Abstract
SummaryBackgroundUncertainty in patients' COVID-19 status contributes to treatment delays, nosocomial transmission, and operational pressures in hospitals. However, the typical turnaround time for laboratory PCR remains 12–24 h and lateral flow devices (LFDs) have limited sensitivity. Previously, we have shown that artificial intelligence-driven triage (CURIAL-1.0) can provide rapid COVID-19 screening using clinical data routinely available within 1 h of arrival to hospital. Here, we aimed to improve the time from arrival to the emergency department to the availability of a result, do external and prospective validation, and deploy a novel laboratory-free screening tool in a UK emergency department.MethodsWe optimised our previous model, removing less informative predictors to improve generalisability and speed, developing the CURIAL-Lab model with vital signs and readily available blood tests (full blood count [FBC]; urea, creatinine, and electrolytes; liver function tests; and C-reactive protein) and the CURIAL-Rapide model with vital signs and FBC alone. Models were validated externally for emergency admissions to University Hospitals Birmingham, Bedfordshire Hospitals, and Portsmouth Hospitals University National Health Service (NHS) trusts, and prospectively at Oxford University Hospitals, by comparison with PCR testing. Next, we compared model performance directly against LFDs and evaluated a combined pathway that triaged patients who had either a positive CURIAL model result or a positive LFD to a COVID-19-suspected clinical area. Lastly, we deployed CURIAL-Rapide alongside an approved point-of-care FBC analyser to provide laboratory-free COVID-19 screening at the John Radcliffe Hospital (Oxford, UK). Our primary improvement outcome was time-to-result, and our performance measures were sensitivity, specificity, positive and negative predictive values, and area under receiver operating characteristic curve (AUROC).Findings72 223 patients met eligibility criteria across the four validating hospital groups, in a total validation period spanning Dec 1, 2019, to March 31, 2021. CURIAL-Lab and CURIAL-Rapide performed consistently across trusts (AUROC range 0·858–0·881, 95% CI 0·838–0·912, for CURIAL-Lab and 0·836–0·854, 0·814–0·889, for CURIAL-Rapide), achieving highest sensitivity at Portsmouth Hospitals (84·1%, Wilson's 95% CI 82·5–85·7, for CURIAL-Lab and 83·5%, 81·8–85·1, for CURIAL-Rapide) at specificities of 71·3% (70·9–71·8) for CURIAL-Lab and 63·6% (63·1–64·1) for CURIAL-Rapide. When combined with LFDs, model predictions improved triage sensitivity from 56·9% (51·7–62·0) for LFDs alone to 85·6% with CURIAL-Lab (81·6–88·9; AUROC 0·925) and 88·2% with CURIAL-Rapide (84·4–91·1; AUROC 0·919), thereby reducing missed COVID-19 cases by 65% with CURIAL-Lab and 72% with CURIAL-Rapide. For the prospective deployment of CURIAL-Rapide, 520 patients were enrolled for point-of-care FBC analysis between Feb 18 and May 10, 2021, of whom 436 received confirmatory PCR testing and ten (2·3%) tested positive. Median time from arrival to a CURIAL-Rapide result was 45 min (IQR 32–64), 16 min (26·3%) sooner than with LFDs (61 min, 37–99; log-rank p<0·0001), and 6 h 52 min (90·2%) sooner than with PCR (7 h 37 min, 6 h 5 min to 15 h 39 min; p<0·0001). Classification performance was high, with sensitivity of 87·5% (95% CI 52·9–97·8), specificity of 85·4% (81·3–88·7), and negative predictive value of 99·7% (98·2–99·9). CURIAL-Rapide correctly excluded infection for 31 (58·5%) of 53 patients who were triaged by a physician to a COVID-19-suspected area but went on to test negative by PCR.InterpretationOur findings show the generalisability, performance, and real-world operational benefits of artificial intelligence-driven screening for COVID-19 over standard-of-care in emergency departments. CURIAL-Rapide provided rapid, laboratory-free screening when used with near-patient FBC analysis, and was able to reduce the number of patients who tested negative for COVID-19 but were triaged to COVID-19-suspected areas.FundingThe Wellcome Trust, University of Oxford Medical and Life Sciences Translational Fund.
Highlights
Reducing nosocomial transmission of SARS-CoV-2 is a priority in safeguarding patient and health-care staff safety, as inpatients are at greatest risk of severe illness and death.[1,2]
We have shown that an artificial intelligence screening test (CURIAL-1.0) rapidly identified patients attending the emergency department with COVID-19, using the routine blood test, blood gas, and vital signs collected within 1 h of presentation to the hospital.[5]
Diagnostic models to identify patients presenting with COVID-19 We updated our previously described model, designed to identify patients presenting to hospital with COVID-19 by use of vital signs, blood gas, and routine laboratory blood tests (CURIAL-1.0),[5] with additional training data to encompass all COVID-19 cases presenting to Oxford University Hospitals (OUH; Oxfordshire, UK) during the first pandemic wave
Summary
Viral testing is mandated for all patients admitted to UK hospitals, long turnaround times and triage failure can cause delays to care, nosocomial transmission, and operational strain in the emergency department.[3–5]. Turn-around times have shortened throughout the pandemic, typically to within 12–24 h in hospitals in high-income and middleincome countries, but the interim uncertainty about patients’ COVID-19 status might postpone safe transfers from the emergency department to SARS-CoV-2-free clinical areas and thereby contribute to nosocomial transmission.[1,8]. LFDs are laboratory-free and highly specific (>99·5%),[13] multiple reports have shown more limited sensitivity (about 40–70%),[14,15] leading to the US Food and Drug Administration issuing a class 1 recall of the Innova SARS-CoV-2 rapid antigen test on June 10, 2021.16 A recent e267 www.thelancet.com/digital-health Vol 4 April 2022
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