ArticleJGH open : an open access journal of gastroenterology and hepatology2025
Clinical Characteristics of Gastrointestinal Malignancy and Validation of the IDIOM Model Among Patients With Iron Deficiency Anemia in Thailand.
Article in JGH open : an open access journal of gastroenterology and hepatology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- An Updated Review on Iron Deficiency Anemia: From Epidemiology to Diagnosis, Etiology, Prevention and Treatment.Journal of blood medicine · 2026Review
- Clinical Characteristics of Gastrointestinal Malignancy and Validation of the IDIOM Model Among Patients With Iron Deficiency Anemia in Thailand.JGH open : an open access journal of gastroenterology and hepatology · 2025Article
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10 authors.
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Abstract
Background: Gastrointestinal malignancy is a concerning etiology of iron deficiency anemia (IDA). The IDIOM model predicts gastrointestinal malignancy risk in IDA patients using age, sex, hemoglobin, and mean corpuscular volume (MCV) as parameters. This study aimed to evaluate the clinical characteristics of gastrointestinal malignancy and externally validate the IDIOM model in Thai IDA patients. Methods: A retrospective cross-sectional study was conducted on adult IDA patients who underwent gastrointestinal endoscopy at Chiang Mai University Hospital between 2019 and 2023. Clinical characteristics were compared between patients with and without gastrointestinal malignancy. The IDIOM model's performance was assessed with discrimination, calibration, and clinical usefulness. To improve its performance, the model was updated using recalibration and refitting methods. Results: Among 474 IDA patients, 101 (21.3%) had gastrointestinal malignancy, predominantly colorectal cancer ( Conclusion: Distinct clinical features should alert physicians to the possibility of underlying gastrointestinal malignancy in IDA patients. Given the limited performance of the IDIOM model in the Thai population, a region-specific prediction model tailored to local clinical characteristics is needed.
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