A machine learning model to predict the risk of liver dysfunction after hepatectomy in patients with hilar cholangiocarcinoma
10.3760/cma.j.cn113884-20240713-00209
- VernacularTitle:构建肝门部胆管癌患者肝切除术后肝功能不全风险预测的机器学习模型
- Author:
Changqian TANG
1
;
Bingyao LI
;
Yongnian REN
;
Hengli ZHU
;
Yuqi GUO
;
Dongxiao LI
;
Yafeng WANG
;
Shipeng LI
;
Deyu LI
;
Liancai WANG
Author Information
1. 河南大学人民医院肝胆胰外科，郑州 450003
- Publication Type:Journal Article
- Keywords:
Bile duct neoplasms;
Hilar cholangiocarcinoma;
Liver dysfunction;
Predictive models;
Machine learning
- From:
Chinese Journal of Hepatobiliary Surgery
2024;30(12):897-902
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To establish a machine learning model to predict the risk of post hepatectomy liver dysfunction (PHLD) in patients with hilar cholangiocarcinoma (HCCA).Methods:Clinical data of 203 patients with HCCA undergoing open radical hemihepatectomy in Henan University People's Hospital from January 2017 to December 2023 were retrospectively analyzed, including 112 males and 91 females, aged 63 (55, 69) years. According to the diagnostic criteria for PHLD, patients were divided into two groups: PHLD group ( n=45) and non-PHLD group ( n=158). Clinical data such as age, sex, neutrophil count (NEU), systemic immunoinflammatory index (SII), nutritional prognosis index (PNI), neutrophil to lymphocyte ratio (NLR), operative time and complications were compared between the two groups. The variables with statistically significant difference between the two groups were included in seven machine learning models, namely logistic regression, random forest, extreme gradient boosting, light gradient boosting, decision tree, gaussian naive bayes and support vector machine. The area under receiver operating characteristic curve optimization model was adopted, and Shapliga sum-interpretation method (SHAP) was used to analyze and interpret the final optimal model. Results:There were statistically significant differences in age, preoperative data including management of jaundice, albumin, total bilirubin, aspartate aminotransferase, NEU, SII, PNI, and NLR, operative time, postoperative complication of Dindo-Clavien≥Grade Ⅲ, and the ratio of FLR/TLV between in the two groups (all P<0.05). Finally, it was determined that the prediction performance of the extreme gradient boosting model was the best, with an area under curve of 0.888 (95% CI: 0.776-0.985), an accuracy of 0.854, a sensitivity of 0.506, a specificity of 0.965, an F1 value of 0.625, and a Kappa value of 0.519. SHAP analysis of the extreme gradient boosting model showed that total bilirubin on admission, operation time, postoperative complication of Dindo-Clavien≥grade Ⅲ, SII and NEU were five important factors of this model, which were positively correlated with the occurrence of PHLD in HCCA patients. Conclusion:The extreme gradient boosting model established in this study has a good predictive performance and stability for PHLD in HCCA patients.
