that they were unable to assess the patients’ immune response after the first infection.
The authors called for further work on genomic sequencing of positive cases to enable effective health surveillance to help identify any such cases of COVID-19 re-infection in the future.
Tillett RL et al. Genomic evidence for reinfection with SARS-CoV-2: a case study. Lancet Infect Dis 2020; https://doi.org/10.1016/ S1473-3099(20)30764-7
9th October 2020
Now a team from Weill Cornell Medicine, New York, has created an artificial intelligence (AI) system that can use routine test data results to determine if a patient has COVID-19. Normally, clinicians order a battery of blood tests in addition to a PCR test, including routine laboratory tests and a chest X-ray and these results are generally available within 1 – 2 hours. Researchers therefore hypothesised if the results of the routine laboratory test could be used to predict if someone was infected with COVID-19 without the PCR test. The included patient demographics such as age, sex, race into a machine learning model and incorporated the results for 27 routine tests. The laboratory results were made available two days before the PCR test result. The dataset included a total of 5893 patients admitted to hospital between March and April 2020 and they excluded individuals under 18 years of age and those who PCR result was inconclusive and patients without laboratory test results within two days prior to the PCR test.
A total of 3356 patients who were tested for COVID-19 were included with a mean age of 56 years of whom, 1402 were positive and 54% emergency department admissions. Using a machine learning technique known as a gradient boosting decision tree, overall, the algorithm identified COVID-19 positivity with a sensitivity of 76% and a specificity of 81%. However, limiting the analysis to emergency department patients, increased the sensitivity to 80% and the specificity to 83%. Moreover, the algorithm correctly identified those who had a negative COVID-19 test result. A recognised limitation was the testing was specific those admitted to hospital with moderate to severe disease and thus requires further work to identify milder cases.
Nevertheless, the authors concluded that their algorithm is potentially of value in identifying whether patients have COVID-19 before they receive the results of a PCR test.
Yang HS et al. Routine laboratory blood tests predict SARS-CoV-2 infection using machine learning. Clin Chem 2020; https://doi.org/10.1093/clinchem/hvaa200