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Broad external validation of a multivariable risk prediction model for gastrointestinal malignancy in iron deficiency anaemia

BACKGROUND: Using two large datasets from Dorset, we previously reported an internally validated multivariable risk model for predicting the risk of GI malignancy in IDA—the IDIOM score. The aim of this retrospective observational study was to validate the IDIOM model using two independent external...

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Autores principales: Almilaji, Orouba, Webb, Gwilym, Maynard, Alec, Chapman, Thomas P., Shine, Brian S. F., Ellis, Antony J., Hebden, John, Docherty, Sharon, Williams, Elizabeth J., Snook, Jonathon
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8672477/
https://www.ncbi.nlm.nih.gov/pubmed/34906262
http://dx.doi.org/10.1186/s41512-021-00112-8
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author Almilaji, Orouba
Webb, Gwilym
Maynard, Alec
Chapman, Thomas P.
Shine, Brian S. F.
Ellis, Antony J.
Hebden, John
Docherty, Sharon
Williams, Elizabeth J.
Snook, Jonathon
author_facet Almilaji, Orouba
Webb, Gwilym
Maynard, Alec
Chapman, Thomas P.
Shine, Brian S. F.
Ellis, Antony J.
Hebden, John
Docherty, Sharon
Williams, Elizabeth J.
Snook, Jonathon
author_sort Almilaji, Orouba
collection PubMed
description BACKGROUND: Using two large datasets from Dorset, we previously reported an internally validated multivariable risk model for predicting the risk of GI malignancy in IDA—the IDIOM score. The aim of this retrospective observational study was to validate the IDIOM model using two independent external datasets. METHODS: The external validation datasets were collected, in a secondary care setting, by different investigators from cohorts in Oxford and Sheffield derived under different circumstances, comprising 1117 and 474 patients with confirmed IDA respectively. The data were anonymised prior to analysis. The predictive performance of the original model was evaluated by estimating measures of calibration, discrimination and clinical utility using the validation datasets. RESULTS: The discrimination of the original model using the external validation data was 70% (95% CI 65, 75) for the Oxford dataset and 70% (95% CI 61, 79) for the Sheffield dataset. The analysis of mean, weak, flexible and across the risk groups’ calibration showed no tendency for under or over-estimated risks in the combined validation data. Decision curve analysis demonstrated the clinical value of the IDIOM model with a net benefit that is higher than ‘investigate all’ and ‘investigate no-one’ strategies up to a threshold of 18% in the combined validation data, using a risk cut-off of around 1.2% to categorise patients into the very low risk group showed that none of the patients stratified in this risk group proved to have GI cancer on investigation in the validation datasets. CONCLUSION: This external validation exercise has shown promising results for the IDIOM model in predicting the risk of underlying GI malignancy in independent IDA datasets collected in different clinical settings. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s41512-021-00112-8.
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spelling pubmed-86724772021-12-15 Broad external validation of a multivariable risk prediction model for gastrointestinal malignancy in iron deficiency anaemia Almilaji, Orouba Webb, Gwilym Maynard, Alec Chapman, Thomas P. Shine, Brian S. F. Ellis, Antony J. Hebden, John Docherty, Sharon Williams, Elizabeth J. Snook, Jonathon Diagn Progn Res Research BACKGROUND: Using two large datasets from Dorset, we previously reported an internally validated multivariable risk model for predicting the risk of GI malignancy in IDA—the IDIOM score. The aim of this retrospective observational study was to validate the IDIOM model using two independent external datasets. METHODS: The external validation datasets were collected, in a secondary care setting, by different investigators from cohorts in Oxford and Sheffield derived under different circumstances, comprising 1117 and 474 patients with confirmed IDA respectively. The data were anonymised prior to analysis. The predictive performance of the original model was evaluated by estimating measures of calibration, discrimination and clinical utility using the validation datasets. RESULTS: The discrimination of the original model using the external validation data was 70% (95% CI 65, 75) for the Oxford dataset and 70% (95% CI 61, 79) for the Sheffield dataset. The analysis of mean, weak, flexible and across the risk groups’ calibration showed no tendency for under or over-estimated risks in the combined validation data. Decision curve analysis demonstrated the clinical value of the IDIOM model with a net benefit that is higher than ‘investigate all’ and ‘investigate no-one’ strategies up to a threshold of 18% in the combined validation data, using a risk cut-off of around 1.2% to categorise patients into the very low risk group showed that none of the patients stratified in this risk group proved to have GI cancer on investigation in the validation datasets. CONCLUSION: This external validation exercise has shown promising results for the IDIOM model in predicting the risk of underlying GI malignancy in independent IDA datasets collected in different clinical settings. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s41512-021-00112-8. BioMed Central 2021-12-15 /pmc/articles/PMC8672477/ /pubmed/34906262 http://dx.doi.org/10.1186/s41512-021-00112-8 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Research
Almilaji, Orouba
Webb, Gwilym
Maynard, Alec
Chapman, Thomas P.
Shine, Brian S. F.
Ellis, Antony J.
Hebden, John
Docherty, Sharon
Williams, Elizabeth J.
Snook, Jonathon
Broad external validation of a multivariable risk prediction model for gastrointestinal malignancy in iron deficiency anaemia
title Broad external validation of a multivariable risk prediction model for gastrointestinal malignancy in iron deficiency anaemia
title_full Broad external validation of a multivariable risk prediction model for gastrointestinal malignancy in iron deficiency anaemia
title_fullStr Broad external validation of a multivariable risk prediction model for gastrointestinal malignancy in iron deficiency anaemia
title_full_unstemmed Broad external validation of a multivariable risk prediction model for gastrointestinal malignancy in iron deficiency anaemia
title_short Broad external validation of a multivariable risk prediction model for gastrointestinal malignancy in iron deficiency anaemia
title_sort broad external validation of a multivariable risk prediction model for gastrointestinal malignancy in iron deficiency anaemia
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8672477/
https://www.ncbi.nlm.nih.gov/pubmed/34906262
http://dx.doi.org/10.1186/s41512-021-00112-8
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