Yang Xiaodan, Ye Qianqian, Zhang Mengxiang, Xu Yuewei, Yang Manqin
Department of Pharmacy, The second Affiliated Hospital of Anhui University of Traditional Chinese Medicine, Hefei, China.
Front Pharmacol. 2025 May 23;16:1565420. doi: 10.3389/fphar.2025.1565420. eCollection 2025.
To construct a risk prediction model for potentially inappropriate medications (PIM) in elderly stroke patients based on multiple machine-learning algorithms, providing decision support to identify high-risk patients and ensure rational clinical medication use.
A total of 1,252 discharged stroke patients from a tertiary hospital in Anhui Province, China, were included from January 2023 to December 2024. PIM was assessed using the American Geriatrics Society 2023 Updated Beers Criteria. Univariate analysis identified factors potentially associated with PIM, and the least absolute shrinkage and selection operator regression analysis was applied to select variables. The dataset was randomly split into training and internal validations sets in a 7:3 ratio. Additionally, a dataset independent of the training set in terms of time was selected, consisting of 240 stroke patients diagnosed at the same hospital from January to February 2025, to serve as an external validation cohort. Four machine-learning models, Random Forest, Elastic Net (Enet), Support Vector Machine Classifier, and Extreme Gradient Boosting were built using the meaningful variables identified after selection. The evaluation of machine-learning models was carried out through the discrimination, calibration, and clinical utility. SHapley Additive exPlanation (SHAP) values were utilized to rank the importance of features and to interpret the best-performing model.
Among 1,252 patients, 675 (53.91%) had PIM, with 107 types and 1,140 occurrences of PIM. Both in internal and external validation cohort, Enet performed the best. The area under the curve (AUC) of Receiver Operating Characteristic (ROC) curve of Enet in external validation set was 0.894 (0.854, 0.933). The model's calibration curve closely followed the ideal curve, and the clinical decision curve showed high net benefit within a threshold probability range of 15%-97%. The results indicate that the Enet prediction model exhibits good accuracy and generalizability, offering a basis for guiding clinical treatment.
The PIM risk prediction model developed using machine-learning can effectively identify PIM, aiding in the implementation of targeted interventions to prevent and reduce the risk of PIM in elderly stroke patients.
基于多种机器学习算法构建老年卒中患者潜在不适当用药（PIM）风险预测模型，为识别高危患者提供决策支持，确保临床合理用药。
纳入2023年1月至2024年12月中国安徽省某三级医院的1252例出院卒中患者。采用美国老年医学会2023年更新的《Beers标准》评估PIM。单因素分析确定与PIM潜在相关的因素，并应用最小绝对收缩和选择算子回归分析选择变量。数据集按7:3的比例随机分为训练集和内部验证集。此外，选取一个与训练集在时间上独立的数据集，该数据集由2025年1月至2月在同一家医院确诊的240例卒中患者组成，作为外部验证队列。使用选择后确定的有意义变量构建随机森林、弹性网络（Enet）、支持向量机分类器和极端梯度提升这四种机器学习模型。通过区分度、校准度和临床实用性对机器学习模型进行评估。利用SHapley值解释（SHAP）值对特征重要性进行排序，并解释表现最佳的模型。
1252例患者中，675例（53.91%）存在PIM，共107种类型，1140次PIM事件。在内部和外部验证队列中，Enet表现最佳。Enet在外部验证集中的受试者操作特征（ROC）曲线下面积（AUC）为0.894（0.854，0.933）。模型的校准曲线与理想曲线密切相关，临床决策曲线在15%-97%的阈值概率范围内显示出较高的净效益。结果表明，Enet预测模型具有良好的准确性和泛化能力，可为指导临床治疗提供依据。
利用机器学习开发的PIM风险预测模型能够有效识别PIM，有助于实施针对性干预措施，预防和降低老年卒中患者PIM风险。
