Establishment of risk prediction model for postoperative liver injury after non-liver surgery based on different machine learning algorithms
10.16016/j.2097-0927.202312018
- VernacularTitle:基于机器学习算法建立非肝脏手术术后肝损伤风险预测模型
- Author:
Yizhu SUN
1
;
Yujie LI
;
Hao LIANG
;
Xiang LIU
;
Jiahao HUANG
;
Xin SHU
;
Ailin SONG
;
Zhiyong YANG
;
Bin YI
Author Information
1. 400038 重庆,陆军军医大学(第三军医大学)第一附属医院麻醉科
- Keywords:
machine learning;
predicting model;
postoperative liver injury
- From:
Journal of Army Medical University
2024;46(7):760-767
- CountryChina
- Language:Chinese
-
Abstract:
Objective To construct a machine learning prediction model for postoperative liver injury in patients with non-liver surgery based on preoperative and intraoperative medication indicators.Methods A case-control study was conducted on 315 patients with liver injury after non-liver surgery selected from the databases developed by 3 large general hospitals from January 2014 to September 2022.With the positive/negative ratio of 1 ∶3,928 cases in corresponding period with non-liver surgery and without liver injury were randomly matched as negative control cases.These 1243 patients were randomly divided into the modeling group(n=869)and the validation group(n=374)in a ratio of 7∶3 using the R language setting code.Preoperative clinical indicators(basic information,medical history,relevant scale score,surgical information and results of laboratory tests)and intraoperative medication were used to construct the prediction model for liver injury after non-liver surgery based on 4 machine learning algorithms,k-nearest neighbor(KNN),support vector machine linear(SVM),logic regression(LR)and extreme gradient boosting(XGBoost).In the validation group,receiver operating characteristic(ROC)curve,precision-recall curve(P-R),decision curve analysis(DCA)curve,Kappa value,sensitivity,specificity,Brier score,and F1 score were applied to evaluate the efficacy of model.Results The model established by 4 machine learning algorithms to predict postoperative liver injury after non-liver surgery was optimal using the XGBoost algorithm.The area under the receiver operating characteristic curve(AUROC)was 0.916(95%CI:0.883～0.949),area under the precision-recall curve(AUPRC)was 0.841,Brier score was 0.097,and sensitivity and specificity was 78.95%and 87.10%,respectively.Conclusion The postoperative liver injury prediction model for non-liver surgery based on the XGBoost algorithm has effective prediction for the occurrence of postoperative liver injury.
