The Effects of Using a Clinical Prediction Rule to Prioritize Diagnostic Testing on Transmission and Hospital Burden: A Modeling Example of Early Severe Acute Respiratory Syndrome Coronavirus 2
Year: 2021
Type: article
Abstract: BACKGROUND: Prompt identification of infections is critical for slowing the spread of infectious diseases. However, diagnostic testing shortages are common in emerging diseases, low resource settings, and during outbreaks. This forces difficult decisions regarding who receives a test, often without knowing the implications of those decisions on population-level transmission dynamics. Clinical prediction rules (CPRs) are commonly used tools to guide clinical decisions. METHODS: Using early severe acute respiratory syndrome coronavirus disease 2 (SARS-CoV-2) as an example, we used data from electronic health records to develop a parsimonious 5-variable CPR to identify those who are most likely to test positive. To consider the implications of gains in daily case detection at the population level, we incorporated testing using the CPR into a compartmentalized model of SARS-CoV-2. RESULTS: We found that applying this CPR (area under the curve, 0.69; 95% confidence interval, .68-.70) to prioritize testing increased the proportion of those testing positive in settings of limited testing capacity. We found that prioritized testing led to a delayed and lowered infection peak (ie, "flattens the curve"), with the greatest impact at lower values of the effective reproductive number (such as with concurrent community mitigation efforts), and when higher proportions of infectious persons seek testing. In addition, prioritized testing resulted in reductions in overall infections as well as hospital and intensive care unit burden. CONCLUSION: We highlight the population-level benefits of evidence-based allocation of limited diagnostic capacity.SummaryWhen the demand for diagnostic tests exceeds capacity, the use of a clinical prediction rule to prioritize diagnostic testing can have meaningful impact on population-level outcomes, including delaying and lowering the infection peak, and reducing healthcare burden.
Source: Clinical Infectious Diseases
DOI: 10.1093/cid/ciab177
Institutions: University of Utah, Pennsylvania State University
Language: English
Subfield: Modeling and Simulation
Field: Mathematics
Domain: Physical Sciences
Open Access status: bronze
Funders: National Science Foundation, University of Utah, National Institutes of Health, Centers for Disease Control and Prevention, National Institute of Food and Agriculture, National Heart, Lung, and Blood Institute, National Institute of Allergy and Infectious Diseases, National Center for Advancing Translational Sciences
Awards: Improving Cardiovascular Drug Safety With Automated Bleeding Classification, Utah Center for Clinical and Translational Science, G6125647192, US-UK Collab: Adaptive surveillance and control for endemic disease elimination, MODELING AND SIMULATION TO SUPPORT EPIDEMIOLOGICAL DECISION-MAKING IN HEALTHCARE SETTINGS, Development of clinical decision tools for management of diarrhea of children in high and low resource settings
OpenAlex ID: W3131154756