ArticleWorld journal of gastroenterology2020
Non-invasive prediction model for high-risk esophageal varices in the Chinese population.
Article in World journal of gastroenterology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 11 citations in OpenAlex.
- Development of a noninvasive ultrasound-based model for predicting high-risk esophageal varices in hepatitis-related cirrhosis.Ultrasonography (Seoul, Korea) · 2026Article
- Application of artificial intelligence in portal hypertension and esophagogastric varices.World journal of gastroenterology · 2025Review
- Development and validation of an interpretable machine learning model for standard spleen volume prediction.Quantitative imaging in medicine and surgery · 2025Article
- Development of a nomogram model based on spleen volume change to predict high-risk esophageal varices in patients with liver cirrhosis.Frontiers in surgery · 2025Article
- Diagnosis, Management, and Prognosis of Cystic Fibrosis-Related Liver Disease in Children.Diagnostics (Basel, Switzerland) · 2024Review
- A nomogram for predicting the 4-year risk of chronic kidney disease among Chinese elderly adults.International urology and nephrology · 2023Review
- Non-invasive model for predicting esophageal varices based on liver and spleen volume.World journal of clinical cases · 2022Article
- Quantitative measurements of esophageal varices using computed tomography for prediction of severe varices and the risk of bleeding: a preliminary study.Insights into imaging · 2022Article
- Nomogram based on spleen volume expansion rate predicts esophagogastric varices bleeding risk in patients with hepatitis B liver cirrhosis.Frontiers in surgery · 2022Article
- Prediction Modelling for Gastroesophageal Variceal Bleeding in Patients With Chronic Hepatitis B Using Four-dimensional Flow MRI.Journal of clinical and experimental hepatologyArticle
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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
backgroundThere are two types of esophageal varices (EVs): high-risk EVs (HEVs) and low-risk EVs, and HEVs pose a greater threat to patient life than low-risk EVs. The diagnosis of EVs is mainly conducted by gastroscopy, which can cause discomfort to patients, or by non-invasive prediction models. A number of non-invasive models for predicting EVs have been reported; however, those that are based on the formula for calculation of liver and spleen volume in HEVs have not been reported.
aimTo establish a non-invasive prediction model based on the formula for liver and spleen volume for predicting HEVs in patients with viral cirrhosis.
methodsData from 86 EV patients with viral cirrhosis were collected. Actual liver and spleen volumes of the patients were determined by computed tomography, and their calculated liver and spleen volumes were calculated by standard formulas. Other imaging and biochemical data were determined. The impact of each parameter on HEVs was analyzed by univariate and multivariate analyses, the data from which were employed to establish a non-invasive prediction model. Then the established prediction model was compared with other previous prediction models. Finally, the discriminating ability, calibration ability, and clinical efficacy of the new model was verified in both the modeling group and the external validation group.
resultsData from univariate and multivariate analyses indicated that the liver-spleen volume ratio, spleen volume change rate, and aspartate aminotransferase were correlated with HEVs. These indexes were successfully used to establish the non-invasive prediction model. The comparison of the models showed that the established model could better predict HEVs compared with previous models. The discriminating ability, calibration ability, and clinical efficacy of the new model were affirmed.
conclusionThe non-invasive prediction model for predicting HEVs in patients with viral cirrhosis was successfully established. The new model is reliable for predicting HEVs and has clinical applicability.
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