Ruff Carmen, Koukalova Ludmila, Haefeli Walter E, Meid Andreas D
Department of Clinical Pharmacology and Pharmacoepidemiology, University of Heidelberg, Heidelberg, Germany.
Front Pharmacol. 2019 Feb 19;10:113. doi: 10.3389/fphar.2019.00113. eCollection 2019.
Patients who do not sufficiently adhere to their dosing regimens will, ultimately, do not get the full benefit of their medication. For example, if direct oral anticoagulants (DOAC) are not taken continuously, an intervention to improve adherence or maintain persistence will show direct effects on clinical outcomes. Usually, adherent patients are defined by taking ≥80% of their medication. The resulting binary adherence status from this threshold can as well be used for predictive classification. Thus, the threshold can determine the prediction model's performance to identify patients at risk for poor adherence by this binary adherence status. In this perspective, we propose a plan for model development and performance considering the threshold's role. Concerning development demands, we extracted predictors from a systematic literature search on DOAC adherence to be used as a core set of candidate predictors. Independently, we investigated how well a future model would technically have to perform by modeling drug intake and thromboembolic events based on a rivaroxaban pharmacokinetic-pharmacodynamic model. Using this simulation framework for different thresholds, we projected the impact of an imperfectly predicted adherence status on the event risk, and how imperfect sensitivity and specificity affect the cost balance if a supporting intervention was offered to patients classified as non-adherent. Our simulation results suggest applying a rather high threshold (90%) for discrimination between patients at low or high risk for non-adherence by a prediction model in order to assure cost-efficient implementation.
未充分遵守给药方案的患者最终无法充分受益于其药物治疗。例如，如果直接口服抗凝剂（DOAC）未持续服用，改善依从性或维持持续性的干预措施将对临床结果产生直接影响。通常，依从性好的患者被定义为服用了≥80%的药物。由此阈值产生的二元依从性状态也可用于预测分类。因此，该阈值可以确定预测模型通过这种二元依从性状态识别依从性差风险患者的性能。从这个角度来看，我们提出了一个考虑阈值作用的模型开发和性能计划。关于开发要求，我们通过对DOAC依从性的系统文献检索提取预测因子，用作候选预测因子的核心集。独立地，我们通过基于利伐沙班药代动力学-药效学模型对药物摄入和血栓栓塞事件进行建模，研究了未来模型在技术上需要达到的性能水平。使用这个针对不同阈值的模拟框架，我们预测了预测的依从性状态不完美对事件风险的影响，以及如果对被分类为不依从的患者提供支持性干预，不完美的敏感性和特异性如何影响成本平衡。我们的模拟结果表明，为了确保成本效益高的实施，预测模型应采用相对较高的阈值（90%）来区分低或高不依从风险的患者。
