ArticleWorld journal of clinical cases2022
Non-invasive model for predicting esophageal varices based on liver and spleen volume.
Article in World journal of clinical cases, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed, 3 citations in OpenAlex.
- Development of a clinical-CT-radiomics nomogram for predicting endoscopic red color sign in cirrhotic patients with esophageal varices.Abdominal radiology (New York) · 2026Article
- The impact of hepatic and splenic volumetric assessment in imaging for chronic liver disease: a narrative review.Insights into imaging · 2024Review
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Authors and funding
9 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundUpper endoscopy is the gold standard for predicting esophageal varices in China. Guidelines and consensus suggest that patients with liver cirrhosis should undergo periodic upper endoscopy, most patients undergo their first upper endoscopy when esophageal variceal bleeds. Therefore, it is important to develop a non-invasive model to early diagnose esophageal varices.
aimTo develop a non-invasive predictive model for esophageal varices based on liver and spleen volume in viral cirrhosis patients.
methodsWe conducted a cross-sectional study based on viral cirrhosis crowd in the Second Affiliated Hospital of Xi'an Jiaotong University. By collecting the basic information and clinical data of the participants, we derived the independent risk factors and established the prediction model of esophageal varices. The established model was compared with other models. Area under the receiver operating characteristic curve, calibration plot and decision curve analysis were used to test the discriminating ability, calibration ability and clinical practicability in both the internal and external validation.
resultsThe portal vein diameter, the liver and spleen volume, and volume change rate were the independent risk factors of esophageal varices. We successfully used the factors to establish the predictive model [area under the curve (AUC) 0.87, 95%CI: 0.80-0.95], which showed better predictive value than other models. The model showed good discriminating ability, calibration ability and the clinical practicability in both modelling group and external validation group.
conclusionThe developed non-invasive predictive model can be used as an effective tool for predicting esophageal varices in viral cirrhosis patients.
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