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Development of a Clinical and Genetic Prediction Model for Early Intestinal Resection in Patients with Crohn’s Disease: Results from the IMPACT Study
Early intestinal resection in patients with Crohn’s disease (CD) is necessary due to a severe and complicating disease course. Herein, we aim to predict which patients with CD need early intestinal resection within 3 years of diagnosis, according to a tree-based machine learning technique. The singl...
Autores principales: | , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7915022/ https://www.ncbi.nlm.nih.gov/pubmed/33562363 http://dx.doi.org/10.3390/jcm10040633 |
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author | Kang, Eun Ae Jang, Jongha Choi, Chang Hwan Kang, Sang Bum Bang, Ki Bae Kim, Tae Oh Seo, Geom Seog Cha, Jae Myung Chun, Jaeyoung Jung, Yunho Kim, Hyun Gun Im, Jong Pil Kim, Sangsoo Ahn, Kwang Sung Lee, Chang Kyun Kim, Hyo Jong Kim, Min Suk Park, Dong Il |
author_facet | Kang, Eun Ae Jang, Jongha Choi, Chang Hwan Kang, Sang Bum Bang, Ki Bae Kim, Tae Oh Seo, Geom Seog Cha, Jae Myung Chun, Jaeyoung Jung, Yunho Kim, Hyun Gun Im, Jong Pil Kim, Sangsoo Ahn, Kwang Sung Lee, Chang Kyun Kim, Hyo Jong Kim, Min Suk Park, Dong Il |
author_sort | Kang, Eun Ae |
collection | PubMed |
description | Early intestinal resection in patients with Crohn’s disease (CD) is necessary due to a severe and complicating disease course. Herein, we aim to predict which patients with CD need early intestinal resection within 3 years of diagnosis, according to a tree-based machine learning technique. The single-nucleotide polymorphism (SNP) genotype data for 337 CD patients recruited from 15 hospitals were typed using the Korea Biobank Array. For external validation, an additional 126 CD patients were genotyped. The predictive model was trained using the 102 candidate SNPs and seven sets of clinical information (age, sex, cigarette smoking, disease location, disease behavior, upper gastrointestinal involvement, and perianal disease) by employing a tree-based machine learning method (CatBoost). The importance of each feature was measured using the Shapley Additive Explanations (SHAP) model. The final model comprised two clinical parameters (age and disease behavior) and four SNPs (rs28785174, rs60532570, rs13056955, and rs7660164). The combined clinical–genetic model predicted early surgery more accurately than a clinical-only model in both internal (area under the receiver operating characteristic (AUROC), 0.878 vs. 0.782; n = 51; p < 0.001) and external validation (AUROC, 0.836 vs. 0.805; n = 126; p < 0.001). Identification of genetic polymorphisms and clinical features enhanced the prediction of early intestinal resection in patients with CD. |
format | Online Article Text |
id | pubmed-7915022 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-79150222021-03-01 Development of a Clinical and Genetic Prediction Model for Early Intestinal Resection in Patients with Crohn’s Disease: Results from the IMPACT Study Kang, Eun Ae Jang, Jongha Choi, Chang Hwan Kang, Sang Bum Bang, Ki Bae Kim, Tae Oh Seo, Geom Seog Cha, Jae Myung Chun, Jaeyoung Jung, Yunho Kim, Hyun Gun Im, Jong Pil Kim, Sangsoo Ahn, Kwang Sung Lee, Chang Kyun Kim, Hyo Jong Kim, Min Suk Park, Dong Il J Clin Med Article Early intestinal resection in patients with Crohn’s disease (CD) is necessary due to a severe and complicating disease course. Herein, we aim to predict which patients with CD need early intestinal resection within 3 years of diagnosis, according to a tree-based machine learning technique. The single-nucleotide polymorphism (SNP) genotype data for 337 CD patients recruited from 15 hospitals were typed using the Korea Biobank Array. For external validation, an additional 126 CD patients were genotyped. The predictive model was trained using the 102 candidate SNPs and seven sets of clinical information (age, sex, cigarette smoking, disease location, disease behavior, upper gastrointestinal involvement, and perianal disease) by employing a tree-based machine learning method (CatBoost). The importance of each feature was measured using the Shapley Additive Explanations (SHAP) model. The final model comprised two clinical parameters (age and disease behavior) and four SNPs (rs28785174, rs60532570, rs13056955, and rs7660164). The combined clinical–genetic model predicted early surgery more accurately than a clinical-only model in both internal (area under the receiver operating characteristic (AUROC), 0.878 vs. 0.782; n = 51; p < 0.001) and external validation (AUROC, 0.836 vs. 0.805; n = 126; p < 0.001). Identification of genetic polymorphisms and clinical features enhanced the prediction of early intestinal resection in patients with CD. MDPI 2021-02-07 /pmc/articles/PMC7915022/ /pubmed/33562363 http://dx.doi.org/10.3390/jcm10040633 Text en © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Kang, Eun Ae Jang, Jongha Choi, Chang Hwan Kang, Sang Bum Bang, Ki Bae Kim, Tae Oh Seo, Geom Seog Cha, Jae Myung Chun, Jaeyoung Jung, Yunho Kim, Hyun Gun Im, Jong Pil Kim, Sangsoo Ahn, Kwang Sung Lee, Chang Kyun Kim, Hyo Jong Kim, Min Suk Park, Dong Il Development of a Clinical and Genetic Prediction Model for Early Intestinal Resection in Patients with Crohn’s Disease: Results from the IMPACT Study |
title | Development of a Clinical and Genetic Prediction Model for Early Intestinal Resection in Patients with Crohn’s Disease: Results from the IMPACT Study |
title_full | Development of a Clinical and Genetic Prediction Model for Early Intestinal Resection in Patients with Crohn’s Disease: Results from the IMPACT Study |
title_fullStr | Development of a Clinical and Genetic Prediction Model for Early Intestinal Resection in Patients with Crohn’s Disease: Results from the IMPACT Study |
title_full_unstemmed | Development of a Clinical and Genetic Prediction Model for Early Intestinal Resection in Patients with Crohn’s Disease: Results from the IMPACT Study |
title_short | Development of a Clinical and Genetic Prediction Model for Early Intestinal Resection in Patients with Crohn’s Disease: Results from the IMPACT Study |
title_sort | development of a clinical and genetic prediction model for early intestinal resection in patients with crohn’s disease: results from the impact study |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7915022/ https://www.ncbi.nlm.nih.gov/pubmed/33562363 http://dx.doi.org/10.3390/jcm10040633 |
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