Selected article for: "accuracy specificity sensitivity and liver function"
Author: Fredi A Diaz-Quijano; Jose Mario Nunes da Silva; Fabiana Ganem; Silvano Oliveira; Andrea Liliana Vesga-Varela; Julio Croda
Title: A model to predict SARS-CoV-2 infection based on the first three-month surveillance data in Brazil.
  • Document date: 2020_4_8
    • ID: 3dzv20b7_23
    Snippet: We obtained a model integrating 15 covariates, including age, days from notification of the first confirmed case (DNFCC) in the corresponding FU, eight variables about clinical manifestations, two on comorbidities, trip history, and two interaction terms ( Table 2 ). The AUC of this multiple model was estimated at 95.36% (95% CI: 94.2 -96.52%) with an accuracy of 89.5%. To obtain the final function, patients with a history of liver disease and th.....
    Document: We obtained a model integrating 15 covariates, including age, days from notification of the first confirmed case (DNFCC) in the corresponding FU, eight variables about clinical manifestations, two on comorbidities, trip history, and two interaction terms ( Table 2 ). The AUC of this multiple model was estimated at 95.36% (95% CI: 94.2 -96.52%) with an accuracy of 89.5%. To obtain the final function, patients with a history of liver disease and those who denied having had any contact with a suspected case were considered with a predicted value equal to zero. With this inclusion, the area was 95.54% (95% CI: 94.41% -96.67%) for the diagnosis of COVID-19 in the modeling dataset and 95.01% (92.51% -97.5%) in the validation dataset ( Figure 3 ). Accuracy in these datasets was 90.1% (sensitivity 87.62% and specificity 92.02%) and 89.47% (sensitivity 87.32% and specificity 91.36%), respectively.

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