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
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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.
Summary
When 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.
Article activity feed
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SciScore for 10.1101/2020.07.07.20148510: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: Thank you for sharing your data.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Our study has a number of limitations. Our CPR was derived using data from a single health system, with test eligibility criteria that followed CDC guidance from early in the pandemic; thus, as with other diagnostic CPR for SARS-CoV-2 (20), our CPR may not be generalizable and requires validation in other settings. Instead, we highlight the generalizability of the approach we have presented, and that the individual and population level impacts …
SciScore for 10.1101/2020.07.07.20148510: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: Thank you for sharing your data.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Our study has a number of limitations. Our CPR was derived using data from a single health system, with test eligibility criteria that followed CDC guidance from early in the pandemic; thus, as with other diagnostic CPR for SARS-CoV-2 (20), our CPR may not be generalizable and requires validation in other settings. Instead, we highlight the generalizability of the approach we have presented, and that the individual and population level impacts of prioritized testing are robust to the specific CPR used (Table S2). Secondly, our model assumes that all individuals seeking testing would present at the same time. In most clinical settings, the implementation of such a CPR would involve the use of a probability threshold, set based on data from the previous day(s) and the expected number of test eligible people. The optimal setting of this threshold, given stochastic testing demands and infection dynamics, would be an area for future exploration during clinical trials. Third, we did not consider the implications of the sensitivity and specificity of SARS-CoV-2 tests, as these values are not currently well known and evolving with new tests and optimization of sampling techniques. Low sensitivity and specificity in the diagnostic tests would reduce the utility of testing in general, and thus also of prioritized testing. Our SEIR model assumes complete and immediate compliance of isolation by infectious individuals who test positive for SARS-CoV-2. While this is unlikely to be the cas...
Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
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