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Real-world evaluation of AI driven COVID-19 triage for emergency admissions: External validation & operational assessment of lab-free and high-throughput screening solutions
Andrew AS Soltan; Jenny Yang; Ravi Pattanshetty; Alex Novak; Omid Rohanian; Sally Beer; Marina A Soltan; David R Thickett; Rory Fairhead; - CURIAL Translational Collaborative; Tingting Zhu; David W Eyre; David A Clifton.
Affiliation
  • Andrew AS Soltan; John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust; RDM Division of Cardiovascular Medicine, University of Oxford
  • Jenny Yang; Institute of Biomedical Engineering, Dept. Engineering Science, University of Oxford
  • Ravi Pattanshetty; John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust
  • Alex Novak; John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust
  • Omid Rohanian; Institute of Biomedical Engineering, Dept. Engineering Science, University of Oxford
  • Sally Beer; John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust
  • Marina A Soltan; The Queen Elizabeth Hospital, University Hospitals Birmingham NHS Foundation Trust; Institute of Inflammation and Ageing, University of Birmingham
  • David R Thickett; The Queen Elizabeth Hospital, University Hospitals Birmingham NHS Foundation Trust; Institute of Inflammation and Ageing, University of Birmingham
  • Rory Fairhead; University of Oxford Medical School
  • - CURIAL Translational Collaborative;
  • Tingting Zhu; Institute of Biomedical Engineering, Dept. Engineering Science, University of Oxford
  • David W Eyre; John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust; Big Data Institute, Nuffield Department of Population Health, University of Oxford; N
  • David A Clifton; Institute of Biomedical Engineering, Dept. Engineering Science, University of Oxford
Preprint in English | medRxiv | ID: ppmedrxiv-21262376
ABSTRACT
BackgroundUncertainty in patients COVID-19 status contributes to treatment delays, nosocomial transmission, and operational pressures in hospitals. However, typical turnaround times for batch-processed laboratory PCR tests remain 12-24h. Although rapid antigen lateral flow testing (LFD) has been widely adopted in UK emergency care settings, sensitivity is limited. We recently demonstrated that AI-driven triage (CURIAL-1.0) allows high-throughput COVID-19 screening using clinical data routinely available within 1h of arrival to hospital. Here we aimed to determine operational and safety improvements over standard-care, performing external/prospective evaluation across four NHS trusts with updated algorithms optimised for generalisability and speed, and deploying a novel lab-free screening pathway in a UK emergency department. MethodsWe rationalised predictors in CURIAL-1.0 to optimise separately for generalisability and speed, developing CURIAL-Lab with vital signs and routine laboratory blood predictors (FBC, U&E, LFT, CRP) and CURIAL-Rapide with vital signs and FBC alone. Models were calibrated during training to 90% sensitivity and validated externally for unscheduled admissions to Portsmouth University Hospitals, University Hospitals Birmingham and Bedfordshire Hospitals NHS trusts, and prospectively during the second-wave of the UK COVID-19 epidemic at Oxford University Hospitals (OUH). Predictions were generated using first-performed blood tests and vital signs and compared against confirmatory viral nucleic acid testing. Next, we retrospectively evaluated a novel clinical pathway triaging patients to COVID-19-suspected clinical areas where either model prediction or LFD results were positive, comparing sensitivity and NPV with LFD results alone. Lastly, we deployed CURIAL-Rapide alongside an approved point-of-care FBC analyser (OLO; SightDiagnostics, Israel) to provide lab-free COVID-19 screening in the John Radcliffe Hospitals Emergency Department (Oxford, UK), as trust-approved service improvement. Our primary improvement outcome was time-to-result availability; secondary outcomes were sensitivity, specificity, PPV, and NPV assessed against a PCR reference standard. We compared CURIAL-Rapides performance with clinician triage and LFD results within standard-care. Results72,223 patients met eligibility criteria across external and prospective validation sites. Model performance was consistent across trusts (CURIAL-Lab AUROCs range 0.858-0.881; CURIAL-Rapide 0.836-0.854), with highest sensitivity achieved at Portsmouth University Hospitals (CURIAL-Lab84.1% [95% Wilsons score CIs 82.5-85.7]; CURIAL-Rapide83.5% [81.8 - 85.1]) at specificities of 71.3% (95% Wilsons score CIs 70.9 - 71.8) and 63.6% (63.1 - 64.1). For 3,207 patients receiving LFD-triage within routine care for OUH admissions between December 23, 2021 and March 6, 2021, a combined clinical pathway increased sensitivity from 56.9% for LFDs alone (95% CI 51.7-62.0) to 88.2% with CURIAL-Rapide (84.4-91.1; AUROC 0.919) and 85.6% with CURIAL-Lab (81.6-88.9; AUROC 0.925). 520 patients were prospectively enrolled for point-of-care FBC analysis between February 18, 2021 and May 10, 2021, of whom 436 received confirmatory PCR testing within routine care and 10 (2.3%) tested positive. Median time from patient arrival to availability of CURIAL-Rapide result was 4500 min (32-64), 16 minutes (26.3%) sooner than LFD results (6100 min, 37-99; log-rank p<0.0001), and 652 h (90.2%) sooner than PCR results (737 h, 605-1539; p<0.0001). Sensitivity and specificity of CURIAL-Rapide were 87.5% (52.9-97.8) and 85.4% (81.3-88.7), therefore achieving high NPV (99.7%, 98.2-99.9). CURIAL-Rapide correctly excluded COVID-19 for 58.5% of negative patients who were triaged by a clinician to COVID-19-suspected (amber) areas. ImpactCURIAL-Lab & CURIAL-Rapide are generalisable, high-throughput screening tests for COVID-19, rapidly excluding the illness with higher NPV than LFDs. CURIAL-Rapide can be used in combination with near-patient FBC analysis for rapid, lab-free screening, and may reduce the number of COVID-19-negative patients triaged to enhanced precautions ( amber) clinical areas.
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Full text: Available Collection: Preprints Database: medRxiv Type of study: Diagnostic study / Experimental_studies / Observational study / Prognostic study Language: English Year: 2021 Document type: Preprint
Full text: Available Collection: Preprints Database: medRxiv Type of study: Diagnostic study / Experimental_studies / Observational study / Prognostic study Language: English Year: 2021 Document type: Preprint
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