Steiner John F, Ho P Michael, Beaty Brenda L, Dickinson L Miriam, Hanratty Rebecca, Zeng Chan, Tavel Heather M, Havranek Edward P, Davidson Arthur J, Magid David J, Estacio Raymond O
Colorado Health Outcomes Program, the Department of Family Medicine, University of Colorado, Denver, CO, USA.
Circ Cardiovasc Qual Outcomes. 2009 Sep;2(5):451-7. doi: 10.1161/CIRCOUTCOMES.108.841635. Epub 2009 Aug 11.
Although many studies have identified patient characteristics or chronic diseases associated with medication adherence, the clinical utility of such predictors has rarely been assessed. We attempted to develop clinical prediction rules for adherence with antihypertensive medications in 2 healthcare delivery systems.
We performed retrospective cohort studies of hypertension registries in an inner-city healthcare delivery system (n=17 176) and a health maintenance organization (n=94 297) in Denver, Colo. Adherence was defined by acquisition of 80% or more of antihypertensive medications. A multivariable model in the inner-city system found that adherent patients (36.3% of the total) were more likely than nonadherent patients to be older, white, married, and acculturated in US society, to have diabetes or cerebrovascular disease, not to abuse alcohol or controlled substances, and to be prescribed fewer than 3 antihypertensive medications. Although statistically significant, all multivariate odds ratios were 1.7 or less, and the model did not accurately discriminate adherent from nonadherent patients (C statistic=0.606). In the health maintenance organization, where 72.1% of patients were adherent, significant but weak associations existed between adherence and older age, white race, the lack of alcohol abuse, and fewer antihypertensive medications. The multivariate model again failed to accurately discriminate adherent from nonadherent individuals (C statistic=0.576).
Although certain sociodemographic characteristics or clinical diagnoses are statistically associated with adherence to refills of antihypertensive medications, a combination of these characteristics is not sufficiently accurate to allow clinicians to predict whether their patients will be adherent with treatment.
尽管许多研究已确定了与药物依从性相关的患者特征或慢性病,但此类预测指标的临床实用性很少得到评估。我们试图在两个医疗服务系统中制定抗高血压药物依从性的临床预测规则。
我们对科罗拉多州丹佛市一个市中心医疗服务系统(n = 17176)和一个健康维护组织(n = 94297)的高血压登记处进行了回顾性队列研究。依从性定义为获取80%或更多的抗高血压药物。市中心系统的多变量模型发现,依从性患者(占总数的36.3%)比不依从性患者更有可能年龄较大、为白人、已婚且融入美国社会,患有糖尿病或脑血管疾病,不滥用酒精或管制药物,且开具的抗高血压药物少于3种。尽管具有统计学意义,但所有多变量比值比均为1.7或更低,且该模型未能准确区分依从性患者和不依从性患者(C统计量 = 0.606)。在健康维护组织中,72.1%的患者依从,依从性与年龄较大、白人种族、不滥用酒精以及抗高血压药物较少之间存在显著但较弱的关联。多变量模型再次未能准确区分依从性个体和不依从性个体(C统计量 = 0.576)。
尽管某些社会人口统计学特征或临床诊断与抗高血压药物续方的依从性在统计学上相关,但这些特征的组合不够准确,无法让临床医生预测其患者是否会坚持治疗。