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Updated: Jun 28, 2026
Technique of Porcine Liver Procurement and Orthotopic Transplantation using an Active Porto-Caval Shunt
Published on: May 7, 2015
Development and validation of a machine learning-based model to predict survival in patients with cirrhosis after
Binlin Da1,2, Huan Chen3, Wei Wu4
1Department of Gastroenterology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, Jiangsu, China.
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).
