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
© The Author(s) 2021. Published by Oxford University Press for the Infectious Diseases Society of America. All rights reserved. For permissions, e-mail: journals.permissionsoup.com..
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.
| Errataetall: | |
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| Medienart: |
E-Artikel |
| Erscheinungsjahr: |
2021 |
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| Erschienen: |
2021 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:73 |
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| Enthalten in: |
Clinical infectious diseases : an official publication of the Infectious Diseases Society of America - 73(2021), 10 vom: 16. Nov., Seite 1822-1830 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Reimer, Jody R [VerfasserIn] |
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| Links: |
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| Themen: |
Clinical prediction rule |
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| Anmerkungen: |
Date Completed 22.11.2021 Date Revised 07.12.2022 published: Print UpdateOf: medRxiv. 2020 Jul 08;:. - PMID 32676615 Citation Status MEDLINE |
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| doi: |
10.1093/cid/ciab177 |
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| funding: |
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| PPN (Katalog-ID): |
NLM321796780 |
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| 100 | 1 | |a Reimer, Jody R |e verfasserin |4 aut | |
| 245 | 1 | 4 | |a The Effects of Using a Clinical Prediction Rule to Prioritize Diagnostic Testing on Transmission and Hospital Burden |b A Modeling Example of Early Severe Acute Respiratory Syndrome Coronavirus 2 |
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| 500 | |a Date Revised 07.12.2022 | ||
| 500 | |a published: Print | ||
| 500 | |a UpdateOf: medRxiv. 2020 Jul 08;:. - PMID 32676615 | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a © The Author(s) 2021. Published by Oxford University Press for the Infectious Diseases Society of America. All rights reserved. For permissions, e-mail: journals.permissionsoup.com. | ||
| 520 | |a 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 | ||
| 520 | |a 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 | ||
| 520 | |a 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 | ||
| 520 | |a 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 | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Research Support, N.I.H., Extramural | |
| 650 | 4 | |a Research Support, Non-U.S. Gov't | |
| 650 | 4 | |a Research Support, U.S. Gov't, Non-P.H.S. | |
| 650 | 4 | |a clinical prediction rule | |
| 650 | 4 | |a diagnostic testing | |
| 650 | 4 | |a transmission dynamics | |
| 700 | 1 | |a Ahmed, Sharia M |e verfasserin |4 aut | |
| 700 | 1 | |a Brintz, Ben J |e verfasserin |4 aut | |
| 700 | 1 | |a Shah, Rashmee U |e verfasserin |4 aut | |
| 700 | 1 | |a Keegan, Lindsay T |e verfasserin |4 aut | |
| 700 | 1 | |a Ferrari, Matthew J |e verfasserin |4 aut | |
| 700 | 1 | |a Leung, Daniel T |e verfasserin |4 aut | |
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