Evidence map›Paper›PMID 39945210›Full record

ArticleJournal of Crohn's & colitis2025

Prevalence, incidence, and mortality of inflammatory bowel disease in the Netherlands: development and external validation of machine learning models.

Reinier C A van Linschoten, Nikki van Leeuwen, David van Klaveren, Marieke J Pierik, Rob Creemers, Evelien M B Hendrix, Jan A Hazelzet, C Janneke van der Woude, Rachel L West, Desirée van Noord

Abstract readValidation Study
In one paragraph

Article in Journal of Crohn's & colitis, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Review
  5. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Reinier C A van LinschotenDepartment of Gastroenterology & Hepatology, Franciscus Gasthuis & Vlietland, P.O. Box 10900, 3004 BA, Rotterdam, The Netherlands.ORCID 0000-0003-3052-596X
Nikki van LeeuwenDepartment of Public Health, Erasmus MC, P.O. Box 2040, 3000 CA, Rotterdam, The Netherlands.
David van KlaverenDepartment of Public Health, Erasmus MC, P.O. Box 2040, 3000 CA, Rotterdam, The Netherlands.
Marieke J PierikDepartment of Gastroenterology and Hepatology, MUMC+, P.O. Box 5800, 6202 AZ, Maastricht, The Netherlands.ORCID 0000-0001-6981-6516
Rob CreemersNUTRIM School of Nutrition and Translational Research in Metabolism, Faculty of Health, Medicine and Life Sciences, Maastricht University, P.O. Box 616, 6200 MD, Maastricht, The Netherlands.ORCID 0000-0002-5046-2030
Evelien M B HendrixDepartment of Gastroenterology and Hepatology, MUMC+, P.O. Box 5800, 6202 AZ, Maastricht, The Netherlands.ORCID 0000-0003-0482-4640
Jan A HazelzetDepartment of Public Health, Erasmus MC, P.O. Box 2040, 3000 CA, Rotterdam, The Netherlands.ORCID 0000-0002-9414-5539
C Janneke van der WoudeDepartment of Gastroenterology & Hepatology, Erasmus MC, P.O. Box 2040, 3000 CA, Rotterdam, The Netherlands.
Rachel L WestDepartment of Gastroenterology & Hepatology, Franciscus Gasthuis & Vlietland, P.O. Box 10900, 3004 BA, Rotterdam, The Netherlands.
Desirée van NoordDepartment of Gastroenterology & Hepatology, Franciscus Gasthuis & Vlietland, P.O. Box 10900, 3004 BA, Rotterdam, The Netherlands.ORCID 0000-0003-1578-9123

Funding

Franciscus Gasthuis & VlietlandFranciscus Research FoundationResearch and Development Foundation
6 · The paper itself

Abstract

BACKGROUND AND

aimsLarge registries are promising tools to study the epidemiology of inflammatory bowel disease (IBD). We aimed to develop and validate machine learning models to identify IBD cases in administrative data, aiming to determine the prevalence, incidence, and mortality of IBD in the Netherlands.

methodsWe developed machine learning models for administrative data to identify IBD cases and classify them on subtype and incidence year. Models were developed in a population-based cohort and externally validated in a hospital cohort. Models were evaluated on Brier score, area under the receiver operating characteristic curve (AUC), calibration, and accuracy. The best models were used to determine the epidemiology of IBD in the Netherlands between 2013 and 2020.

resultsFor identifying IBD cases the random forest model was best (AUC: 0.97, 95% CI [0.96; 0.97]). The gradient-boosted trees model for subtype was best (accuracy: 0.95, 95% CI [0.94; 0.95]) as was the random forest model for incidence year (0.88, 95% CI [0.86; 0.89]). The prevalence of IBD in the Netherlands was 577.6 (95% CI [566.7; 586.2]) per 100 000 on December 31, 2020, with varying prevalence across the Netherlands. Incidence of IBD was 20.1 (95% CI [18.0; 22.3]) per 100 000 in 2020 and stable over time. Mortality rates of IBD patients rose over time and were 11.6 (95% CI [10.5; 11.8]) per 1000 in 2020 as compared to 9.5 in the general population.

conclusionInflammatory bowel disease cases can be accurately identified using administrative data. The prevalence of IBD in the Netherlands is increasing slower than expected, suggesting a trend towards the epidemiological stage of Prevalence Equilibrium.

Indexed as

Inflammatory Bowel DiseasesMachine LearningAdolescentAdultAgedFemaleHumansIncidenceMaleMiddle AgedNetherlandsPrevalenceRegistriesYoung Adultincidenceinflammatory bowel diseasemachine learningmortalityprevalence

Identifiers

PMID39945210
PMCPMC11879025

What OpenQuestion holds

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LicenceCC BY-NC
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.