ArticleEClinicalMedicine2025
Development and validation of a machine learning-based model to predict survival in patients with cirrhosis after transjugular intrahepatic portosystemic shunt.
Article in EClinicalMedicine, 2025. 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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3 citing papers in PubMed.
- Toward personalized prediction: a multicenter machine learning model for omalizumab response duration in moderate-to-severe perennial allergic rhinitis.Frontiers in allergy · 2026Article
- Machine Learning-Based Prediction of Decompensation in Hepatitis B Virus-Related Cirrhosis.Diagnostics (Basel, Switzerland) · 2025Article
- The Global Burden of Migraine: A 30-Year Trend Review and Future Projections by Age, Sex, Country, and Region.Pain and therapy · 2025Article
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13 authors.
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Abstract
Background: Although numerous prognostic scores have been developed for patients with cirrhosis after Transjugular intrahepatic portosystemic shunt (TIPS) placement over years, an accurate machine learning (ML)-based model remains unavailable. The aim of this study was to develop and validate a ML-based prognostic model to predict survival in patients with cirrhosis after TIPS placement. Methods: In this retrospective study in China, patients diagnosed with cirrhosis after TIPS placement from 2014 to 2020 in our cohort were included to develop a ML-based model. Patients from the other two tertiary hospitals between 2016 and 2022 were as external validation cohort. The random forest (RF) model was built using 7 selected features via the least absolute shrinkage and selection operator (LASSO) regression, and subsequent 10-fold cross-validation was performed. Findings: A total of 400 patients in our cohort were included (median age and interquartile range, 59 (50, 66); 240 men). Two hundred and eighty patients made up the training set and 120 were in the testing set, and 346 patients were included in the external validation cohort. Seven attributes were selected: Na, ammonia (Amm), total bilirubin (Tb), albumin (Alb), age, creatinine (Cr), and ascites. These parameters were included in a new score named the RF model. The accuracy, precision, recall, and F1 Score of the RF model were 0.84 (95% CI: 0.76, 0.91), 0.84 (95% CI: 0.77, 0.91), 0.99 (95% CI: 0.95, 1.00), 0.91 (95% CI: 0.81, 0.10) in the testing set, and 0.88 (95% CI: 0.84, 0.91), 0.89 (95% CI: 0.85, 0.92), 0.99 (95% CI: 0.97, 1.00), 0.93 (95% CI: 0.85, 0.97) in the validation cohort, respectively. The calibration curve showed a slope of 0.875 in the testing set and a slope of 0.778 in the external validation cohort, suggesting well calibration performance. The RF model outperformed other scoring systems, such as the (Child-Turcotte-Pugh score) CTP, (model for end-stage liver disease) MELD, (sodium MELD) MELD-Na, (Freiburg index of post-TIPS survival) FIPS and (Albumin-Bilirubin) ALBI, showing the highest (area under the curve) AUC of 0.82 (95% CI: 0.72, 0.91) and 0.7 (95% CI: 0.60, 0.79) in predicting 1-year survival across the testing set and external validation cohort. Interpretation: This study developed a RF model that better predicted 1-year survival for patients with cirrhosis after TIPS placement than the other scores. Funding: National Natural Science Foundation of China (grant numbers 81900552 and 82370628).
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