Department of Cardiology, The First Affiliated Hospital of the University of Science and Technology of China, Hefei, China.
PeerJ. 2023 Aug 9;11:e15876. doi: 10.7717/peerj.15876. eCollection 2023.
To investigate the incidence and influencing factors affecting the non-adherence behavior of patients with coronary heart disease (CHD) to antiplatelet therapy after discharge and to construct a personalized predictive tool.
In this retrospective cohort study, 289 patients with CHD who were admitted to the Department of Cardiology of The First Affiliated Hospital of the University of Science and Technology of China between June 2021 and September 2021 were enrolled. The clinical data of all patients were retrospectively collected from the hospital information system, and patients were followed up for 1 year after discharge to evaluate their adherence level to antiplatelet therapy, analyze their present situation and influencing factors for post-discharge adherence to antiplatelet therapy, and construct a nomogram model to predict the risk of non-adherence.
Based on the adherence level to antiplatelet therapy within 1 year after discharge, the patients were divided into the adherence ( = 216) and non-adherence ( = 73) groups. Univariate analysis revealed statistically significant differences between the two groups in terms of variable distribution, including age, education level, medical payment method, number of combined risk factors, percutaneous coronary intervention, duration of antiplatelet medication, types of drugs taken at discharge, and CHD type ( < 0.05). Furthermore, multivariate logistic regression analysis revealed that, except for the medical payment method, all the seven abovementioned variables were independent risk factors for non-adherence to antiplatelet therapy ( < 0.05). The areas under the receiver operating characteristic curve before and after the internal validation of the predictive tool based on the seven independent risk factors and the nomogram were 0.899 (95% confidence interval [CI]: 0.858-0.941) and 0.89 (95% CI: 0.847-0.933), respectively; this indicates that the tool has good discrimination ability. The calibration curve and Hosmer-Lemeshow goodness of fit test revealed that the tool exhibited good calibration and prediction consistency ( = 5.17, = 0.739).
In this retrospective cohort study, we investigated the incidence and influencing factors affecting the non-adherence behavior of patients with CHD after discharge to antiplatelet therapy. For this, we constructed a personalized predictive tool based on seven independent risk factors affecting non-adherence behavior. The predictive tool exhibited good discrimination ability, calibration, and clinical applicability. Overall, our constructed tool is useful for predicting the risk of non-adherence behavior to antiplatelet therapy in discharged patients with CHD and can be used in personalized intervention strategies to improve patient outcomes.
调查冠心病（CHD）患者出院后抗血小板治疗依从性的发生率及影响因素，并构建个性化预测工具。
本回顾性队列研究纳入 2021 年 6 月至 2021 年 9 月在中国科学技术大学第一附属医院心内科住院的 289 例 CHD 患者。从医院信息系统中回顾性收集所有患者的临床资料，对患者出院后进行为期 1 年的随访，以评估其抗血小板治疗的依从性，分析其出院后抗血小板治疗依从性的现状及影响因素，并构建预测非依从性风险的列线图模型。
根据出院后 1 年内抗血小板治疗的依从性，将患者分为依从组（n=216）和不依从组（n=73）。单因素分析显示，两组在年龄、文化程度、医疗付费方式、合并危险因素数量、经皮冠状动脉介入治疗、抗血小板药物治疗时间、出院时用药种类、CHD 类型等方面的变量分布存在统计学差异（<0.05）。进一步的多因素 logistic 回归分析显示，除医疗付费方式外，上述 7 个变量均为抗血小板治疗不依从的独立危险因素（<0.05）。基于 7 个独立危险因素和列线图的预测工具的内部验证前后的受试者工作特征曲线下面积分别为 0.899（95%置信区间[CI]：0.8580.941）和 0.89（95% CI：0.8470.933），表明该工具具有良好的区分能力。校准曲线和 Hosmer-Lemeshow 拟合优度检验表明，该工具具有良好的校准和预测一致性（=5.17，=0.739）。
本回顾性队列研究调查了冠心病患者出院后抗血小板治疗依从性的发生率及影响因素，并构建了基于 7 个影响非依从性行为的独立危险因素的个性化预测工具。该预测工具具有良好的区分能力、校准能力和临床适用性。总之，我们构建的工具可用于预测出院后冠心病患者抗血小板治疗的不依从风险，并可用于制定个性化的干预策略，以改善患者结局。
