Asset Details
Do you wish to reserve the book?
Prediction of long-term adherence to direct oral anti-coagulants in patients with atrial fibrillation using first-order Markov models
Hey, we have placed the reservation for you!
By the way, why not check out events that you can attend while you pick your title.
Oops! Something went wrong.
Looks like we were not able to place the reservation. Kindly try again later.
Are you sure you want to remove the book from the shelf?
Prediction of long-term adherence to direct oral anti-coagulants in patients with atrial fibrillation using first-order Markov models
Do you wish to request the book?
Prediction of long-term adherence to direct oral anti-coagulants in patients with atrial fibrillation using first-order Markov models
Please be aware that the book you have requested cannot be checked out. If you would like to checkout this book, you can reserve another copy
We have requested the book for you!
Your request is successful and it will be processed during the Library working hours. Please check the status of your request in My Requests.
Oops! Something went wrong.
Looks like we were not able to place your request. Kindly try again later.
Journal Article
Prediction of long-term adherence to direct oral anti-coagulants in patients with atrial fibrillation using first-order Markov models
2025
Overview
Direct Oral Anti-Coagulants (DOACs) are the primary treatment for the long-term prevention of stroke in patients with atrial fibrillation. Strict adherence to DOAC therapy is crucial and must be maintained over the long term. Therefore, predicting long-term adherence is valuable for identifying patients at risk of non-adherence. We developed a novel method for predicting long-term adherence using first-order Markov models to assess adherence in new DOAC users during years 2–5. The prediction utilized age, CHA2DS2-VASc score, and first-year adherence data as predictors. Adherence was measured by calculating the proportion of days covered within consecutive 90-day windows, which were then stratified into deciles. We subsequently calculated the probability of a patient being in a specific adherence decile. The developed model demonstrated good calibration. We discovered that missing even 1 day of treatment per month in the first year was predictive of a lower likelihood of achieving the highest adherence decile in years 2–5. Additionally, we noted a non-linear relationship between age and adherence; adherence increased linearly with age but plateaued around age 75. This innovative approach to modelling and predicting adherence to DOACs for long-term therapy can help identify patients at risk of low adherence and may be applicable to other chronic medications.
Publisher
Frontiers Media SA,Frontiers Media S.A
Subject
