A Novel 8-Predictors Signature to Predict Complicated Disease Course in Pediatric-onset Crohn's Disease : A Population-based Study
© The Author(s) 2023. Published by Oxford University Press on behalf of Crohn’s & Colitis Foundation. All rights reserved. For permissions, please e-mail: journals.permissionsoup.com..
BACKGROUND: The identification of patients at high risk of a disabling disease course would be invaluable in guiding initial therapy in Crohn's disease (CD). Our objective was to evaluate a combination of clinical, serological, and genetic factors to predict complicated disease course in pediatric-onset CD.
METHODS: Data for pediatric-onset CD patients, diagnosed before 17 years of age between 1988 and 2004 and followed more than 5 years, were extracted from the population-based EPIMAD registry. The main outcome was defined by the occurrence of complicated behavior (stricturing or penetrating) and/or intestinal resection within the 5 years following diagnosis. Lasso logistic regression models were used to build a predictive model based on clinical data at diagnosis, serological data (ASCA, pANCA, anti-OmpC, anti-Cbir1, anti-Fla2, anti-Flax), and 369 candidate single nucleotide polymorphisms.
RESULTS: In total, 156 children with an inflammatory (B1) disease at diagnosis were included. Among them, 35% (n = 54) progressed to a complicated behavior or an intestinal resection within the 5 years following diagnosis. The best predictive model (PREDICT-EPIMAD) included the location at diagnosis, pANCA, and 6 single nucleotide polymorphisms. This model showed good discrimination and good calibration, with an area under the curve of 0.80 after correction for optimism bias (sensitivity, 79%, specificity, 74%, positive predictive value, 61%, negative predictive value, 87%). Decision curve analysis confirmed the clinical utility of the model.
CONCLUSIONS: A combination of clinical, serotypic, and genotypic variables can predict disease progression in this population-based pediatric-onset CD cohort. Independent validation is needed before it can be used in clinical practice.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:29 |
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| Enthalten in: |
Inflammatory bowel diseases - 29(2023), 11 vom: 02. Nov., Seite 1793-1804 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Sarter, Hélène [VerfasserIn] |
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| Links: |
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| Themen: |
Biomarkers |
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| Anmerkungen: |
Date Completed 08.11.2023 Date Revised 08.11.2023 published: Print Citation Status MEDLINE |
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| doi: |
10.1093/ibd/izad090 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| 245 | 1 | 2 | |a A Novel 8-Predictors Signature to Predict Complicated Disease Course in Pediatric-onset Crohn's Disease |b A Population-based Study |
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| 500 | |a Citation Status MEDLINE | ||
| 520 | |a © The Author(s) 2023. Published by Oxford University Press on behalf of Crohn’s & Colitis Foundation. All rights reserved. For permissions, please e-mail: journals.permissionsoup.com. | ||
| 520 | |a BACKGROUND: The identification of patients at high risk of a disabling disease course would be invaluable in guiding initial therapy in Crohn's disease (CD). Our objective was to evaluate a combination of clinical, serological, and genetic factors to predict complicated disease course in pediatric-onset CD | ||
| 520 | |a METHODS: Data for pediatric-onset CD patients, diagnosed before 17 years of age between 1988 and 2004 and followed more than 5 years, were extracted from the population-based EPIMAD registry. The main outcome was defined by the occurrence of complicated behavior (stricturing or penetrating) and/or intestinal resection within the 5 years following diagnosis. Lasso logistic regression models were used to build a predictive model based on clinical data at diagnosis, serological data (ASCA, pANCA, anti-OmpC, anti-Cbir1, anti-Fla2, anti-Flax), and 369 candidate single nucleotide polymorphisms | ||
| 520 | |a RESULTS: In total, 156 children with an inflammatory (B1) disease at diagnosis were included. Among them, 35% (n = 54) progressed to a complicated behavior or an intestinal resection within the 5 years following diagnosis. The best predictive model (PREDICT-EPIMAD) included the location at diagnosis, pANCA, and 6 single nucleotide polymorphisms. This model showed good discrimination and good calibration, with an area under the curve of 0.80 after correction for optimism bias (sensitivity, 79%, specificity, 74%, positive predictive value, 61%, negative predictive value, 87%). Decision curve analysis confirmed the clinical utility of the model | ||
| 520 | |a CONCLUSIONS: A combination of clinical, serotypic, and genotypic variables can predict disease progression in this population-based pediatric-onset CD cohort. Independent validation is needed before it can be used in clinical practice | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Research Support, Non-U.S. Gov't | |
| 650 | 4 | |a Crohn’s disease | |
| 650 | 4 | |a complication | |
| 650 | 4 | |a genetics | |
| 650 | 4 | |a inflammatory bowel disease | |
| 650 | 4 | |a prediction | |
| 650 | 4 | |a prognosis | |
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| 700 | 1 | |a Agoute, E |e investigator |4 oth | |
| 700 | 1 | |a Andre, J M |e investigator |4 oth | |
| 700 | 1 | |a Antonietti, M |e investigator |4 oth | |
| 700 | 1 | |a Aouakli, A |e investigator |4 oth | |
| 700 | 1 | |a Armand, A |e investigator |4 oth | |
| 700 | 1 | |a Armengol-Debeir, L |e investigator |4 oth | |
| 700 | 1 | |a Aroichane, I |e investigator |4 oth | |
| 700 | 1 | |a Assi, F |e investigator |4 oth | |
| 700 | 1 | |a Aubet, J P |e investigator |4 oth | |
| 700 | 1 | |a Auxenfants, E |e investigator |4 oth | |
| 700 | 1 | |a Avram, A |e investigator |4 oth | |
| 700 | 1 | |a Ayafi-Ramelot, F |e investigator |4 oth | |
| 700 | 1 | |a Azzouzi, K |e investigator |4 oth | |
| 700 | 1 | |a Bankovski, D |e investigator |4 oth | |
| 700 | 1 | |a Barbry, B |e investigator |4 oth | |
| 700 | 1 | |a Bardoux, N |e investigator |4 oth | |
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| 700 | 1 | |a Becqwort, J P |e investigator |4 oth | |
| 700 | 1 | |a Bellati, S |e investigator |4 oth | |
| 700 | 1 | |a Benet, V |e investigator |4 oth | |
| 700 | 1 | |a Benali, H |e investigator |4 oth | |
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| 700 | 1 | |a Billoud, E |e investigator |4 oth | |
| 700 | 1 | |a Biron, N |e investigator |4 oth | |
| 700 | 1 | |a Bismuth, B |e investigator |4 oth | |
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| 700 | 1 | |a Bonkovski, D |e investigator |4 oth | |
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| 700 | 1 | |a Boudaillez, C |e investigator |4 oth | |
| 700 | 1 | |a Bourgeaux, C |e investigator |4 oth | |
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| 700 | 1 | |a Canva, J Y |e investigator |4 oth | |
| 700 | 1 | |a Canva-Delcambre, V |e investigator |4 oth | |
| 700 | 1 | |a Capron, J P |e investigator |4 oth | |
| 700 | 1 | |a Cardot, F |e investigator |4 oth | |
| 700 | 1 | |a Carette, S |e investigator |4 oth | |
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| 700 | 1 | |a Choteau, A |e investigator |4 oth | |
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