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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.
Reimer, Jody R; Ahmed, Sharia M; Brintz, Ben J; Shah, Rashmee U; Keegan, Lindsay T; Ferrari, Matthew J; Leung, Daniel T.
  • Reimer JR; Department of Mathematics, University of Utah, Salt Lake City, Utah, USA.
  • Ahmed SM; Department of Internal Medicine, Division of Infectious Diseases, University of Utah School of Medicine, Salt Lake City Utah, USA.
  • Brintz BJ; Department of Internal Medicine, Division of Infectious Diseases, University of Utah School of Medicine, Salt Lake City Utah, USA.
  • Shah RU; Department of Internal Medicine, Division of Epidemiology, University of Utah School of Medicine, Salt Lake City, Utah, USA.
  • Keegan LT; Department of Internal Medicine, Division of Cardiovascular Medicine, University of Utah School of Medicine, Salt Lake City Utah, USA.
  • Ferrari MJ; Department of Internal Medicine, Division of Epidemiology, University of Utah School of Medicine, Salt Lake City, Utah, USA.
  • Leung DT; Department of Biology, The Pennsylvania State University, State College, Pennsylvania, USA.
Clin Infect Dis ; 73(10): 1822-1830, 2021 11 16.
Article in English | MEDLINE | ID: covidwho-1522141
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.
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Full text: Available Collection: International databases Database: MEDLINE Document Type: Article Main subject: SARS-CoV-2 / COVID-19 Subject: SARS-CoV-2 / COVID-19 Type of study: Diagnostic study / Prognostic study / Risk factors Language: English Journal: Clin Infect Dis Clinical aspect: Prediction / Prognosis Year: 2021

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Full text: Available Collection: International databases Database: MEDLINE Document Type: Article Main subject: SARS-CoV-2 / COVID-19 Subject: SARS-CoV-2 / COVID-19 Type of study: Diagnostic study / Prognostic study / Risk factors Language: English Journal: Clin Infect Dis Clinical aspect: Prediction / Prognosis Year: 2021
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