Application of machine learning models for predicting acute kidney injury following donation after cardiac death liver transplantation
| Autor: | Zhikun Liu, Tian Shen, Junbin Zhou, Yun-Tao Zhang, Xiao Xu, Zeng-Lei He, Shusen Zheng, Siyi Dong |
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| Rok vydání: | 2020 |
| Předmět: |
medicine.medical_treatment
Decision tree Liver transplantation urologic and male genital diseases Machine learning computer.software_genre Logistic regression Machine Learning 03 medical and health sciences 0302 clinical medicine medicine Humans Survival rate Hepatology Receiver operating characteristic urogenital system business.industry Gastroenterology Acute kidney injury Acute Kidney Injury medicine.disease female genital diseases and pregnancy complications Liver Transplantation Death ROC Curve 030220 oncology & carcinogenesis 030211 gastroenterology & hepatology Artificial intelligence business Complication computer Kidney disease |
| Zdroj: | Hepatobiliarypancreatic diseases international : HBPD INT. 20(3) |
| ISSN: | 1499-3872 |
| Popis: | 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.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).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).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. |
| Databáze: | OpenAIRE |
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