中国药物警戒 ›› 2021, Vol. 18 ›› Issue (11): 1070-1074.
DOI: 10.19803/j.1672-8629.2021.11.16

• 安全与合理用药 • 上一篇    下一篇

高脂血症患者他汀类用药依从性影响因素探讨及风险预测

陈捷1, 吴磊1, 吴杨霞1, 潘敉1, 段自皞1,*, 朱枝祥2   

  1. 1安徽医科大学附属六安医院,六安市人民医院药学部,安徽 六安 237005;
    2北京中医药大学中药现代研究中心,北京 100029
  • 收稿日期:2020-02-07 发布日期:2021-11-18
  • 通讯作者: *段自皞,男,硕士,主管药师,临床药学。E-mail：duanzihao1985@126.com
  • 作者简介:陈捷,男,本科,副主任药师,医院管理。
  • 基金资助:
    国家自然科学基金资助项目（81603361）

Compliance of Patients with Hyperlipidemia in Taking Statins and Risk Prediction

CHEN Jie1, WU Lei1, WU Yangxia1, PAN Mi1, DUAN Zihao1,*, ZHU Zhixiang2   

  1. 1Department of Pharmacy, The Lu'an Afiliated Hospital of Anhui Medical University and Lu'an People's Hospital, Lu'an Anhui 237005, China;
    2Modern Research Center for Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing 100029, China
  • Received:2020-02-07 Published:2021-11-18

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摘要： 目的 探讨高脂血症患者使用他汀类药物降脂治疗时用药依从性的影响因素,并建立Nomogram模型对依从性差的风险进行预测。方法 选取2018年1月1日至2019年9月30日在我院就诊的160例高脂血症患者,根据药物依从性问卷调查结果分为依从性好组（84例）和依从性差组（76例）,并采用logistic回归分析筛选其独立危险因素。根据筛选出的独立危险因素建立列线图预测模型,并对模型的预测性及准确度进行验证。结果 通过对2组患者一般临床资料及相关并发症资料做logistic回归分析可知,年龄（OR=6.517,95% CI ：1.986~21.39）、居住方式（OR=0.130,95% CI ：0.039~0.429）、医疗费用（OR=0.162,95% CI ：0.058~0.452）、医患关系（OR=15.017,95% CI ：4.991~45.181）、医护人员随访（OR=7.889,95% CI ：2.668~23.323）、饮食习惯（OR= 5.187,95% CI：1.839~14.634）、他汀类药品不良反应（OR= 0.316,95% CI：0.110~0.904）为高脂血症患者用药依从性差的独立危险因素,具有统计学差异（P<0.05）。根据筛选出的独立危险因素,建立了预测高脂血症患者用药依从性差的列线图模型,经验证,该模型预测值与实测值基本一致,预测能力较好。同时用Bootstrap 内部验证法验证该模型,C-index指数高达0.934（95% CI：0.896~0.972）,表明该模型精准度、区分度良好。结论 根据年龄、居住方式、医疗费用、医患关系、医护人员随访、饮食习惯、他汀类药物不良反应及合并其他疾病等因素综合评估高脂血症患者用药依从性差的发生率,能够提前预测高脂血症患者是否发生用药依从性差,有重要临床意义。

关键词: 高脂血症, 他汀类药物, 用药依从性, 列线图, 风险模型

Abstract: Objectiv eTo explore the influencing factors of drug compliance in hyperlipidemia patients treated with statins, and to establish a nomogram model to predict the risk of poor compliance. Methods One hundred and sixty patients with hyperlipidemia treated in our hospital were selected between January 2018 and September 2019. These patients were divided into the good compliance group (84 cases) and poor compliance group (76 cases) according to the results of a questionnaire survey. Logistic regression analysis was used to screen independent risk factors. A linear prediction model was established by including the selected independent risk factors before the predictability and accuracy of the model were verified. Results According to logistic regression analysis of the general clinical data and related complications of the two groups, age (OR= 6.517, 95% CI: 1.986~21.39), lifestyles (OR=0.130, 95% CI: 0.039~0.429), medical expenses (OR=0.162, 95% CI: 0.058~0.452), doctor-patient relationships (OR=15.017, 95% CI: 4.991~45.181), follow-up (OR=7.889, 95% CI: 2.668~23.323), dietary habits (OR=5.187, 95% CI: 1.839~14.634) and adverse reactions of statins (OR=0.316, 95% CI: 0.110~0.904) were independent risk factors for poor drug compliance of patients with hyperlipidemia. The predictability of this model was basically consistent with the measured value. In addition, this model was verified by the Bootstrap internal verification method. The C-index was as high as 0.934（95% CI: 0.896-0.972）, indicating the precision of the model. Conclusion It is of great clinical significance to evaluate the incidence of poor drug compliance in hyperlipidemia patients by taking into consideration age, lifestyles, medical expenses, doctor-patient relationships, follow-up, eating habits, adverse reactions of statins and other diseases.

Key words: hyperlipidemia, statins, medication compliance, nomogram, risk model

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