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| Application of machine learning models for predicting acute kidney injury following donation after cardiac death liver transplantation |
| Zeng-Lei He a , b , Jun-Bin Zhou a , b , Zhi-Kun Liu a , b , Si-Yi Dong a , b , Yun-Tao Zhang a , b , Tian Shen a , b , Shu-Sen Zheng a , b , Xiao Xu a , b , ∗ |
a Division of Hepatobiliary and Pancreatic Surgery, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310003, China
b Key Laboratory of Combined Multi-organ Transplantation, Ministry of Public Health, the First Affiliated Hospital, Zhejiang University School of Medicine,
Hangzhou 310003, China
∗ Corresponding author at: Division of Hepatobiliary and Pancreatic Surgery, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310003, China
E-mail address: zjxu@zju.edu.cn (X. Xu). |
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Abstract Background: Acute kidney injury (AKI) is a common complication after liver transplantation (LT) and is an indicator of poor prognosis. The establishment of a more accurate preoperative prediction model of AKI could help to improve the prognosis of LT. Machine learning algorithms provide a potentially effective approach.
Methods: A total of 493 patients with donation after cardiac death LT (DCDLT) were enrolled. AKI was defined according to the clinical practice guidelines of kidney disease: improving global outcomes (KDIGO). The clinical data of patients with AKI (AKI group) and without AKI (non-AKI group) were compared. With logistic regression analysis as a conventional model, four predictive machine learning models were developed using the following algorithms: random forest, support vector machine, classical decision tree, and conditional inference tree. The predictive power of these models was then evaluated using the area under the receiver operating characteristic curve (AUC).
Results: The incidence of AKI was 35.7% (176/493) during the follow-up period. Compared with the non-AKI group, the AKI group showed a remarkably lower survival rate ( P < 0.001). The random forest model demonstrated the highest prediction accuracy of 0.79 with AUC of 0.850 [95% confidence interval (CI): 0.794–0.905], which was significantly higher than the AUCs of the other machine learning algorithms and logistic regression models ( P < 0.001).
Conclusions: The random forest model based on machine learning algorithms for predicting AKI occurring after DCDLT demonstrated stronger predictive power than other models in our study. This suggests that machine learning methods may provide feasible tools for forecasting AKI after DCDLT.
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2021, Vol. 20 
