Franklin Jessica M, Krumme Alexis A, Tong Angela Y, Shrank William H, Matlin Olga S, Brennan Troyen A, Choudhry Niteesh K
Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
CVS Caremark, Woonsocket, RI, USA.
Pharmacoepidemiol Drug Saf. 2015 Oct;24(10):1105-13. doi: 10.1002/pds.3787. Epub 2015 Apr 22.
Trajectory models have been shown to (1) identify groups of patients with similar patterns of medication filling behavior and (2) summarize the trajectory, the average adherence in each group over time. However, the association between adherence trajectories and clinical outcomes remains unclear. This study investigated the association between 12-month statin trajectories and subsequent cardiovascular events.
We identified patients with insurance coverage from a large national insurer who initiated a statin during January 1, 2007 to December 31, 2010. We assessed medication adherence during the 360 days following initiation and grouped patients based on the proportion of days covered (PDC) and trajectory models. We then measured cardiovascular events during the year after adherence assessment. Cox proportional hazards models were used to evaluate the association between adherence measures and cardiovascular outcomes; strength of association was quantified by the hazard ratio, the increase in model C-statistic, and the net reclassification index (NRI).
Among 519 842 statin initiators, 8777 (1.7%) had a cardiovascular event during follow-up. More consistent medication use was associated with a lower likelihood of clinical events, whether adherence was measured through trajectory groups or PDC. When evaluating the prediction of future cardiovascular events by including a measure of adherence in the model, the best model reclassification was observed when adherence was measured using three or four trajectory groups (NRI = 0.189; 95% confidence interval: [0.171, 0.210]).
Statin adherence trajectory predicted future cardiovascular events better than measures categorizing PDC. Thus, adherence trajectories may be useful for targeting adherence interventions.
轨迹模型已被证明能够(1)识别具有相似药物填充行为模式的患者群体,以及(2)总结轨迹,即每组患者随时间的平均依从性。然而,依从性轨迹与临床结局之间的关联仍不明确。本研究调查了12个月他汀类药物轨迹与随后心血管事件之间的关联。
我们从一家大型全国性保险公司中识别出在2007年1月1日至2010年12月31日期间开始服用他汀类药物的参保患者。我们评估了开始用药后360天内的药物依从性,并根据覆盖天数比例(PDC)和轨迹模型对患者进行分组。然后,我们在依从性评估后的一年中测量心血管事件。使用Cox比例风险模型评估依从性测量与心血管结局之间的关联;关联强度通过风险比、模型C统计量的增加以及净重新分类指数(NRI)进行量化。
在519842名他汀类药物起始者中,8777名(1.7%)在随访期间发生了心血管事件。无论通过轨迹组还是PDC来衡量依从性,更持续的药物使用都与较低的临床事件发生可能性相关。在模型中纳入依从性测量指标来评估未来心血管事件的预测时,使用三个或四个轨迹组测量依从性时观察到最佳的模型重新分类(NRI = 0.189;95%置信区间:[0.171, 0.210])。
他汀类药物依从性轨迹比PDC分类指标能更好地预测未来心血管事件。因此,依从性轨迹可能有助于确定依从性干预的目标。