Ni Congning, Song Qingyuan, Warner Jeremy L, Chen Qingxia, Song Lijun, Rosenbloom S Trent, Malin Bradley A, Yin Zhijun
Vanderbilt University, Nashville, TN, USA.
Brown University, Providence, RI, USA.
AMIA Annu Symp Proc. 2025 May 22;2024:865-874. eCollection 2024.
Medication persistence is essential for the efficacy of treatment and patient health outcomes. This study investigates the discontinuation of oral anticancer medications (capecitabine, ibrutinib, or sunitinib) in a cohort that is well-characterized by medication discontinuation survey questionnaires, prescription refill data, and structured electronic health records (EHRs). We categorized discontinuation reasons based on a survey of patients taking medication, revealing that 38% of 257 patients completed therapy, while discontinuation was due primarily to no response to therapy and/or progression of disease leading to discontinuation (33%) and side effects/complication (9%). Survival analysis showed variable medication persistence, with capecitabine persistence decreasing significantly over time, to 0.08 in two years. A logistic regression model demonstrated strong capability (with an AUC of 0.835) to identify patients at risk for medication discontinuation. Our study shows the complexities of medication persistence and emphasizes the importance of understanding medication discontinuation patterns and leveraging predictive analytics to inform future research and clinical monitoring in the treatment of cancer.
药物持续使用对于治疗效果和患者健康结局至关重要。本研究通过药物停用调查问卷、处方 refill 数据和结构化电子健康记录（EHR）对一个队列进行了充分特征描述，调查了口服抗癌药物（卡培他滨、伊布替尼或舒尼替尼）的停用情况。我们根据对正在服药患者的调查对停药原因进行了分类，结果显示，257 名患者中有 38%完成了治疗，而停药主要是由于对治疗无反应和/或疾病进展导致停药（33%）以及副作用/并发症（9%）。生存分析显示药物持续使用情况各不相同，卡培他滨的持续使用随时间显著下降，两年时降至 0.08。逻辑回归模型显示出强大的能力（AUC 为 0.835）来识别有药物停用风险的患者。我们的研究显示了药物持续使用的复杂性，并强调了了解药物停用模式以及利用预测分析为未来癌症治疗研究和临床监测提供信息的重要性。
