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电子监测的药物依从性可预测心力衰竭患者的住院情况。

Electronically monitored medication adherence predicts hospitalization in heart failure patients.

作者信息

Riegel Barbara, Knafl George J

机构信息

University of Pennsylvania School of Nursing, Philadelphia, PA, USA ; University of Pennsylvania Leonard Davis Institute, Philadelphia, PA, USA.

University of North Carolina School of Nursing, Chapel Hill, NC, USA.

出版信息

Patient Prefer Adherence. 2013 Dec 5;8:1-13. doi: 10.2147/PPA.S54520. eCollection 2013.

Abstract

BACKGROUND

Hospitalization contributes enormously to health care costs associated with heart failure. Many investigators have attempted to predict hospitalization in these patients. None of these models has been highly effective in prediction, suggesting that important risk factors remain unidentified.

PURPOSE

To assess prospectively collected medication adherence, objectively measured by the Medication Event Monitoring System, as a predictor of hospitalization in heart failure patients.

MATERIALS AND METHODS

We used recently developed adaptive modeling methods to describe patterns of medication adherence in a sample of heart failure patients, and tested the hypothesis that poor medication adherence as determined by adaptive methods was a significant predictor of hospitalization within 6 months.

RESULTS

Medication adherence was the best predictor of hospitalization. Besides two dimensions of poor adherence (adherence pattern type and low percentage of prescribed doses taken), four other single factors predicted hospitalization: low hemoglobin, depressed ejection fraction, New York Heart Association class IV, and 12 or more medications taken daily. Seven interactions increased the predictive capability of the model: 1) pattern of poor adherence type and lower score on the Letter-Number Sequencing test, a measure of short-term memory; 2) higher number of comorbid conditions and higher number of daily medications; 3) higher blood urea nitrogen and lower percentage of prescribed doses taken; 4) lower hemoglobin and much worse perceived health compared to last year; 5) older age and lower score on the Telephone Interview of Cognitive Status; 6) higher body mass index and lower hemoglobin; and 7) lower ejection fraction and higher fatigue. Patients with none of these seven interactions had a hospitalization rate of 9.7%. For those with five of these interaction risk factors, 100% were hospitalized. The C-index (the area under the receiver-operating characteristics [ROC] curve) for the model based on the seven interactions was 0.83, indicating excellent discrimination.

CONCLUSION

Medication adherence adds important new information to the list of variables previously shown to predict hospitalization in adults with heart failure.

摘要

背景

住院治疗在与心力衰竭相关的医疗费用中占比极大。许多研究人员试图预测这些患者的住院情况。但这些模型中没有一个在预测方面非常有效,这表明仍有重要的风险因素未被识别。

目的

前瞻性评估通过药物事件监测系统客观测量的药物依从性,作为心力衰竭患者住院的预测指标。

材料与方法

我们使用最近开发的自适应建模方法来描述心力衰竭患者样本中的药物依从性模式,并检验以下假设:通过自适应方法确定的药物依从性差是6个月内住院的重要预测指标。

结果

药物依从性是住院的最佳预测指标。除了依从性差的两个维度(依从性模式类型和所服用规定剂量的低百分比)外,其他四个单一因素也可预测住院情况:血红蛋白水平低、射血分数降低、纽约心脏病协会IV级以及每日服用12种或更多药物。七种相互作用增强了模型的预测能力:1)依从性差的模式类型与字母数字排序测试得分较低(一种短期记忆测量方法);2)共病状况数量较多和每日服用药物数量较多;3)血尿素氮水平较高和所服用规定剂量的百分比较低;4)血红蛋白水平较低且与去年相比自我感觉健康状况差得多;5)年龄较大且认知状态电话访谈得分较低;6)体重指数较高且血红蛋白水平较低;7)射血分数较低且疲劳程度较高。没有这七种相互作用的患者住院率为9.7%。对于有五种这些相互作用风险因素的患者,100%都住院了。基于这七种相互作用的模型的C指数(受试者操作特征[ROC]曲线下面积)为0.83,表明具有出色的区分能力。

结论

药物依从性为先前已证明可预测成年心力衰竭患者住院情况的变量列表增添了重要的新信息。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c472/3862652/2a4932efbdb7/ppa-8-001Fig1.jpg

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