An Artificial Intelligence-Assisted Diagnostic Platform for Rapid Near-Patient Hematology

Bachar, Neta; Benbassat, Dana; Brailovsky, D; Eshel, Yochay; Glück, Dan; Levner, Daniel; Levy, Sarah; Pecker, S; Yurkovsky, Evgeny; Zait, Amir; Sever, Cordelia E.; Kratz, Alexander; Brugnara, Carlo

doi:10.1101/2021.04.27.21255770

Cited by 2 publications

(5 citation statements)

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“…Separately, we optimised for result-time, developing a minimalist model (CURIAL-Rapide) considering only predictors that can be obtained by the patient bedside (FBC and vital signs). We selected the FBC due to recent approval of a point-of-care haematology analyser (OLO, SightDiagnostics, Israel) with a result-time of 10 minutes, and as explanability analyses showed FBC components were most informative 31 . Models were trained using the OUH first-wave dataset described above.…”

Section: Methodsmentioning

confidence: 99%

“…The copyright holder for this this version posted August 31, 2021. ; https://doi.org/10.1101/2021.08.24.21262376 doi: medRxiv preprint To prospectively assess operational and predictive performance of CURIAL-Rapide in a lab-free setting, we deployed two OLO rapid haematology analysers [SightDiagnostics, Tel Aviv] in the Emergency Department (ED) at the John Radcliffe Hospital, Oxford, as part of an OUH-approved service evaluation (Ulysses ID: 6907) 31 . We simultaneously aimed to improve routine clinical care by reducing the time for routine blood test results to become available in ED.…”

Section: Diagnostic Models To Identify Patients Presenting With Covid-19mentioning

confidence: 99%

“…Following trust procedures, vital signs were documented on the trust electronic health record [SEND; Sensyne Health], and blood bottles were labelled using printed labels from the electronic record. Two drops of venous blood (27uL) from a routinely-collected EDTA blood tube were extracted using a single-use sampling device, and prepared for OLO analysis by trained operators directed by on-screen instructions 31 . OLO results were uploaded immediately to the electronic medical record using the POCcelerator Data Management System [Siemens Healthineers GmbH, Erlangen, Germany], making results available to clinicians and supporting routine patient care.…”

Section: Supplementary Figure S1mentioning

confidence: 99%

“…The copyright holder for this this version posted August 31, 2021. ; https://doi.org/10.1101/2021.08. 24.21262376 doi: medRxiv preprint advances in near-patient diagnostics for lab-free COVID-19 screening 31 . First, we externally validate two models with rationalised predictors, optimised separately for throughput (CURIAL-Lab; vital signs & routine blood tests) and speed (CURIAL-Rapide; vital signs & FBC only), across three independent UK NHS hospital trusts and prospectively for the second wave of the UK COVID-19 epidemic at Oxford University Hospitals.…”

Section: Introductionmentioning

confidence: 99%

“…Accordingly, in this study we investigate efficacy, generalisability and real-world operational benefits of AI-driven COVID-19 screening in emergency departments, using insight from explainability analyses to improve generalisability and exploit advances in near-patient diagnostics for lab-free COVID-19 screening 31 . First, we externally validate two models with rationalised predictors, optimised separately for throughput (CURIAL-Lab; vital signs & routine blood tests) and speed (CURIAL- Rapide; vital signs & FBC only), across three independent UK NHS hospital trusts and prospectively for the second wave of the UK COVID-19 epidemic at Oxford University Hospitals.…”

Section: Introductionmentioning

confidence: 99%

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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

Soltan

Yang

Pattanshetty

et al. 2021

Preprint

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Background Uncertainty 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. Methods We 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 Hospital's 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-Rapide's performance with clinician triage and LFD results within standard-care. Results 72,223 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-Lab:84.1% [95% Wilson's score CIs 82.5-85.7]; CURIAL-Rapide:83.5% [81.8 - 85.1]) at specificities of 71.3% (95% Wilson's 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 45:00 min (32-64), 16 minutes (26.3%) sooner than LFD results (61:00 min, 37-99; log-rank p<0.0001), and 6:52 h (90.2%) sooner than PCR results (7:37 h, 6:05-15:39; 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. Impact CURIAL-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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Section: Methodsmentioning

confidence: 99%

Section: Diagnostic Models To Identify Patients Presenting With Covid-19mentioning

confidence: 99%

Section: Supplementary Figure S1mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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