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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.

The Role of Adherence Thresholds for Development and Performance Aspects of a Prediction Model for Direct Oral Anticoagulation Adherence