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Comment
. 2021 Apr;82(4):e20-e22.
doi: 10.1016/j.jinf.2020.12.026. Epub 2020 Dec 31.

Developing a multivariable risk prediction model to predict prolonged viral clearance in patients with COVID-19

Affiliations
Comment

Developing a multivariable risk prediction model to predict prolonged viral clearance in patients with COVID-19

Ying Liu et al. J Infect. 2021 Apr.
No abstract available

Keywords: COVID-19; Prediction model; SARS-CoV-2; Viral clearance.

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Conflict of interest statement

Declaration of Competing Interest All of the authors have no conflict of interest.

Figures

Fig 1
Fig. 1
Selection of potential predictors of the mode applying the LASSO regression analysis. (A) LASSO coefficient profiles of the potential included predictors. The coefficient profile plot was produced against the log(lambda) sequence. (B) Befitting λ selection in the LASSO regression analysis based on the 5-fold cross-validation. Partial likelihood deviance (binomial deviance) curve was plotted versus log (λ). Dotted vertical lines were drawn based on the minimum criteria and 1 standard error of the minimum criteria (the 1-SE criteria).
Fig 2
Fig. 2
Nomogram for predicting prolonged SARS-CoV-2 RNA clearance risk. The score of each predictors were added to obtain the total score and a vertical line was drawn on the total score to obtain the corresponding possibility of SARS-CoV-2 RNA prolonged shedding.

Comment on

References

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