An explainable machine learning model to predict early and late acute kidney injury after major hepatectomy.

BACKGROUND: Risk assessment models for acute kidney injury (AKI) after major hepatectomy that differentiate between early and late AKI are lacking. This retrospective study aimed to create a model predicting AKI through machine learning and identify features that contribute to the development of early and late AKI. METHODS: Patients that underwent major hepatectomy were categorized into the No-AKI, Early-AKI (within 48 h) or Late-AKI group (between 48 h and 7 days). Modeling was done with 20 perioperative features and the performance of prediction models were measured by the area under the receiver operating characteristic curve (AUROCC). Shapley Additive Explanation (SHAP) values were utilized to explain the outcome of the prediction model. RESULTS: Of the 1383 patients included in this study, 1229, 110 and 44 patients were categorized into the No-AKI, Early-AKI and Late-AKI group, respectively. The CatBoost classifier exhibited the greatest AUROCC of 0.758 (95% CI: 0.671-0.847) and was found to differentiate well between Early and Late-AKI. We identified different perioperative features for predicting each outcome and found 1-year mortality to be greater for Early-AKI. CONCLUSIONS: Our results suggest that risk factors are different for Early and Late-AKI after major hepatectomy, and 1-year mortality is greater for Early-AKI.

New scoring system for resectable hepatocellular carcinoma with a maximum tumor size of ≤5 cm based on preoperative tumor factors.

BACKGROUND: The aim of this study was to establish a new scoring system for hepatocellular carcinoma (HCC) that can be used to predict the postoperative prognosis of HCC patients. METHODS: A total of 359 HCC patients who underwent hepatectomy were included in this study. All eligible patients were randomly allocated to derivation cohort or validation cohort samples. We assigned one point each for preoperative factors identified in the derivation cohort, and the sum of the scores was used to classify the patients into high-risk and low-risk groups. The scoring system established using the derivation cohort was fitted to the validation cohort. RESULTS: The prognosis of the high-risk group was significantly poorer than that of the low-risk group in both the derivation and validation samples (p = 0.04, p < 0.01, respectively). In the high-risk group, major hepatectomy resulted in a significantly better prognosis than minor hepatectomy in both samples (p = 0.04, p = 0.03, respectively). On the other hand, the extent of hepatectomy did not influence the prognosis of the low-risk group in either sample (p = 0.14, p = 0.34, respectively). CONCLUSION: Our new scoring system can predict the treatment outcome of patients undergoing curative hepatectomy for HCC and could help determine the optimal extent of resection.