Derivation and Internal Validation of a Model to Predict the Probability of Severe Acute Respiratory Syndrome Coronavirus-2 Infection in Community People.
J Gen Intern Med
; 36(1): 162-169, 2021 01.
Article
en En
| MEDLINE
| ID: mdl-33107007
ABSTRACT
BACKGROUND:
The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes COVID-19 disease. There are concerns regarding limited testing capacity and the exclusion of cases from unproven screening criteria. Knowing COVID-19 risks can inform testing. This study derived and assessed a model to predict risk of SARS-CoV-2 in community-based people.METHODS:
All people presenting to a community-based COVID-19 screening center answered questions regarding symptoms, possible exposure, travel, and occupation. These data were anonymously linked to SARS-CoV-2 testing results. Logistic regression was used to derive a model to predict SARS-CoV-2 infection. Bootstrap sampling evaluated the model.RESULTS:
A total of 9172 consecutive people were studied. Overall infection rate was 6.2% but this varied during the study period. SARS-CoV-2 infection likelihood was primarily influenced by contact with a COVID-19 case, fever symptoms, and recent case detection rates. Internal validation found that the SARS-CoV-2 Risk Prediction Score (SCRiPS) performed well with good discrimination (c-statistic 0.736, 95%CI 0.715-0.757) and very good calibration (integrated calibration index 0.0083, 95%CI 0.0048-0.0131). Focusing testing on people whose expected SARS-CoV-2 risk equaled or exceeded the recent case detection rate would increase the number of identified SARS-CoV-2 cases by 63.1% (95%CI 54.5-72.3).CONCLUSION:
The SCRiPS model accurately estimates the risk of SARS-CoV-2 infection in community-based people undergoing testing. Using SCRiPS can importantly increase SARS-CoV-2 infection identification when testing capacity is limited.Palabras clave
Texto completo:
1
Banco de datos:
MEDLINE
Asunto principal:
Medición de Riesgo
/
Prueba de COVID-19
/
COVID-19
Tipo de estudio:
Diagnostic_studies
/
Etiology_studies
/
Prognostic_studies
/
Risk_factors_studies
Límite:
Adolescent
/
Adult
/
Aged
/
Aged80
/
Female
/
Humans
/
Male
/
Middle aged
País como asunto:
America do norte
Idioma:
En
Año:
2021
Tipo del documento:
Article