ArticleJournal of personalized medicine2023
Nomogram for Predicting Portal Vein Thrombosis in Cirrhotic Patients: A Retrospective Cohort Study.
Article in Journal of personalized medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 7 citations in OpenAlex.
- A Retrospective Analysis of 581 Cases of Esophagogastric Variceal Bleeding Complicated by Portal Vein Thrombosis at a Single Center.Canadian journal of gastroenterology & hepatology · 2026Article
- Article
- Portal Vein Thrombosis in Liver Cirrhosis: A Review of Risk Factors and Predictive Indicators.Journal of clinical and translational hepatology · 2025Review
- [Research progress on predictive models of portal vein thrombosis in liver cirrhosis].Zhonghua gan zang bing za zhi = Zhonghua ganzangbing zazhi = Chinese journal of hepatology · 2025Review
- Nomogram model for identifying portal vein thrombosis in patients with decompensated cirrhosis.European journal of gastroenterology & hepatology · 2025Article
- Hypoalbuminemia and Risk of Portal Vein Thrombosis in Cirrhosis.Gastro hep advances · 2024Article
- Towards Personalized Medicine for Chronic Liver Disease.Journal of personalized medicine · 2023Article
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Authors and funding
6 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
aimPortal vein thrombosis (PVT) is a common complication in cirrhotic patients and will aggravate portal hypertension, thus leading to a series of severe complications. The aim of this study was to develop a nomogram based on a simple and effective model to predict PVT in cirrhotic patients.
methodsClinical data of 656 cirrhotic patients with or without PVT in the First Affiliated Hospital of Soochow University and The Third Affiliated Hospital of Nantong University from January 2017 to March 2022 were retrospectively collected, and all patients were divided into training, internal and external validation cohorts. SPSS and R software were used to identify the independent risk factors and construct a predictive model. We evaluated the predictive value of the model by receiver operating characteristic (ROC) curve, calibration curve, and decision curve analyses. The feasibility of the model was further validated in the internal and external cohorts. All enrolled patients were followed up to construct the survival curves and calculate the incidence of complications.
resultsThe predictors of PVT included serum albumin, D-dimer, portal vein diameter, splenectomy, and esophageal and gastric varices. Based on the clinical and imaging findings, the final model served as a potential tool for predicting PVT in cirrhotic patients, with an AUC of 0.806 (0.766 in the internal validation cohort and 0.845 in the external validation cohort). The decision curve analysis revealed that the model had a high level of concordance between different medical centers. There was a significant difference between the PVT and non-PVT groups in survival analyses, with
conclusionThe nomogram based on our model provides a simple and convenient method for predicting PVT in cirrhotic patients. Cirrhotic patients with PVT had a shorter survival time and were prone to recurrent EGVB compared with those in the non-PVT group.
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