Department of Ophthalmology and Visual Sciences, W.K. Kellogg Eye Center, University of Michigan, Ann Arbor, Michigan.
Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan.
Ophthalmol Glaucoma. 2022 Mar-Apr;5(2):137-145. doi: 10.1016/j.ogla.2021.07.012. Epub 2021 Aug 4.
To assess the accuracy of 5 subjective self-assessment tools (3 adherence measures and 2 psychometric scales) and pharmacy refill data in predicting objective electronically monitored nonadherence.
Prospective cohort study.
Glaucoma patients (> 40 years old, >1 medication with poor self-reported adherence) were recruited from University of Michigan Kellogg Eye Center for a study assessing the impact of a personalized glaucoma coaching program on medication adherence.
Participants completed an initial assessment including 5 self-assessment tools and a 3-month period of electronic monitoring of glaucoma medication adherence (AdhereTech); pharmacy refill data were obtained. Electronically monitored adherence was calculated monthly as the percentage of doses taken on time. The median of these adherence rates was designated as baseline adherence. Patients with adherence of ≤80% by electronic monitoring were considered nonadherent. Self-assessment tools were scored, and pharmacy refill data were summarized as the proportion of days covered. Correlation between measures of adherence was estimated with Pearson and Spearman correlation coefficients. Receiver operating characteristic curves, including estimation of area under the curve (AUC), sensitivity, specificity, and accuracy were used to compare measures of adherence with respect to predicting electronically monitored nonadherence.
Accuracy of self-reported and pharmacy refill data adherence measures in predicting electronically monitored nonadherence.
Ninety-five patients completed 3 months of electronic monitoring with a median monthly adherence of 74 (± 21%); 53 patients (56%) were nonadherent. Pharmacy refill data were not correlated significantly with electronically monitored medication adherence (r = 0.12; P = 0.2). Of all the measures, a single-item adherence question ("Over the past month, what percentage of your drops do you think you took correctly?") showed the largest correlation with median electronically monitored adherence (r = 0.47; P < 0.0001), largest AUC for predicting nonadherence (AUC = 0.76 [95% confidence interval [CI], 0.66-0.87]), best accuracy (71% [95% CI, 61%-82%]), and good sensitivity (84% [95% CI, 73%-96%]).
The single-item question was the most accurate in predicting electronically monitored nonadherence among participants with poor self-reported adherence. In clinical practice, where alternatives are too resource intensive, this free single-item screening question can help to identify glaucoma patients at risk of poor medication adherence with reasonable accuracy.
评估 5 种主观自我评估工具（3 种依从性测量工具和 2 种心理计量尺度）和药房补充数据在预测客观电子监测不依从方面的准确性。
前瞻性队列研究。
密歇根大学凯洛格眼科中心招募了年龄>40 岁、>1 种用药且自我报告依从性差的青光眼患者，参与一项评估个性化青光眼辅导计划对药物依从性影响的研究。
参与者完成了初始评估，包括 5 种自我评估工具和为期 3 个月的电子监测青光眼药物依从性（AdhereTech）；获得了药房补充数据。电子监测依从性每月以按时服用的剂量百分比计算。这些依从率的中位数被指定为基线依从率。电子监测依从率≤80%的患者被认为是不依从的。自我评估工具的评分和药房补充数据的总结为覆盖率天数的比例。采用 Pearson 和 Spearman 相关系数估计依从性测量之间的相关性。接收者操作特征曲线，包括曲线下面积（AUC）估计、敏感性、特异性和准确性，用于比较自我报告和药房补充数据的依从性测量在预测电子监测不依从方面的准确性。
自我报告和药房补充数据的依从性测量在预测电子监测不依从方面的准确性。
95 名患者完成了 3 个月的电子监测，中位每月依从率为 74（±21%）；53 名患者（56%）不依从。药房补充数据与电子监测药物依从性无显著相关性（r=0.12；P=0.2）。在所有测量中，一个单一项目的依从性问题（“在过去的一个月里，你认为你正确服用了多少滴眼药水？”）与中位电子监测依从性相关性最大（r=0.47；P<0.0001），对不依从性的预测 AUC 最大（AUC=0.76 [95%置信区间（CI），0.66-0.87]），准确率最高（71%[95%CI，61%-82%]），灵敏度较好（84%[95%CI，73%-96%]）。
在自我报告依从性较差的参与者中，单一问题是预测电子监测不依从的最准确方法。在临床实践中，如果替代方案资源过于密集，这种免费的单一项目筛选问题可以帮助以合理的准确性识别出药物依从性差的青光眼患者。
