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United European Gastroenterology Journal ; 9(SUPPL 8):302, 2021.
Artigo em Inglês | EMBASE | ID: covidwho-1490962


Introduction: Waiting times for endoscopy are rising rapidly following the COVID-19 pandemic, leading to significant backlogs.1 Modelling has demonstrated that delays in presentation to health services and delays in completing diagnostic procedures will lead to excess mortality.2 In addition, many cancers are likely to be missed as patients are placed on routine waiting lists but are not regularly monitored. Some hospitals use the Edinburgh Dysphagia Score to risk assess and prioritise patients for investigation.3 This offers a sensitivity of 98.4% and specificity of 9.3% to detect malignancy in patients presenting with dysphagia.4 However, it is primarily not designed for detecting gastric cancer. We aimed to create a more accurate screening questionnaire to risk assess patients and prioritise those who need early endoscopy. Aims & Methods: Patients were recruited as part of the Saliva to Predict rIsk of disease using Transcriptomics and epigenetics (SPIT) study. Ethical approval was gained from the Coventry and Warwickshire Regional Ethics Committee (17/WM/0079). Patients were recruited from 2 week-wait pathway referrals at 20 hospitals in the United Kingdom, which is used by physicians to refer patients who have may suspected cancer for further investigation The cohort was further enriched with patients found to have oesophageal adenocarcinoma on emergency hospital admission. They completed over 200 questions about a wide variety of symptoms and risk factors. After data cleaning, 800 patients were available for evaluation. Of these, 80 had upper GI cancer. A machine learning model was developed to identify those at highest risk of having upper GI cancer using a 'cost-based' approach which maximises the chance of detecting cancer. Information gain was followed by correlated feature selection and a multivariable logistic regression curve was created with scores from 0 (cancer very unlikely) to 100 (cancer very likely). The training dataset used 80% of the data and the model was tested with the other 20%. Results: 20 features were found to be important and reproducible. They included age, sex, dysphagia, odynophagia, early satiety, weight loss, duration of chest pain and regurgitation, frequency of acid taste in the mouth, a previous history of smoking, cancer or psychological disorders, current anxiety level and frequency of vegetable intake. The area under the receiver operator curve to detect cancer was 0.83. 50% of cancers scored greater than 85 whereas 50% of normals scored less than 25. At a cut-off score of 10, sensitivity was 98.7% with specificity 26.8% to detect cancer. Conclusion: We have created a simple, reproducible risk score to identify patients at high and low risk of upper GI cancer. It performs better than previous scores but now needs testing in the real world. It might be usable to both upgrade routine patients to urgent endoscopy and remove patients at very low risk from waiting lists, thereby helping to prioritise patients with a greater clinical need and reducing the endoscopic backlog.