Development and validation of a risk prediction model to diagnose Barrett's oesophagus (MARK-BE): a case-control machine learning approach
| Autor: | 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, Jose Ariza |
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| Rok vydání: | 2020 |
| Předmět: |
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 |
| Zdroj: | The Lancet: Digital Health, Vol 2, Iss 1, Pp e37-e48 (2020) |
| ISSN: | 2589-7500 |
| DOI: | 10.1016/s2589-7500(19)30216-x |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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