Applications of machine learning in decision analysis for dose management for dofetilide

English version

Autoři: Andrew E. Levy ^aff001; Minakshi Biswas ^aff001; Rachel Weber ^aff002; Khaldoun Tarakji ^aff003; Mina Chung ^aff003; Peter A. Noseworthy ^aff004; Christopher Newton-Cheh ^aff005; Michael A. Rosenberg ^aff001
Působiště autorů: Division of Cardiology, University of Colorado Anschutz Medical Campus, Aurora, CO, United States of America ^aff001; Division of Biostatistics and Informatics, Colorado School of Public Health, Aurora, CO, United States of America ^aff002; Center for Atrial Fibrillation, Section of Cardiac Pacing and Electrophysiology, Cleveland Clinic Foundation, Cleveland, OH, United States of America ^aff003; Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, MN, United States of America ^aff004; Cardiovascular Research Center, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States of America ^aff005; Colorado Center for Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, United States of America ^aff006
Vyšlo v časopise: PLoS ONE 14(12)
Kategorie: Research Article
prolekare.web.journal.doi_sk: https://doi.org/10.1371/journal.pone.0227324

Souhrn

Background

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.

Methods and results

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.

Conclusions

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.

Klíčová slova:

Cardiology – Electrocardiography – Coronary heart disease – Machine learning algorithms – Machine learning – Decision making – Data processing – Dose prediction methods

Zdroje

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