ArticleWorld journal of gastroenterology2025
Predicting colorectal adenomatous polyps in patients with chronic liver disease: A novel nomogram.
Article in World journal of gastroenterology, 2025. 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
backgroundColorectal polyps are commonly observed in patients with chronic liver disease (CLD) and pose a significant clinical concern because of their potential for malignancy.
aimTo explore the clinical characteristics of colorectal polyps in patients with CLD, a nomogram was established to predict the presence of adenomatous polyps (AP).
methodsPatients with CLD who underwent colonoscopy at Tianjin Second People's Hospital from January 2020 to May 2023 were evaluated. Clinical data including laboratory results, colonoscopy findings, and pathology reports were collected. Key variables for the nomogram were identified through least absolute shrinkage and selection operator regression, followed by multivariate logistic regression. The performance of the model was evaluated using the area under the receiver area under curve, as well as calibration curves and decision curve analysis.
resultsThe study enrolled 870 participants who underwent colonoscopy, and the detection rate of AP in patients with CLD was 28.6%. Compared to individuals without polyps, six risk factors were identified as predictors for AP occurrence: Age, male sex, body mass index, alcohol consumption, overlapping metabolic dysfunction-associated steatotic liver disease, and serum ferritin levels. The novel nomogram (AP model) demonstrated an area under curve of 0.801 (95% confidence interval: 0.756-0.845) and 0.785 (95% confidence interval: 0.712-0.858) in the training and validation groups. Calibration curves indicated good agreement among predicted and actual probabilities (training:
conclusionThe AP model showed reasonable accuracy and provided a clinical foundation for predicting the occurrence of AP in patients with CLD, which has a certain predictive value.
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