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Updated: Jul 23, 2025

Establishing a Competing Risk Regression Nomogram Model for Survival Data
Published on: October 23, 2020
Development and validation of a nomogram model for predicting chronic kidney disease after liver transplantation: a
Zenglei He1, Yimou Lin1, Siyi Dong2
1Department of Hepatobiliary and Pancreatic Surgery, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310003, China.
Abstract:
Chronic kidney disease (CKD) is a frequent complication after liver transplantation (LT) and associated with poor prognosis. In this study, we retrospectively analyzed 515 adult patients who underwent LT in our center. They were randomly divided into a training set (n = 360) and an internal test set (n = 155). Another 118 recipients in other centers served as external validation set. Univariate and multivariate COX regression analysis were used to determine risk factors. A nomogram model was developed to predict post-LT CKD. The incidence of post-LT CKD in our center was 16.9% (87/515) during a median follow-up time of 22.73 months. The overall survival of recipients with severe CKD (stage IV and V) were significantly lower than those with non or mild CKD (stage III) (p = 0.0015). A nomogram model was established based on recipient's age, anhepatic phase, estimated glomerular filtration rate and triglyceride levels at 30 days after LT. The calibration curves for post-LT CKD prediction in the nomogram were consistent with the actual observation in both the internal and external validation set. In conclusion, severe post-LT CKD resulted in a significantly reduced survival in liver recipient. The newly established nomogram model had good predictive ability for post-LT CKD.
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