中国药业2026，Vol.35Issue(16)：143-147,5.DOI:10.3969/j.issn.1006-4931.2026.16.028
基于机器学习模型的哌拉西林钠他唑巴坦钠相关低钾血症风险预测
Risk Prediction of Piperacillin Sodium and Tazobactam Sodium-Related Hypokalemia Based on Machine Learning Models
摘要
Abstract
Objective To construct a risk prediction model for piperacillin sodium and tazobactam sodium(PTZ)-related hypokalemia to assist in early identification of high-risk patients.Methods A total of 850 hospitalized patients using PTZ in Norinco General Hospital from January 2024 to December 2024 were retrospectively collected,and were randomly divided into the training set and the internal validation set in an 8∶2 ratio.Clinical data from 227 hospitalized patients in Xi'an Fifth Hospital were collected using the same criteria as the external validation set.Least Absolute Shrinkage and Selection Operator(LASSO)regression was adopted to screen characteristic variables;six machine learning algorithms,including system comparison logistic regression(LR),were systematically compared;the predictive performance of the model was evaluated through factors such as receiver operating characteristic(ROC)curve area under the curve(AUC);and Shapley Additive Interpretation(SHAP)was adopted to analyze characteristic variable contributions.Results 15 characteristic variables were included in the model construction,and the LR model had the best overall performance with an AUC of 0.827,sensitivity of 0.651,specificity of 0.903,accuracy of 0.778,and F1 score of 0.693 in the internal validation set;the AUC in the external validation set was 0.800,indicating good generalization ability;the SHAP scatter plot showed that the top 2 importance of characteristic variables predicted by the LR model were baseline serum potassium level and cumulative PTZ dose,which were negatively and positively correlated with the risk of PTZ-related hypokalemia,respectively.Conclusion The LR model performs well in PTZ-related hypokalemia prediction task,which can be taken to intervene in important prediction characteristic variables.关键词
哌拉西林钠他唑巴坦钠/低钾血症/机器学习/预测模型/逻辑回归Key words
piperacillin sodium and tazobactam sodium/hypokalemia/machine learning/prediction model/Logistic regression分类
医药卫生引用本文复制引用
吴爽爽,冯琴琴,孙钟慧,王全华,杨建婷,席宇菁,张茜..基于机器学习模型的哌拉西林钠他唑巴坦钠相关低钾血症风险预测[J].中国药业,2026,35(16):143-147,5.基金项目
兵器工业卫生研究所新技术及管理创新项目[所运营字[2024]43号]. （）
