Prediction of Linezolid-induced Thrombocytopenia Based on Machine Learning Algorithm
10.3870/j.issn.1004-0781.2025.04.028
- VernacularTitle:基于机器学习算法的利奈唑胺相关血小板减少预测
- Author:
Ru LIAO
1
;
Yi CUI
;
Xiaoliang CHENG
;
Feng WANG
;
Houli LI
;
Haiyan DONG
Author Information
1. 西安交通大学第一附属医院药学部,西安 710061
- Publication Type:Journal Article
- Keywords:
Linezolid;
Thrombocytopenia;
Machine learning;
Decision tree;
Random forest
- From:
Herald of Medicine
2025;44(4):676-681
- CountryChina
- Language:Chinese
-
Abstract:
Objective To construct machine learning models to predict the incidence of linezolid-induced thrombocytopenia(LIT).Methods A total of 198 patients treated with linezolid in a hospital between January 2020 and March 2024 were retrospectively included.Firstly,the patients were divided into LIT and non-LIT groups,and the basic characteristics of the two groups were compared.Then,the variables with significant differences between the two groups were selected as potential risk factors to construct models for predicting LIT,including Logistic regression,decision tree and random forest models,and the prediction performance of the models was evaluated and compared.Results There were 52(26.3％)patients developed LIT during the treatment.The univariate analysis showed significant differences in linezolid trough concentration(Cmin),baseline platelet counts and creatinine clearance,the incidence of cerebrovascular disease,acute respiratory distress syndrome,and abdominal infection in patients with and without LIT.Among the three models built based on these variables,the random forest model has the best predictive performance.The results of variable importance analysis based on random forest model showed that Cmin,baseline platelet count and combined with acute respiratory distress syndrome had higher importance scores.Conclusions The random forest model has high accuracy in predicting the occurrence of LIT,and the risk of LIT is higher in patients with higher levels of linezolid exposure and lower baseline platelets.
