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BACKGROUND: Early 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 generalisability. 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 6 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.
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document_parses/pdf_json/4e7c4ec6163f4ec6a1dbd57904260aceb88d9dd2.json
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Development and internal validation of a diagnostic prediction model for COVID-19 at time of admission to hospital
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