Development and internal validation of a diagnostic prediction model for COVID-19 at time of admission to hospital

QJM. 2021 Dec 20;114(10):699-705. doi: 10.1093/qjmed/hcaa305.

Abstract

Background: Early coronavirus disease 2019 (COVID-19) diagnosis prior to laboratory testing results is crucial for infection control in hospitals. Models exist predicting COVID-19 diagnosis, but significant concerns exist regarding methodology and generalizability.

Aim: To generate the first COVID-19 diagnosis risk score for use at the time of hospital admission using the TRIPOD (transparent reporting of a multivariable prediction model for individual prognosis or diagnosis) checklist.

Design: A multivariable diagnostic prediction model for COVID-19 using the TRIPOD checklist applied to a large single-centre retrospective observational study of patients with suspected COVID-19.

Methods: 581 individuals were admitted with suspected COVID-19; the majority had laboratory-confirmed COVID-19 (420/581, 72.2%). Retrospective collection was performed of electronic clinical records and pathology data.

Results: The final multivariable model demonstrated AUC 0.8535 (95% confidence interval 0.8121-0.8950). The final model used six clinical variables that are routinely available in most low and high-resource settings. Using a cut-off of 2, the derived risk score has a sensitivity of 78.1% and specificity of 86.8%. At COVID-19 prevalence of 10% the model has a negative predictive value (NPV) of 96.5%.

Conclusions: Our risk score is intended for diagnosis of COVID-19 in individuals admitted to hospital with suspected COVID-19. The score is the first developed for COVID-19 diagnosis using the TRIPOD checklist. It may be effective as a tool to rule out COVID-19 and function at different pandemic phases of variable COVID-19 prevalence. The simple score could be used by any healthcare worker to support hospital infection control prior to laboratory testing results.

Publication types

  • Observational Study

MeSH terms

  • COVID-19 Testing
  • COVID-19*
  • Hospitals
  • Humans
  • Retrospective Studies
  • SARS-CoV-2