ArticleWorld journal of gastrointestinal surgery2026
Development and validation of a risk prediction model for small intestinal mucosal injury in nonsteroidal anti-inflammatory drug users.
Article in World journal of gastrointestinal surgery, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundDespite the enhanced detection capability of capsule endoscopy for small intestinal lesions, the independent risk factors and distinct clinical-endoscopic characteristics in high-risk populations remain incompletely elucidated.
aimTo investigate the risk factors for small intestinal mucosal injury (SIMI) in nonsteroidal anti-inflammatory drug (NSAID) users and to establish a corresponding predictive model.
methodsWe conducted a retrospective analysis of clinical data from patients undergoing capsule endoscopy at our institution and Shanxi Bethune Hospital from August 2012 to January 2025. Multivariate analysis was performed to identify independent risk factors for SIMI. A nomogram was subsequently developed to predict the risk of SIMI in NSAID users. Receiver operating characteristic curve analysis, calibration curve and decision curve analysis were performed to evaluate the predictive accuracy of the nomogram.
resultsIn the primary cohort, SIMI was identified in 114 out of 181 NSAID users. Multivariate analysis revealed that advanced age, smoking, proton pump inhibitor use, elevated body mass index, high triglyceride levels, and increased low-density lipoprotein were independent risk factors for SIMI. The research team developed a risk prediction model for estimating the risk of mucosal injury, which achieved an area under the curve of 0.775 (95% confidence interval: 0.700-0.849) in the derivation set. The model exhibited an area under the curve of 0.797 (95% confidence interval: 0.643-0.952) in the validation cohort.
conclusionWe identified risk factors for SIMI in NSAID users and established a predictive model, which may facilitate early identification of high-risk populations and guide clinical interventions.
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