The utility of a symptom model to predict the risk of oesophageal cancer.

Surgeon

Faculty of Medical Sciences, Newcastle University, Framlington Place, Newcastle Upon Tyne, NE2 4HH, United Kingdom; Department of Medical Physics, Freeman Hospital, Freeman Road, High Heaton, Newcastle Upon Tyne, NE7 7DN, United Kingdom.

Published: April 2023

Objective: To assess whether extra-oesophageal symptoms are predictive of oesophageal malignancy.

Methods: A prospective, single-centre cross-sectional questionnaire study at a tertiary referral unit for oesophageal cancer using the Comprehensive Reflux Symptoms Scale (CReSS) questionnaire tool. Respondents with oesophageal malignancy were compared with historical cohorts undergoing airway examination or upper gastrointestinal endoscopy and found to have benign diagnoses. We developed a model for predicting oesophageal cancer using linear discriminant analysis and logistic regression, assessed by Monte Carlo cross validation.

Results: Respondents with oesophageal malignancy (n = 146; mean age 70.5; male: female, 71:29) were compared with those undergoing airway examination (n = 177) and upper gastrointestinal endoscopy (n = 351), found to have benign diagnoses. No single questionnaire item, or group of co-varying items (factors), reliably discriminated oesophageal cancer from other diagnoses. Individual items which suggested higher risk of oesophageal malignancy included dysphagia (area under the curve (AUC) 0.68), low appetite (AUC 0.66), and early satiety (AUC 0.58). Conversely, throat pain (AUC 0.38), bloating (AUC 0.38) and heartburn (AUC 0.37) were inversely related to cancer risk. A forward stepwise regression analysis including a subset of 12 CReSS questionnaire items together with age and sex derived a model predictive of oesophageal malignancy in this cohort (AUC 0.89).

Conclusion: We demonstrate a model comprised of 12 questionnaire items and 2 demographic parameters as a potential predictive tool for oesophageal malignancy diagnosis in this study population. Translating this model for predicting oesophageal malignancy in the general population is a valuable topic for future research.

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http://dx.doi.org/10.1016/j.surge.2022.03.006DOI Listing

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