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Refinement and validation of the IDIOM score for predicting the risk of gastrointestinal cancer in iron deficiency anaemia

OBJECTIVE: To refine and validate a model for predicting the risk of gastrointestinal (GI) cancer in iron deficiency anaemia (IDA) and to develop an app to facilitate use in clinical practice. DESIGN: Three elements: (1) analysis of a dataset of 2390 cases of IDA to validate the predictive value of...

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Detalles Bibliográficos
Autores principales: Almilaji, Orouba, Smith, Carla, Surgenor, Sue, Clegg, Andrew, Williams, Elizabeth, Thomas, Peter, Snook, Jonathon
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BMJ Publishing Group 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7247388/
https://www.ncbi.nlm.nih.gov/pubmed/32444424
http://dx.doi.org/10.1136/bmjgast-2020-000403
Descripción
Sumario:OBJECTIVE: To refine and validate a model for predicting the risk of gastrointestinal (GI) cancer in iron deficiency anaemia (IDA) and to develop an app to facilitate use in clinical practice. DESIGN: Three elements: (1) analysis of a dataset of 2390 cases of IDA to validate the predictive value of age, sex, blood haemoglobin concentration (Hb), mean cell volume (MCV) and iron studies on the probability of underlying GI cancer; (2) a pilot study of the benefit of adding faecal immunochemical testing (FIT) into the model; and (3) development of an app based on the model. RESULTS: Age, sex and Hb were all strong, independent predictors of the risk of GI cancer, with ORs (95% CI) of 1.05 per year (1.03 to 1.07, p<0.00001), 2.86 for men (2.03 to 4.06, p<0.00001) and 1.03 for each g/L reduction in Hb (1.01 to 1.04, p<0.0001) respectively. An association with MCV was also revealed, with an OR of 1.03 for each fl reduction (1.01 to 1.05, p<0.02). The model was confirmed to be robust by an internal validation exercise. In the pilot study of high-risk cases, FIT was also predictive of GI cancer (OR 6.6, 95% CI 1.6 to 51.8), but the sensitivity was low at 23.5% (95% CI 6.8% to 49.9%). An app based on the model was developed. CONCLUSION: This predictive model may help rationalise the use of investigational resources in IDA, by fast-tracking high-risk cases and, with appropriate safeguards, avoiding invasive investigation altogether in those at ultra-low predicted risk.