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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...

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Autores principales: 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
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
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.
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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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