基于机器学习的老年患者万古霉素相关急性肾损伤危险因素分析

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【中图分类号】R978.1 【文献标识码】A

【Abstract】Objective To explore the risk factors for vancomycin-related acute kidney injury （VA-AKI） in elderly patients. Methods Clinical data of elderly inpatients who used vancomycin at the Inner Mongolia Autonomous Region People's Hospital from January 2021 to June 2024 were retrospectively collcted. The incidence of VA-AKI and the situation of treatment drug monitoring （TDM） were statistically analyzed.LAssO regression was used for feature selection，and this process was repeated 10，000 times.In each iteration， 75% ofthe training sampleswere randomly selected，and the frequency of each feature being selected was counted. Finall，the features with higher frequency in multiple iterations were selected for model training.Thedata were divided into training set and test set at an 8：2 ratio.Four machine learning prediction models，including Logistic regression，random forest， extreme gradient boosting （XGBoost）， and support vector machine （SVM）， were established. The accuracy and area under the receiver operating characteristic curve （AUC）of the above prediction models were calculated in the test set. The minimum depth distribution was used to visualize the importance of the characteristics of the model. Results A total of 305 elderly patients receiving vancomycin were included， among which 49 cases （16.07% ）developed VA-AKI. LASSO regression analysis selected 7characteristic variables to build 4 machine learning models，and finally selected the random forest model as the risk prediction model.The random forest model has an AUC value of 0.91，an accuracy of 0.89，an accuracy of 0.88，a recallrate of 0.98，and anF1 value of 0.93.The predictor importance ranking was in order of post-treatment creatinine level， C-reactive protein （CRP），albumin（Alb），respiratory failure，cardiac insuficiencytrough concentration time，and dose. Conclusion Post-treatment creatinine level， respiratory weaknes，trough concentration time，cardiac insufficiency，Alb，CRP，and dosage were the risk factors for VA-KI. The random forest model is the most effctive in predicting the risk of VA-AKI in elderly patients， providing a reference for rational use of vancomycin in elderly patients.

【Keywords】 Vancomycin; Acute kidney injury; Elderly patients; Risk factor; Machine learning

万古霉素是20世纪50年代从东方链霉菌中分离获得的糖肽类抗菌药物，主要用于治疗甲氧西林耐药葡萄球菌属、肠球菌属等革兰阳性菌所致感染。（剩余12168字）