An explainable supervised machine learning predictor of acute kidney injury after adult deceased donor liver transplantation.
BACKGROUND
Early prediction of acute kidney injury (AKI) after liver transplantation (LT) facilitates timely recognition and intervention. We aimed to build a risk predictor of post-LT AKI via supervised machine learning and visualize the mechanism driving within to assist clinical decision-making.


METHODS
Data of 894 cases that underwent liver transplantation from January 2015 to September 2019 were collected, covering demographics, donor characteristics, etiology, peri-operative laboratory results, co-morbidities and medications. The primary outcome was new-onset AKI after LT according to Kidney Disease Improving Global Outcomes guidelines. Predicting performance of five classifiers including logistic regression, support vector machine, random forest, gradient boosting machine (GBM) and adaptive boosting were respectively evaluated by the area under the receiver-operating characteristic curve (AUC), accuracy, F1-score, sensitivity and specificity. Model with the best performance was validated in an independent dataset involving 195 adult LT cases from October 2019 to March 2021. SHapley Additive exPlanations (SHAP) method was applied to evaluate feature importance and explain the predictions made by ML algorithms.


RESULTS
430 AKI cases (55.1%) were diagnosed out of 780 included cases. The GBM model achieved the highest AUC (0.76, CI 0.70 to 0.82), F1-score (0.73, CI 0.66 to 0.79) and sensitivity (0.74, CI 0.66 to 0.8) in the internal validation set, and a comparable AUC (0.75, CI 0.67 to 0.81) in the external validation set. High preoperative indirect bilirubin, low intraoperative urine output, long anesthesia time, low preoperative platelets, and graft steatosis graded NASH CRN 1 and above were revealed by SHAP method the top 5 important variables contributing to the diagnosis of post-LT AKI made by GBM model.


CONCLUSIONS
Our GBM-based predictor of post-LT AKI provides a highly interoperable tool across institutions to assist decision-making after LT.
['Zhang|Yihan|Y|', 'Yang|Dong|D|', 'Liu|Zifeng|Z|', 'Chen|Chaojin|C|', 'Ge|Mian|M|', 'Li|Xiang|X|', 'Luo|Tongsen|T|', 'Wu|Zhengdong|Z|', 'Shi|Chenguang|C|', 'Wang|Bohan|B|', 'Huang|Xiaoshuai|X|', 'Zhang|Xiaodong|X|', 'Zhou|Shaoli|S|', 'Hei|Ziqing|Z|0000-0002-1256-5899']
D058186:Acute Kidney Injury D000328:Adult D006801:Humans D016031:Liver Transplantation D019520:Living Donors D000069550:Machine Learning D018570:Risk Assessment D000069553:Supervised Machine Learning
Liver transplant Gradient boosting machine Artificial intelligence Big data Clinical assisting tool Kidney dysfunction Perioperative medicine Prognostic predictor SHAP value SHapley Additive exPlaination methods