ArticleGut and liver2022
Risk Prediction Model Based on Magnetic Resonance Elastography-Assessed Liver Stiffness for Predicting Posthepatectomy Liver Failure in Patients with Hepatocellular Carcinoma.
Article in Gut and liver, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 7 citations in OpenAlex.
- Multivariable prognostic models for post-hepatectomy liver failure: An updated systematic review.World journal of hepatology · 2025Article
- Risk predictors of post-hepatectomy liver failure: unraveling complexities and navigating challenges in clinical application.Hepatobiliary surgery and nutrition · 2024Article
- Current status of preoperative risk assessment for posthepatectomy liver failure in patients with hepatocellular carcinoma.Annals of gastroenterological surgery · 2023Review
- Diagnostic Performance of a Comprehensive Risk Model for Posthepatectomy Liver Failure.Gut and liver · 2022Article
- Machine learning prediction model for post- hepatectomy liver failure in hepatocellular carcinoma: A multicenter study.Frontiers in oncology · 2022Article
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Authors and funding
12 authors at 4 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
BACKGROUND/
aimsPosthepatectomy liver failure (PHLF) is a major complication that increases mortality in patients with hepatocellular carcinoma after surgical resection. The aim of this retrospective study was to evaluate the utility of magnetic resonance elastography-assessed liver stiffness (MRE-LS) for the prediction of PHLF and to develop an MRE-LS-based risk prediction model.
methodsA total of 160 hepatocellular carcinoma patients who underwent surgical resection with available preoperative MRE-LS data were enrolled. Clinical and laboratory parameters were collected from medical records. Logistic regression analyses were conducted to identify the risk factors for PHLF and develop a risk prediction model.
resultsPHLF was present in 24 patients (15%). In the multivariate logistic analysis, high MRE-LS (kPa; odds ratio [OR] 1.49, 95% confidence interval [CI] 1.12 to 1.98, p=0.006), low serum albumin (≤3.8 g/dL; OR 15.89, 95% CI 2.41 to 104.82, p=0.004), major hepatic resection (OR 4.16, 95% CI 1.40 to 12.38, p=0.014), higher albumin-bilirubin score (>-0.55; OR 3.72, 95% CI 1.15 to 12.04, p=0.028), and higher serum α-fetoprotein (>100 ng/mL; OR 3.53, 95% CI 1.20 to 10.40, p=0.022) were identified as independent risk factors for PHLF. A risk prediction model for PHLF was established using the multivariate logistic regression equation. The area under the receiver operating characteristic curve (AUC) of the risk prediction model was 0.877 for predicting PHLF and 0.923 for predicting grade B and C PHLF. In leave-one-out cross-validation, the risk model showed good performance, with AUCs of 0.807 for all-grade PHLF and 0. 871 for grade B and C PHLF.
conclusionsOur novel MRE-LS-based risk model had excellent performance in predicting PHLF, especially grade B and C PHLF.
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