Derivation and Internal Validation of a Model to Predict the Probability of Severe Acute Respiratory Syndrome Coronaviru
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Medicine and Epidemiology & Community Medicine, University of Ottawa, ASB1-003 1053 Carling Ave, Ottawa, ON, Canada; 2Ottawa Hospital Research Institute, Ottawa, Canada; 3ICES uOttawa, Ottawa, Canada; 4Eastern Ontario Regional Laboratory Association, Ottawa, Canada.
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. KEY WORDS: COVID-19 disease; SARS-CoV-2; prediction. J Gen Intern Med DOI: 10.1007/s11606-020-06307-x © Society of General Internal Medicine 2020
Electronic supplementary material The online version of this article (https://doi.org/10.1007/s11606-020-06307-x) contains supplementary material, which is available to authorized users. Received May 28, 2020 Accepted October 7, 2020
BACKGROUND
Identifying people who are infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the cause of COVID-19 disease, is an important component of limiting its spread in the community.1 The detection and isolation of community-based people who are infected with the virus should significantly decrease spread of COVID-19 disease.2 Appropriate testing returns essential information regarding the pandemic.3 Being able to accurately predict the risk of SARS-CoV-2 infection would be helpful. First, resources to test for SARSCoV-2 may be limited,4,5 making the selection of people for testing essential to infection control.1 In such situations, selectively testing people with higher infection risks will maximize the number of identified cases f
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