Abstract
Initiation of the antiarrhythmic medication dofetilide requires an FDA-mandated 3 days of telemetry monitoring due to heightened risk of toxicity within this time period. Although a recommended dose management algorithm for dofetilide exists, there is a range of real-world approaches to dosing the medication. In this multicenter investigation, clinical data from the Antiarrhythmic Drug Genetic (AADGEN) study was examined for 354 patients undergoing dofetilide initiation. Univariate logistic regression identified a starting dofetilide dose of 500 mcg (OR 5.0, 95%CI 2.5-10.0, p<0.001) and sinus rhythm at the start of dofetilide loading (OR 2.8, 95%CI 1.8-4.2, p<0.001) as strong positive predictors of successful loading. Any dose-adjustment during loading (OR 0.19, 95%CI 0.12-0.31, p<0.001) and a history coronary artery disease (OR 0.33, 95%CI 0.19-0.59, p<0.001) were strong negative predictors of successful dofetilide loading. Based on the observation that any dose adjustment was a significant negative predictor of successful initiation, we applied multiple supervised approaches to attempt to predict the dose adjustment decision, but none of these approaches identified dose adjustments better than a probabilistic guess. Principal component analysis and cluster analysis identified 8 clusters as a reasonable data reduction method. These 8 clusters were then used to define patient states in a tabular reinforcement learning model trained on 80% of dosing decisions. Testing of this model on the remaining 20% of dosing decisions revealed good accuracy of the reinforcement learning model, with only 16/410 (3.9%) instances of disagreement. Dose adjustments are a strong determinant of whether patients are able to successfully initiate dofetilide. A reinforcement learning algorithm informed by unsupervised learning was able to predict dosing decisions with 96.1% accuracy. Future studies will apply this algorithm prospectively as a data-driven decision aid.
Highlights
Initiation of the antiarrhythmic medication dofetilide requires an FDA-mandated 3 days of telemetry monitoring due to heightened risk of toxicity within this time period
Medical decisions can be formulated as Markov-decision processes (MDPs), in which a given state of conditions can predict future states based on a model for decision-making[2]
We examine the patterns of dofetilide dose adjustment and the role of machine learning to develop algorithms aimed at successful initiation of the medication
Summary
Clinical data from the Antiarrhythmic Drug Genetic (AADGEN) study was examined for 354 patients undergoing dofetilide initiation. Principal component analysis and cluster analysis identified 8 clusters as a reasonable data reduction method These 8 clusters were used to define patient states in a tabular reinforcement learning model trained on 80% of dosing decisions. The Antiarrhythmic Drug Genetic (AADGEN) study is a multi-center collaboration that includes investigators from the Massachusetts General Hospital (MGH, Boston, MA), Beth Israel Deaconess Medical Center (Boston, MA), the Boston-area Veterans Affairs Medical Center (West Roxbury, MA), the Cleveland Clinic (Cleveland, OH), the Mayo Clinic (Rochester, MN), and the University of Colorado Hospital (Aurora, CO). The exclusion criteria included failure to provide written informed consent and failure to obtain a pre-dofetilide ECG. This study is a sub-study of a larger investigation into the genetic predictors of cardiac repolarization and drug toxicity of antiarrhythmic medications (Clinicaltrials.gov identifier: NCT02439658)
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