ArticleFrontiers in surgery2023
A nomogram model to predict the portal vein thrombosis risk after surgery in patients with pancreatic cancer.
Article in Frontiers in surgery, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- [Nomogram and machine learning models for predicting in-hospital mortality in sepsis patients with deep vein thrombosis].Zhong nan da xue xue bao. Yi xue ban = Journal of Central South University. Medical sciences · 2025Article
- Organ-specific role of neutrophil extracellular traps in shaping the postoperative thrombo-inflammatory niche in pancreatic ductal adenocarcinoma: a multi-model machine learning and mechanistic study.Frontiers in immunology · 2025Article
- Development and Empirical Study of a Lower Extremity Deep Vein Thrombosis Prevention Protocol for Patients Undergoing Gynecologic Laparoscopic Surgery.Clinical and applied thrombosis/hemostasis : official journal of the International Academy of Clinical and Applied Thrombosis/HemostasisArticle
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Authors and funding
6 authors.
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Abstract
Background: Portal vein thrombosis (PVT) is a common postoperative complication in patients with pancreatic cancer (PC), significantly affecting their quality of life and long-term prognosis. Our aim is to establish a new nomogram to predict the risk of PVT after PC surgery. Method: We collected data from 416 patients who underwent PC surgery at our hospital between January 2011 and June 2022. This includes 87 patients with PVT and 329 patients without PVT. The patients were randomly divided into a training group and a validation group at a ratio of 7:3. We constructed a nomogram model using the outcomes from both univariate and multivariate logistic regression analyses conducted on the training group. The nomogram's predictive capacity was assessed using calibration curve, receiver operating characteristic (ROC) curve, and decision curve analysis (DCA). Results: In the study, the prevalence of PVT was 20.9%. Age, albumin, vein reconstruction and preoperative D-dimer were independent related factors. The model achieved a C-index of 0.810 (95% confidence interval: 0.752-0.867), demonstrating excellent discrimination and calibration performance. The area under the ROC curve of the nomogram was 0.829 (95% CI: 0.750-0.909) in the validation group. DCA confirmed that the nomogram model was clinically useful when the incidence of PVT in patients was 5%-60%. Conclusion: We have established a high-performance nomogram for predicting the risk of PVT in patients undergoing PC surgery. This will assist clinical doctors in identifying individuals at high risk of PVT and taking appropriate preventive measures.
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