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1. Combined Point-of-Care Nucleic Acid and Antibody Testing for SARS-CoV-2 following Emergence of D614G Spike Variant

Author

Petra Mlcochova, Dami Collier, Allyson Ritchie, Sonny M. Assennato, Myra Hosmillo, Neha Goel, Bo Meng, Krishna Chatterjee, Vivien Mendoza, Nigel Temperton, Leo Kiss, Leo C. James, Katarzyna A. Ciazynska, Xiaoli Xiong, John A.G. Briggs, James A. Nathan, Federica Mescia, Laura Bergamaschi, Hongyi Zhang, Petros Barmpounakis, Nikos Demeris, Richard Skells, Paul A. Lyons, John Bradley, Steven Baker, Jean Pierre Allain, Kenneth G.C. Smith, Rachel Bousfield, Michael Wilson, Dominic Sparkes, Glenn Amoroso, Effrosyni Gkrania-Klotsas, Susie Hardwick, Adrian Boyle, Ian Goodfellow, Ravindra K. Gupta, Stephen Baker, Gordon Dougan, Ravi Gupta, Paul J. Lehner, Paul Lyons, Nicholas J. Matheson, Mark Toshner, Michael P. Weekes, Nick Brown, Martin Curran, Surendra Palmar, David Enoch, Daniel Chapman, Ashley Shaw, Sherly Jose, Areti Bermperi, Julie Ann Zerrudo, Evgenia Kourampa, Laura Watson, Jieniean Worsley, Caroline Saunders, Ranalie de Jesus, Jason Domingo, Ciro Pasquale, Bensi Vergese, Phoebe Vargas, Marivic Fabiculana, Marlyn Perales, Lee Mynott, Elizabeth Blake, Amy Bates, Anne-Laure Vallier, Alexandra Williams, David Phillips, Edmund Chiu, Alex Overhill, Nicola Ramenatte, Jamal Sipple, Steven Frost, Helena Knock, Richard Hardy, Emily Foster, Fiona Davidson, Viona Rundell, Purity Bundi, Richmond Abeseabe, Sarah Clark, Isabel Vicente, Anne Elmer, Carla Ribeiro, Jenny Kourampa, Jane Kennet, Jane Rowlands, Anne Meadows, Criona O’Brien, Rebecca Rastall, Cherry Crucusio, Sarah Hewitt, Jane Price, Jo Calder, Laura Canna, Ashlea Bucke, Hugo Tordesillas, Julie Harris, Valentina Ruffolo, Barbara Graves, Helen Butcher, Daniela Caputo, Emma Le Gresley, Benjamin J. Dunmore, Jennifer Martin, Ekaterina Legchenko, Carmen Treacy, Christopher Huang, Jennifer Wood, Rachel Sutcliffe, Josh Hodgson, Joy Shih, Stefan Graf, Zhen Tong, Tobias Tilly, Ciara O’Donnell, Kelvin Hunter, Linda Pointon, Nicole Pond, Marta Wylot, Emma Jones, Stuart Fawke, Ben Bullman, Lori Turner, Isobel Jarvis, Ommar Omarjee, Aloka De Sa, Joe Marsden, Ariana Betancourt, Marianne Perera, Maddie Epping, Nathan Richoz, Georgie Bower, Rahul Sharma, Francesca Nice, Oisin Huhn, Natalia Savoinykh Yarkoni, Nika Romashova, Daniel Lewis, Andrew Hinch, Chiara Cossetti, Mateusz Strezlecki, Richard Grenfell, Hannah Stark, Neil Walker, Kathy Stirrups, Nigel Ovington, Eleanor Dewhust, Emily Li, Sofia Papadia, Nathalie Kingston, Andrew Lever, Estee Torok, William Hamilton, Grant Hall, Aminu Jahun, Yasmin Chaudhry, Malte Pinckert, Iliana Georgana, Anna Yakovleva, Laura Caller, Sarah Caddy, Theresa Feltwell, Fahad Khokhar, Luke Meredith, Charlotte Holdcroft, and Surendra Parmar

Subjects
SARS-CoV-2, COVID-19, rapid diagnoses, point of care testing, D614G, Medicine (General), R5-920
Abstract

Summary: Rapid COVID-19 diagnosis in the hospital is essential, although this is complicated by 30%–50% of nose/throat swabs being negative by SARS-CoV-2 nucleic acid amplification testing (NAAT). Furthermore, the D614G spike mutant dominates the pandemic and it is unclear how serological tests designed to detect anti-spike antibodies perform against this variant. We assess the diagnostic accuracy of combined rapid antibody point of care (POC) and nucleic acid assays for suspected COVID-19 disease due to either wild-type or the D614G spike mutant SARS-CoV-2. The overall detection rate for COVID-19 is 79.2% (95% CI 57.8–92.9) by rapid NAAT alone. The combined point of care antibody test and rapid NAAT is not affected by D614G and results in very high sensitivity for COVID-19 diagnosis with very high specificity.

Published
2020
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2. Point of Care Nucleic Acid Testing for SARS-CoV-2 in Hospitalized Patients: A Clinical Validation Trial and Implementation Study

Author

Dami A. Collier, Sonny M. Assennato, Ben Warne, Nyarie Sithole, Katherine Sharrocks, Allyson Ritchie, Pooja Ravji, Matthew Routledge, Dominic Sparkes, Jordan Skittrall, Anna Smielewska, Isobel Ramsey, Neha Goel, Martin Curran, David Enoch, Rhys Tassell, Michelle Lineham, Devan Vaghela, Clare Leong, Hoi Ping Mok, John Bradley, Kenneth G.C. Smith, Vivienne Mendoza, Nikos Demiris, Martin Besser, Gordon Dougan, Paul J. Lehner, Mark J. Siedner, Hongyi Zhang, Claire S. Waddington, Helen Lee, Ravindra K. Gupta, Stephen Baker, Ian Goodfellow, Paul Lyons, Nicholas J. Matheson, Mark Toshner, Michael P. Weekes, Nick Brown, Surendra Palmar, Daniel Chapman, Ashley Shaw, Vivien Mendoza, Sherly Jose, Areti Bermperi, Julie Ann Zerrudo, Evgenia Kourampa, Caroline Saunders, Ranalie de Jesus, Jason Domingo, Ciro Pasquale, Bensi Vergese, Phoebe Vargas, Marivic Fabiculana, Marlyn Perales, Richard Skells, Lee Mynott, Elizabeth Blake, Amy Bates, Anne-laure Vallier, Alexandra Williams, David Phillips, Edmund Chiu, Alex Overhill, Nicola Ramenante, Jamal Sipple, Steven Frost, Helena Knock, Richard Hardy, Emily Foster, Fiona Davidson, Viona Rundell, Purity Bundi, Richmond Abeseabe, Sarah Clark, and Isabel Vicente

Subjects
COVID-19, SARS-CoV-2, POC, point of care, diagnostic test, nosocomial, Medicine (General), R5-920
Abstract

Summary: There is an urgent need for rapid SARS-CoV-2 testing in hospitals to limit nosocomial spread. We report an evaluation of point of care (POC) nucleic acid amplification testing (NAAT) in 149 participants with parallel combined nasal and throat swabbing for POC versus standard lab RT-PCR testing. Median time to result is 2.6 (IQR 2.3–4.8) versus 26.4 h (IQR 21.4–31.4, p < 0.001), with 32 (21.5%) positive and 117 (78.5%) negative. Cohen’s κ correlation between tests is 0.96 (95% CI 0.91–1.00). When comparing nearly 1,000 tests pre- and post-implementation, the median time to definitive bed placement from admission is 23.4 (8.6-41.9) versus 17.1 h (9.0–28.8), p = 0.02. Mean length of stay on COVID-19 “holding” wards is 58.5 versus 29.9 h (p < 0.001). POC testing increases isolation room availability, avoids bed closures, allows discharge to care homes, and expedites access to hospital procedures. POC testing could mitigate the impact of COVID-19 on hospital systems.

Published
2020
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3. Development and validation of a risk prediction model to diagnose Barrett's oesophagus (MARK-BE): a case-control machine learning approach

Author

Avi Rosenfeld, PhD, David G Graham, MBBS, Sarah Jevons, PhD, Jose Ariza, RGN, Daryl Hagan, MSc, Ash Wilson, BSc, Samuel J Lovat, Sarmed S Sami, MBBS, Omer F Ahmad, MBBS, Marco Novelli, ProfMBChB, Manuel Rodriguez Justo, MBBS, Alison Winstanley, MBBS, Eliyahu M Heifetz, PhD, Mordehy Ben-Zecharia, PhD, Uria Noiman, PhD, Rebecca C Fitzgerald, ProfMBChB, Peter Sasieni, ProfPhD, Laurence B Lovat, ProfMBBS, Karen Coker, Wanfeng Zhao, Kathryn Brown, Beverley Haynes, Tara Nuckcheddy Grant, Massimiliano di Pietro, Eleanor Dewhurst, Bincy Alias, Leanne Mills, Caroline Wilson, Elizabeth Bird-Lieberman, Jan Bornschein, Yean Lim, Kareem Shariff, Roberto Cayado Lopez, Myrna Udarbe, Claire Shaw, Glynis Rose, Ian Sargeant, M Al-Izzi, Roisin Schimmel, Elizabeth Green, Morgan Moorghen, Reshma Kanani, Mariann Baulf, Jayne Butcher, Adil Butt, Steve Bown, Gideon Lipman, Rami Sweis, Vinay Sehgal, Matthew Banks, Rehan Haidry, John Louis-Auguste, Darina Kohoutova, Sarah Kerr, Victor Eneh, Nigel Butter, Haroon Miah, Rommel Butawan, Grace Adesina, Sabrina Holohan, Joan Idris, Nick Hayes, Shajahan Wahed, Nelson Kath Houghton, Marc Hopton, Anne Eastick, Debasis Majumdar, Kassem Manuf, Lyndsey Fieldson, Helen Bailey, Jacobo Fernandez-Sordo Ortiz, Mina Patel, Suzanne Henry, Samantha Warburton, Jonathan White, Lisa Gadeke, Beverley Longhurst, Richmond Abeseabe, Peter Basford, Rupam Bhattacharyya, Scott Elliot, Roisin Bevan, Carly Brown, Philippa Laverick, Gayle Clifford, Anita Gibbons, Julie Ingmire, Abdullah Mawas, Jacquelyn Harvey, and Sharon Cave

Subjects
Computer applications to medicine. Medical informatics, R858-859.7
Abstract

Summary: Background: Screening for Barrett's oesophagus relies on endoscopy, which is invasive and few who undergo the procedure are found to have the condition. We aimed to use machine learning techniques to develop and externally validate a simple risk prediction panel to screen individuals for Barrett's oesophagus. Methods: In this prospective study, machine learning risk prediction in Barrett's oesophagus (MARK-BE), we used data from two case-control studies, BEST2 and BOOST, to compile training and validation datasets. From the BEST2 study, we analysed questionnaires from 1299 patients, of whom 880 (67·7%) had Barrett's oesophagus, including 40 with invasive oesophageal adenocarcinoma, and 419 (32·3%) were controls. We randomly split (6:4) the cohort using a computer algorithm into a training dataset of 776 patients and a testing dataset of 523 patients. We compiled an external validation cohort from the BOOST study, which included 398 patients, comprising 198 patients with Barrett's oesophagus (23 with oesophageal adenocarcinoma) and 200 controls. We identified independently important diagnostic features of Barrett's oesophagus using the machine learning techniques information gain and correlation-based feature selection. We assessed multiple classification tools to create a multivariable risk prediction model. Internal validation of the model using the BEST2 testing dataset was followed by external validation using the BOOST external validation dataset. From these data we created a prediction panel to identify at-risk individuals. Findings: The BEST2 study included 40 diagnostic features. Of these, 19 added information gain but after correlation-based feature selection only eight showed independent diagnostic value including age, sex, cigarette smoking, waist circumference, frequency of stomach pain, duration of heartburn and acidic taste, and taking antireflux medication, of which all were associated with increased risk of Barrett's oesophagus, except frequency of stomach pain, with was inversely associated in a case-control population. Logistic regression offered the highest prediction quality with an area under the receiver-operator curve (AUC) of 0·87 (95% CI 0·84–0·90; sensitivity set at 90%; specificity of 68%). In the testing dataset, AUC was 0·86 (0·83–0·89; sensitivity set at 90%; specificity of 65%). In the external validation dataset, the AUC was 0·81 (0·74–0·84; sensitivity set at 90%; specificity of 58%). Interpretation: Our diagnostic model offers valid predictions of diagnosis of Barrett's oesophagus in patients with symptomatic gastro-oesophageal reflux disease, assisting in identifying who should go forward to invasive confirmatory testing. Our predictive panel suggests that overweight men who have been taking antireflux medication for a long time might merit particular consideration for further testing. Our risk prediction panel is quick and simple to administer but will need further calibration and validation in a prospective study in primary care. Funding: Charles Wolfson Charitable Trust and Guts UK.

Published
2020
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4. Combined point-of-care nucleic acid and antibody testing for SARS-CoV-2 following emergence of D614G spike variant

Author

Petra Mlcochova, Dami Collier, Allyson Ritchie, Sonny M. Assennato, Myra Hosmillo, Neha Goel, Bo Meng, Krishna Chatterjee, Vivien Mendoza, Nigel Temperton, Leo Kiss, Leo C. James, Katarzyna A. Ciazynska, Xiaoli Xiong, John A.G. Briggs, James A. Nathan, Federica Mescia, Laura Bergamaschi, Hongyi Zhang, Petros Barmpounakis, Nikos Demeris, Richard Skells, Paul A. Lyons, John Bradley, Steven Baker, Jean Pierre Allain, Kenneth G.C. Smith, Rachel Bousfield, Michael Wilson, Dominic Sparkes, Glenn Amoroso, Effrosyni Gkrania-Klotsas, Susie Hardwick, Adrian Boyle, Ian Goodfellow, Ravindra K. Gupta, Stephen Baker, Gordon Dougan, Ravi Gupta, Paul J. Lehner, Paul Lyons, Nicholas J. Matheson, Mark Toshner, Michael P. Weekes, Nick Brown, Martin Curran, Surendra Palmar, David Enoch, Daniel Chapman, Ashley Shaw, Sherly Jose, Areti Bermperi, Julie Ann Zerrudo, Evgenia Kourampa, Laura Watson, Jieniean Worsley, Caroline Saunders, Ranalie de Jesus, Jason Domingo, Ciro Pasquale, Bensi Vergese, Phoebe Vargas, Marivic Fabiculana, Marlyn Perales, Lee Mynott, Elizabeth Blake, Amy Bates, Anne-Laure Vallier, Alexandra Williams, David Phillips, Edmund Chiu, Alex Overhill, Nicola Ramenatte, Jamal Sipple, Steven Frost, Helena Knock, Richard Hardy, Emily Foster, Fiona Davidson, Viona Rundell, Purity Bundi, Richmond Abeseabe, Sarah Clark, Isabel Vicente, Anne Elmer, Carla Ribeiro, Jenny Kourampa, Jane Kennet, Jane Rowlands, Anne Meadows, Criona O’Brien, Rebecca Rastall, Cherry Crucusio, Sarah Hewitt, Jane Price, Jo Calder, Laura Canna, Ashlea Bucke, Hugo Tordesillas, Julie Harris, Valentina Ruffolo, Barbara Graves, Helen Butcher, Daniela Caputo, Emma Le Gresley, Benjamin J. Dunmore, Jennifer Martin, Ekaterina Legchenko, Carmen Treacy, Christopher Huang, Jennifer Wood, Rachel Sutcliffe, Josh Hodgson, Joy Shih, Stefan Graf, Zhen Tong, Tobias Tilly, Ciara O’Donnell, Kelvin Hunter, Linda Pointon, Nicole Pond, Marta Wylot, Emma Jones, Stuart Fawke, Ben Bullman, Lori Turner, Isobel Jarvis, Ommar Omarjee, Aloka De Sa, Joe Marsden, Ariana Betancourt, Marianne Perera, Maddie Epping, Nathan Richoz, Georgie Bower, Rahul Sharma, Francesca Nice, Oisin Huhn, Natalia Savoinykh Yarkoni, Nika Romashova, Daniel Lewis, Andrew Hinch, Chiara Cossetti, Mateusz Strezlecki, Richard Grenfell, Hannah Stark, Neil Walker, Kathy Stirrups, Nigel Ovington, Eleanor Dewhust, Emily Li, Sofia Papadia, Nathalie Kingston, Andrew Lever, Estee Torok, William Hamilton, Grant Hall, Aminu Jahun, Yasmin Chaudhry, Malte Pinckert, Iliana Georgana, Anna Yakovleva, Laura Caller, Sarah Caddy, Theresa Feltwell, Fahad Khokhar, Luke Meredith, Charlotte Holdcroft, and Surendra Parmar

Subjects
Male, Antibodies, Viral, Serology, 0302 clinical medicine, COVID-19 Testing, 80 and over, Medicine, 030212 general & internal medicine, Viral, Aged, 80 and over, Immunoassay, 0303 health sciences, lcsh:R5-920, biology, Middle Aged, D614G, Spike Glycoprotein, 3. Good health, medicine.anatomical_structure, Point-of-Care Testing, Spike Glycoprotein, Coronavirus, Spike (software development), Female, Antibody, lcsh:Medicine (General), Nucleic Acid Amplification Techniques, Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), Point-of-care testing, Sensitivity and Specificity, General Biochemistry, Genetics and Molecular Biology, Antibodies, 03 medical and health sciences, rapid diagnoses, Neutralization Tests, Throat, Report, Humans, 030304 developmental biology, Point of care, Aged, QR355, COVID-19, point of care testing, SARS-CoV-2, business.industry, Virology, Coronavirus, biology.protein, Nucleic acid, business
Abstract

Rapid COVID-19 diagnosis in the hospital is essential, although this is complicated by 30%–50% of nose/throat swabs being negative by SARS-CoV-2 nucleic acid amplification testing (NAAT). Furthermore, the D614G spike mutant dominates the pandemic and it is unclear how serological tests designed to detect anti-spike antibodies perform against this variant. We assess the diagnostic accuracy of combined rapid antibody point of care (POC) and nucleic acid assays for suspected COVID-19 disease due to either wild-type or the D614G spike mutant SARS-CoV-2. The overall detection rate for COVID-19 is 79.2% (95% CI 57.8–92.9) by rapid NAAT alone. The combined point of care antibody test and rapid NAAT is not affected by D614G and results in very high sensitivity for COVID-19 diagnosis with very high specificity., Graphical Abstract, Highlights Combined rapid antibody + nucleic acid detection correctly diagnoses SARS-CoV-2 Rapid antibody tests detect immune responses against SARS-CoV-2 bearing D614G Rapid SARS-CoV-2 antibody tests do not cross-react with antibodies to seasonal CoV False positivity in SARS-CoV-2 finger prick blood antibody tests can be very low, Mlcochova et al. report that combined rapid nucleic acid amplification testing (NAAT) and finger prick blood antibody tests can substantially improve the diagnosis of COVID-19 as compared to NAAT alone and is able to detect the SARS-CoV-2 Spike D614G variant that dominates the pandemic.

Published
2021
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5. Development and validation of a risk prediction model to diagnose Barrett's oesophagus (MARK-BE): a case-control machine learning approach

Author

Sharon Cave, Vinay Sehgal, Samuel J Lovat, Lyndsey Fieldson, Eliyahu M Heifetz, Beverley Longhurst, Jan Bornschein, Debasis Majumdar, Matthew R. Banks, Kathryn Brown, Jayne Butcher, Morgan Moorghen, Omer F. Ahmad, Grace Adesina, Myrna Udarbe, Suzanne Henry, Sarah Jevons, M Al-Izzi, Joan Idris, Rommel Butawan, Elizabeth L. Bird-Lieberman, Julie Ingmire, Steve Bown, Beverley Haynes, Ash Wilson, Peter Sasieni, Nick Hayes, Gayle Clifford, Jacquelyn Harvey, Marco Novelli, Tara Nuckcheddy Grant, Marc Hopton, Kassem Manuf, Carly Brown, Sabrina Holohan, Samantha Warburton, Roisin Schimmel, Uria Noiman, John Louis-Auguste, Manuel Rodriguez–Justo, Mina Patel, Avi Rosenfeld, Roisin Bevan, Leanne Mills, Gideon Lipman, Shajahan Wahed, David Graham, Elizabeth Green, Yean Lim, Jonathan R. White, Mordehy Ben-Zecharia, Rami Sweis, Glynis Rose, Wanfeng Zhao, Sarmed S. Sami, Reshma Kanani, Laurence Lovat, Claire Shaw, Sarah Kerr, Nigel Butter, Karen Coker, Alison Winstanley, Haroon Miah, Jacobo Fernandez-Sordo Ortiz, Caroline Wilson, Roberto Cayado Lopez, Rebecca C. Fitzgerald, Bincy Alias, Massimiliano di Pietro, Anne Eastick, Darina Kohoutova, Ian Sargeant, Adil Butt, Rupam Bhattacharyya, Abdullah Mawas, Lisa Gadeke, Nelson Kath Houghton, Kareem M. Shariff, Mariann Baulf, Richmond Abeseabe, Peter Basford, Helen Bailey, Scott Elliot, Philippa Laverick, Eleanor Dewhurst, Victor Eneh, Anita Gibbons, Rehan Haidry, Daryl Hagan, and Jose Ariza

Subjects
Male, Population, MEDLINE, Medicine (miscellaneous), Health Informatics, Feature selection, lcsh:Computer applications to medicine. Medical informatics, Machine learning, computer.software_genre, Logistic regression, Risk Assessment, Machine Learning, Barrett Esophagus, Health Information Management, Humans, Medicine, Decision Sciences (miscellaneous), Prospective Studies, Prospective cohort study, education, Aged, education.field_of_study, medicine.diagnostic_test, business.industry, Heartburn, Middle Aged, United Kingdom, digestive system diseases, Endoscopy, Case-Control Studies, Cohort, lcsh:R858-859.7, Female, Artificial intelligence, medicine.symptom, business, computer, Forecasting
Abstract

Summary Background Screening for Barrett's oesophagus relies on endoscopy, which is invasive and few who undergo the procedure are found to have the condition. We aimed to use machine learning techniques to develop and externally validate a simple risk prediction panel to screen individuals for Barrett's oesophagus. Methods In this prospective study, machine learning risk prediction in Barrett's oesophagus (MARK-BE), we used data from two case-control studies, BEST2 and BOOST, to compile training and validation datasets. From the BEST2 study, we analysed questionnaires from 1299 patients, of whom 880 (67·7%) had Barrett's oesophagus, including 40 with invasive oesophageal adenocarcinoma, and 419 (32·3%) were controls. We randomly split (6:4) the cohort using a computer algorithm into a training dataset of 776 patients and a testing dataset of 523 patients. We compiled an external validation cohort from the BOOST study, which included 398 patients, comprising 198 patients with Barrett's oesophagus (23 with oesophageal adenocarcinoma) and 200 controls. We identified independently important diagnostic features of Barrett's oesophagus using the machine learning techniques information gain and correlation-based feature selection. We assessed multiple classification tools to create a multivariable risk prediction model. Internal validation of the model using the BEST2 testing dataset was followed by external validation using the BOOST external validation dataset. From these data we created a prediction panel to identify at-risk individuals. Findings The BEST2 study included 40 diagnostic features. Of these, 19 added information gain but after correlation-based feature selection only eight showed independent diagnostic value including age, sex, cigarette smoking, waist circumference, frequency of stomach pain, duration of heartburn and acidic taste, and taking antireflux medication, of which all were associated with increased risk of Barrett's oesophagus, except frequency of stomach pain, with was inversely associated in a case-control population. Logistic regression offered the highest prediction quality with an area under the receiver-operator curve (AUC) of 0·87 (95% CI 0·84–0·90; sensitivity set at 90%; specificity of 68%). In the testing dataset, AUC was 0·86 (0·83–0·89; sensitivity set at 90%; specificity of 65%). In the external validation dataset, the AUC was 0·81 (0·74–0·84; sensitivity set at 90%; specificity of 58%). Interpretation Our diagnostic model offers valid predictions of diagnosis of Barrett's oesophagus in patients with symptomatic gastro-oesophageal reflux disease, assisting in identifying who should go forward to invasive confirmatory testing. Our predictive panel suggests that overweight men who have been taking antireflux medication for a long time might merit particular consideration for further testing. Our risk prediction panel is quick and simple to administer but will need further calibration and validation in a prospective study in primary care. Funding Charles Wolfson Charitable Trust and Guts UK.

Published
2020
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