Controlling nutritional status score predicts posthepatectomy liver failure: an online interpretable machine learning
Jun Yuan1, Rui Qing Zhang1, Qiang Guo1
1Department of Hepatobiliary and Echinococcosis Surgery, Digestive and Vascular Surgery Center, The First Affiliated Hospital.
Background And Aims:
Posthepatectomy liver failure (PHLF) remains a severe complication after hepatectomy for hepatocellular carcinoma (HCC) and accurate preoperative evaluation and predictive measures are urgently needed. We investigated the impact of the controlling nutritional status (CONUT) score on PHLF and utilized machine learning (ML) algorithms to identify high-risk individuals of PHLF.
Method:
A total of 464 patients with HCC undergoing hepatectomy were randomized 7 : 2: 1 into the training group ( n = 324), test group ( n = 94), and validation group ( n = 46). In the training group, variables were screened by univariate logistic regression combined with least absolute shrinkage and selection operator regression. Models were then developed using nine ML algorithms and the optimal model was interpreted via SHapley Additive exPlanations and deployed online.
Results:
PHLF was present in 29 of 324 (8.9%) patients. The light gradient boosting machine (LightGBM) model based on the CONUT score exhibited excellent performance, with an area under the curve (AUC) of 0.927 [95% confidence interval (CI): 0.886-0.967], an area under the precision-recall curve (AUPRC) of 0.644 (95% CI: 0.469-0.785), and a Brier score of 0.055 in the training group. And an AUC of 0.703 (95% CI: 0.528-0.879), an AUPRC of 0.420 (95% CI: 0.096-0.703), and a Brier score of 0.091 in the test group. In the validation group, AUC, AUPRC, and Brier score were 0.808 (95% CI: 0.637-0.980), 0.516 (95% CI: 0.086-0.841), and 0.096, respectively. The model was made available online for clinical application (LightGBM for PHLF).
Conclusion:
The CONUT score significantly influences PHLF. The LightGBM model demonstrates the prominent predictive capacity of PHLF.
