Hu Jiuli, Hu Chanchan, Liang Yunwei, Wang Ziwei, Liang Yunfei, Ma Jiying, Wang Ying
Department of Pharmacy, The Affiliated Hospital of Chengde Medical University, Chengde, Hebei, PR China.
Department of Oncology, The Affiliated Hospital of Chengde Medical University, Chengde, Hebei, PR China.
Medicine (Baltimore). 2025 Mar 21;104(12):e41900. doi: 10.1097/MD.0000000000041900.
Lung cancer is the leading cause of death among patients with cancer. Medication nonadherence affects survival time and remission of disease symptoms in patients with lung cancer. Therefore, this study analyzed the risk factors for medication nonadherence in patients with lung cancer and established a nomogram prediction model. The basic information and clinical characteristics of patients with lung cancer were collected from the Affiliated Hospital of Chengde Medical University from April 2020 to March 2023. The Chinese version of the Morisky Medication Adherence Questionnaire-8 was used to evaluate patients' medication adherence. A least absolute shrinkage and selection operator regression model and multivariate logistic regression analysis were used to identify the risk factors for medication nonadherence and establish a nomogram prediction model. The predictive ability of the nomogram was evaluated using the concordance index (C-index) and the area under the operating characteristic curve. Decision curve analysis (DCA) and the clinical impact curve were used to assess the potential clinical value of the nomogram. A total of 161 patients with lung cancer were included in this study, with a medication nonadherence rate of 47.20%. Risk factors included age, surgery, education level, bone metastases, comorbidities, well-being, and constipation. The C-index and area under the operating characteristic curve were 0.946. The threshold probability for DCA ranged from 0.07 to 0.95. The nomogram can predict the risk of medication nonadherence in patients with lung cancer and help identify those at risk early in clinical settings, allowing for the development of intervention programs and improved clinical management.
肺癌是癌症患者死亡的主要原因。药物治疗不依从会影响肺癌患者的生存时间和疾病症状缓解情况。因此，本研究分析了肺癌患者药物治疗不依从的危险因素，并建立了列线图预测模型。2020年4月至2023年3月期间，从承德医学院附属医院收集肺癌患者的基本信息和临床特征。使用中文版的莫里isky药物治疗依从性问卷-8来评估患者的药物治疗依从性。采用最小绝对收缩和选择算子回归模型以及多因素逻辑回归分析来确定药物治疗不依从的危险因素，并建立列线图预测模型。使用一致性指数（C指数）和操作特征曲线下面积评估列线图的预测能力。决策曲线分析（DCA）和临床影响曲线用于评估列线图的潜在临床价值。本研究共纳入161例肺癌患者，药物治疗不依从率为47.20%。危险因素包括年龄、手术、教育程度、骨转移、合并症、幸福感和便秘。C指数和操作特征曲线下面积为0.946。DCA的阈值概率范围为0.07至0.95。该列线图可以预测肺癌患者药物治疗不依从的风险，并有助于在临床环境中早期识别有风险的患者，从而制定干预方案并改善临床管理。
