Diagnostic accuracy and utility of SARS-CoV-2 antigen lateral flow assays in medical admissions with possible COVID-19

We evaluated diagnostic accuracy of the Innova SARS-CoV-2 Antigen Rapid Qualitative Test compared to SARS-CoV-2 RT-PCR from nasopharyngeal swabs in adult admissions who met the COVID-19 case definition at a busy acute hospital in the UK. We found the Innova SARS-CoV-2 Antigen Rapid Qualitative Test had a good specificity in patients with symptoms of COVID-19 presenting to hospital. The Innova LFA can be used to rapidly identify COVID-19 cases amongst hospital admissions meeting the COVID-19 case definition, allowing patients to be allocated to COVID-19 cohort areas.

Use of the FebriDx point-of-care assay as part of a triage algorithm for medical admissions with possible COVID-19

BackgroundPatients admitted to hospital with COVID-19 need rapid identification and isolation to prevent nosocomial transmission. However, isolation facilities are often limited, and SARS-CoV-2 RT-PCR results are often not available when discharged from the emergency department. We evaluated a triage algorithm to isolate patients with suspected COVID-19 using simple clinical criteria and the FebriDx assay. DesignRetrospective observational cohort SettingLarge acute care hospital in London, UK ParticipantsAll medical admissions from the ED between 10th August 2020 and 4th November 2020 with valid SARS-CoV-2 RT-PCR. InterventionsMedical admissions were triaged as likely, possible or unlikely COVID-19 based on clinical criteria. Patients triaged as possible COVID-19 underwent FebriDx lateral flow assay on capillary blood, and those positive for MxA were managed as likely COVID-19. Primary Outcome measuresDiagnostic accuracy (sensitivity, specificity and predictive values) of the algorithm and the FebriDx assay compared to SARS-CoV-2 RT-PCR from nasopharyngeal swabs as the reference standard. Results4.0% (136/3,443) of medical admissions had RT-PCR confirmed COVID-19. Prevalence of COVID-19 was 45.7% (80/175) in those triaged as likely, 4.1% (50/1,225) in possible and 0.3% (6/2,033) in unlikely COVID-19. Compared to SARS-CoV-2 RT-PCR, clinical triage had sensitivity of 95.6% (95%CI: 90.5% - 98.0%) and specificity of 61.5% (95%CI: 59.8% - 63.1%), whilst the triage algorithm including FebriDx had sensitivity of 92.6% (95%CI: 86.8% - 96.0%) and specificity of 86.4% (95%CI: 85.2% - 87.5%). The triage algorithm reduced the need for 2,859 patients to be admitted to isolation rooms. Ten patients missed by the algorithm had mild or asymptomatic COVID-19. ConclusionsA triage algorithm including FebriDx assay had good sensitivity and was useful to rule-out COVID-19 among medical admissions to hospital. STRENGTHS AND LIMITATIONS OF THIS STUDYO_LIPragmatic study including a large cohort of consecutive medical admissions providing routine clinical care. C_LIO_LIA single SARS-CoV-2 RT-PCR is an imperfect reference standard for COVID-19. Multiple RT-PCR platforms used, with different PCR targets and performance. C_LIO_LIA higher prevalence of COVID-19 or other respiratory pathogens might alter performance. C_LIO_LICriteria for likely and possible COVID-19 groups changed subtly during the study period. C_LI

Predicting COVID19 Critical Care Beds - The London North-West University Healthcare Trust Experience

Our trust has an urgent need to make short-term (3-4 days in advance) informed operational decisions which take into account best-practice treatment regimens and known clinical features of COVID19 inpatients. We believe that any model which is relied upon for operational decision making should have clinically identifiable parameters. Our models parameters take into account the conversion rates from acute wards into wards equipped with Non-Invasive Ventilation (NIV) and Mechanical Ventilation (MV), the typical time that these conversions take place and, the historical non-COVID usage of NIV and MV beds. We have observed that this clinical performance is mathematically identical to a series of linear delays on the time varying inpatient level. High frequency inpatient data, sampled [~]4 hourly, has allowed our hospital trust to predict total critical care usage up to 4 days in advance without making any assumptions on upcoming inpatients. It is based entirely upon current bed occupancy levels and measured clinical pathways. Through back-testing over the recent 4 months, the bounds of this model include 93.8% of all 4 day inpatient sequences. The average next-day error is 0.8 (95% CI: 0.44, 1.15) and so the system tends to over-predict the next day critical care inpatients by approximately 1 bed. Potential extensions to the basic model include adjustments for seasonality, case mix, probabilistic marginalisation and known discharges.