Assessment of Malnutrition in Crohn's Disease Patients: A Novel Risk Prediction Model with Dynamic Optimization Potential and Effectiveness Validation

Summary

This study constructed and validated a novel malnutrition risk prediction model for patients with Crohn’s disease using meta-analysis, multivariable logistic regression, and machine learning. The model demonstrated good predictive performance and clinical utility for guiding personalized nutritional interventions.

Abstract

This study aimed to construct and validate a malnutrition risk prediction model combining multivariable logistic regression and machine learning for patients with Crohn’s disease (CD), with the goal of improving the precision of malnutrition risk identification through integration of inflammatory markers and disease characteristics. PubMed, Web of Science, Cochrane Library, Embase, and China National Knowledge Infrastructure (CNKI) were systematically searched to identify risk factors associated with malnutrition in patients with CD. High-quality studies using the Global Leadership Initiative on Malnutrition (GLIM) 2019 criteria, European Society for Clinical Nutrition and Metabolism (ESPEN) 2015 criteria, or Malnutrition Universal Screening Tool (MUST) criteria were included in the meta-analysis, while the study cohort applied the ESPEN 2015 criteria exclusively to ensure consistent outcome definition. The prediction model was developed using data from 800 patients with CD from the Inflammatory Bowel Disease Cohort Database (IBDCD) and validated using bootstrap resampling and an independent non-overlapping hold-out subset of 280 patients from the same database. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), Hosmer–Lemeshow test, and Brier score. No significant baseline differences were observed between the training and validation cohorts. The model achieved an AUC of 0.987 in the training cohort and 0.967 in the validation cohort, demonstrating good discrimination and calibration. Decision curve analysis further demonstrated clinically meaningful net benefits across relevant threshold probabilities. This model effectively identifies malnutrition risk in patients with CD and may support personalized nutritional intervention, optimize clinical decision-making, and improve patient outcomes and quality of life. Future multicenter studies are required to further validate the model's generalizability and to evaluate the integration of socioeconomic factors for further optimization.

Introduction

Crohn’s disease (CD) is a chronic, progressive inflammatory bowel disease (IBD) with a multifactorial etiology, and its global burden has increased substantially in recent years¹. Epidemiological studies indicate that although CD remains more prevalent in Western countries, the incidence and prevalence of CD in Asian populations, particularly in China, have increased markedly over the past three decades¹.

Malnutrition is one of the most common and clinically significant complications in patients with CD.^2 Approximately one-third to one-half of patients with CD experience moderate to sev....

Protocol

All patients whose data were entered into the Inflammatory Bowel Disease Cohort Database (IBDCD) provided written informed consent for the use of de-identified clinical data for research purposes at the time of database enrollment. The same ethical approval and consent framework applied to all data extracted from the database, including both the development and validation cohorts. No identifiable patient information was used in this study, and all data were de-identified and encrypted for storage. As this study involved retrospective analysis of de-identified data from an established database, the ethics committee waived the requirement for additional patient consent ....

Results

Basic characteristics of the study population
This meta-analysis included 17 high-quality studies evaluating malnutrition in patients with Crohn’s disease, as summarized in Supplementary Table 1. The studies were published between 2009 and 2025, with a median sample size of 502 patients (range: 175–773). Study designs included prospective cohort, retrospective cohort, case-control, cross-sectional, and mixed-methods studies, with mixed-methods studies accounting for 17.6% (3/17) of t.......

Discussion

Crohn’s disease (CD) is a chronic, recurrent inflammatory bowel disease that can affect any part of the gastrointestinal tract^1,35,36,37. The etiology of CD remains unclear and is thought to involve multiple factors, including genetic susceptibility, environmental exposures, and immune dysregulation. Common clinical manifestations include abdominal pain, diarrhea, weight loss, and fistula.......

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
RevMan software	Cochrane Collaboration‌	5.4	The professional meta-analysis software developed by the Cochrane collaboration is mainly used for the formulation of systematic reviews and meta-analyses, data entry, and visualization of results. ‌
R software	The R Project for Statistical Computing	4.2.1	R is a branch of the S language that was widely used in the field of statistics and was born around 1980. It can be regarded as an implementation of the S language. The S language, developed by AT&T Bell LABS, is an interpretive language used for data exploration, statistical analysis and graphing