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. 2021 Mar;25(21):1–68. doi: 10.3310/hta25210

Modelling of hypothetical SARS-CoV-2 point-of-care tests on admission to hospital from A&E: rapid cost-effectiveness analysis.

Matt Stevenson, Andrew Metry, Michael Messenger
PMCID: PMC8020197  PMID: 33764295

Abstract

BACKGROUND

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the virus that causes coronavirus disease 2019. At the time of writing (October 2020), the number of cases of COVID-19 had been approaching 38 million and more than 1 million deaths were attributable to it. SARS-CoV-2 appears to be highly transmissible and could rapidly spread in hospital wards.

OBJECTIVE

The work undertaken aimed to estimate the clinical effectiveness and cost-effectiveness of viral detection point-of-care tests for detecting SARS-CoV-2 compared with laboratory-based tests. A further objective was to assess occupancy levels in hospital areas, such as waiting bays, before allocation to an appropriate bay.

PERSPECTIVE/SETTING

The perspective was that of the UK NHS in 2020. The setting was a hypothetical hospital with an accident and emergency department.

METHODS

An individual patient model was constructed that simulated the spread of disease and mortality within the hospital and recorded occupancy levels. Thirty-two strategies involving different hypothetical SARS-CoV-2 tests were modelled. Recently published desirable and acceptable target product profiles for SARS-CoV-2 point-of-care tests were modelled. Incremental analyses were undertaken using both incremental cost-effectiveness ratios and net monetary benefits, and key patient outcomes, such as death and intensive care unit care, caused directly by COVID-19 were recorded.

RESULTS

A SARS-CoV-2 point-of-care test with a desirable target product profile appears to have a relatively small number of infections, a low occupancy level within the waiting bays, and a high net monetary benefit. However, if hospital laboratory testing can produce results in 6 hours, then the benefits of point-of-care tests may be reduced. The acceptable target product profiles performed less well and had lower net monetary benefits than both a laboratory-based test with a 24-hour turnaround time and strategies using data from currently available SARS-CoV-2 point-of-care tests. The desirable and acceptable point-of-care test target product profiles had lower requirement for patients to be in waiting bays before being allocated to an appropriate bay than laboratory-based tests, which may be of high importance in some hospitals. Tests that appeared more cost-effective also had better patient outcomes.

LIMITATIONS

There is considerable uncertainty in the values for key parameters within the model, although calibration was undertaken in an attempt to mitigate this. The example hospital simulated will also not match those of decision-makers deciding on the clinical effectiveness and cost-effectiveness of introducing SARS-CoV-2 point-of-care tests. Given these limitations, the results should be taken as indicative rather than definitive, particularly cost-effectiveness results when the relative cost per SARS-CoV-2 point-of-care test is uncertain.

CONCLUSIONS

Should a SARS-CoV-2 point-of-care test with a desirable target product profile become available, this appears promising, particularly when the reduction on the requirements for waiting bays before allocation to a SARS-CoV-2-infected bay, or a non-SARS-CoV-2-infected bay, is considered. The results produced should be informative to decision-makers who can identify the results most pertinent to their specific circumstances.

FUTURE WORK

More accurate results could be obtained when there is more certainty on the diagnostic accuracy of, and the reduction in time to test result associated with, SARS-CoV-2 point-of-care tests, and on the impact of these tests on occupancy of waiting bays and isolation bays. These parameters are currently uncertain.

FUNDING

This report was commissioned by the National Institute for Health Research (NIHR) Evidence Synthesis programme as project number 132154. This project was funded by the NIHR Health Technology Assessment programme and will be published in full in Health Technology Assessment; Vol. 25, No. 21. See the NIHR Journals Library website for further project information.

Plain language summary

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the virus that causes coronavirus disease 2019 (COVID-19). SARS-CoV-2 is highly infectious, and this can cause problems in hospitals, where the virus can spread quickly. Laboratory-based tests can determine whether or not a patient has SARS-CoV-2, but these tests are not perfect and can require a considerable time to provide a result. Point-of-care tests to detect SARS-CoV-2 are being developed that may have much shorter times to a test result, although these are likely to be less accurate than laboratory-based tests. The benefit of quicker tests is that a decision to put a patient in a SARS-CoV-2-infected bay or in a non-SARS-CoV-2-infected bay can be made sooner, limiting contact between patients with SARS-CoV-2 and patients without SARS-CoV-2 and reducing the risk of infection transmission. The disadvantage of reduced accuracy is that some patients may be allocated to the wrong bay, increasing the risk of SARS-CoV-2 infection. A computer model was built to explore the impact of using SARS-CoV-2 point-of-care tests for people admitted to hospital. This model estimated the number of infections and deaths due to COVID-19, the costs of testing, and the number of people waiting to be put in an appropriate bay. Strategies were run using different values, including the time to get a test result, the accuracy of tests and whether or not staff who do not have symptoms should be tested. The results of the model indicated that point-of-care tests could be good if there was a large reduction in the time to get a test result and if accuracy was high. However, it is not certain whether or not such tests will become available. When newer SARS-CoV-2 tests are available, the model will allow an estimate of the clinical effectiveness and cost-effectiveness of the test to be made.

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