Division of Population Health Sciences, Royal College of Surgeons in Ireland, Dublin, Ireland.
Clinical Research Centre, Beaumont Hospital, Dublin, Ireland.
PLoS One. 2018 Apr 20;13(4):e0195663. doi: 10.1371/journal.pone.0195663. eCollection 2018.
We derive a novel model-based metric for effective adherence to medication, and validate it using data from the INhaler Compliance Assessment device (INCATM). This technique employs dose timing data to estimate the threshold drug concentration needed to maintain optimal health.
The parameters of the model are optimised against patient outcome data using maximum likelihood methods. The model is fitted and validated by secondary analysis of two independent datasets from two remote-monitoring studies of adherence, conducted through clinical research centres of 5 Irish hospitals. Training data came from a cohort of asthma patients (~ 47,000 samples from 218 patients). Validation data is from a cohort of 204 patients with COPD recorded between 2014 and 2016.
The time above threshold measure is strongly predictive of adverse events (exacerbations) in COPD patients (Odds Ratio of exacerbation = 0.52 per SD increase in adherence, 95% Confidence Interval [0.34-0.79]). This compares well with the best known previous method, the Area Under the dose-time Curve (AUC) (Odds Ratio = 0.69, 95% Confidence Interval [0.48-0.99]). In addition, the fitted value of the dose threshold (0.56 of prescribed dosage) suggests that prescribed doses may be unnecessarily high given good adherence.
The resulting metric accounts for missed doses, dose-timing errors, and errors in inhaler technique, and provides enhanced predictive validity in comparison to previously used measures. In addition, the method allows us to estimate the correct dosage required to achieve the effect of the medication using the patients' own adherence data and outcomes. The adherence score does depend not on sex or other demographic factors suggesting that effective adherence is driven by individual behavioural factors.
我们提出了一种新的基于模型的药物依从性度量方法，并使用 INhaler Compliance Assessment 设备（INCATM）的数据对其进行验证。该技术利用剂量时间数据来估计维持最佳健康所需的药物临界浓度。
使用最大似然方法对患者结局数据进行模型参数优化。通过对两个远程监测研究的两个独立数据集进行二次分析，对模型进行拟合和验证。这些研究是在爱尔兰 5 家医院的临床研究中心进行的，涉及依从性的研究。训练数据来自哮喘患者队列（来自 218 名患者的约 47000 个样本）。验证数据来自 COPD 患者队列（2014 年至 2016 年期间记录的 204 名患者）。
时间超过阈值的测量值与 COPD 患者的不良事件（加重）具有很强的预测关系（依从性每增加一个标准差，加重的几率比为 0.52，95%置信区间[0.34-0.79]）。这与以前最好的方法——剂量时间曲线下面积（AUC）（几率比=0.69，95%置信区间[0.48-0.99]）相当。此外，所拟合的剂量阈值（规定剂量的 0.56）表明，在良好依从性的情况下，规定剂量可能过高。
该度量方法考虑了漏服、剂量时间错误和吸入器技术错误，并与以前使用的方法相比提供了更好的预测有效性。此外，该方法还允许我们根据患者自己的依从性数据和结果来估计达到药物效果所需的正确剂量。依从性评分并不依赖于性别或其他人口统计学因素，这表明有效的依从性是由个体行为因素驱动的。
