Zhao Xian, Peng Qin, Hu Dongmei, Li Weiwei, Ji Qing, Dong Qianqian, Huang Luguang, Piao Miyang, Ding Yi, Wang Jingwen
Department of Pharmacy, First Affiliated Hospital of Air Force Medical University, Xi'an, Shaanxi, China.
Department of Hepatobiliary Surgery, First Affiliated Hospital of Air Force Medical University, Xi'an, Shaanxi, China.
Front Pharmacol. 2024 Feb 21;15:1292828. doi: 10.3389/fphar.2024.1292828. eCollection 2024.
Based on real-world medical data, the artificial neural network model was used to predict the risk factors of linezolid-induced thrombocytopenia to provide a reference for better clinical use of this drug and achieve the timely prevention of adverse reactions. The artificial neural network algorithm was used to construct the prediction model of the risk factors of linezolid-induced thrombocytopenia and further evaluate the effectiveness of the artificial neural network model compared with the traditional Logistic regression model. A total of 1,837 patients receiving linezolid treatment in a hospital in Xi 'an, Shaanxi Province from 1 January 2011 to 1 January 2021 were recruited. According to the exclusion criteria, 1,273 cases that did not meet the requirements of the study were excluded. A total of 564 valid cases were included in the study, with 89 (15.78%) having thrombocytopenia. The prediction accuracy of the artificial neural network model was 96.32%, and the AUROC was 0.944, which was significantly higher than that of the Logistic regression model, which was 86.14%, and the AUROC was 0.796. In the artificial neural network model, urea, platelet baseline value and serum albumin were among the top three important risk factors. The predictive performance of the artificial neural network model is better than that of the traditional Logistic regression model, and it can well predict the risk factors of linezolid-induced thrombocytopenia.
基于真实世界医学数据，采用人工神经网络模型预测利奈唑胺所致血小板减少症的危险因素，为该药物更好地临床应用提供参考，实现不良反应的及时预防。采用人工神经网络算法构建利奈唑胺所致血小板减少症危险因素的预测模型，并与传统Logistic回归模型比较，进一步评估人工神经网络模型的有效性。选取2011年1月1日至2021年1月1日在陕西省西安市某医院接受利奈唑胺治疗的1837例患者。根据排除标准，排除1273例不符合研究要求的病例。共纳入564例有效病例，其中89例（15.78%）发生血小板减少症。人工神经网络模型的预测准确率为96.32%，曲线下面积（AUROC）为0.944，显著高于Logistic回归模型的86.14%和0.796。在人工神经网络模型中，尿素、血小板基线值和血清白蛋白是最重要的前三位危险因素。人工神经网络模型的预测性能优于传统Logistic回归模型，能够很好地预测利奈唑胺所致血小板减少症的危险因素。
