{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,17]],"date-time":"2025-10-17T00:13:56Z","timestamp":1760660036807,"version":"build-2065373602"},"reference-count":25,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,10,16]],"date-time":"2025-10-16T00:00:00Z","timestamp":1760572800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Pharmacol."],"abstract":"<jats:p>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\u20135. 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\u00a0day of treatment per month in the first year was predictive of a lower likelihood of achieving the highest adherence decile in years 2\u20135. 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.<\/jats:p>","DOI":"10.3389\/fphar.2025.1673919","type":"journal-article","created":{"date-parts":[[2025,10,16]],"date-time":"2025-10-16T08:27:13Z","timestamp":1760603233000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Prediction of long-term adherence to direct oral anti-coagulants in patients with atrial fibrillation using first-order Markov models"],"prefix":"10.3389","volume":"16","author":[{"given":"Elias Edward","family":"Tannous","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shlomo","family":"Selitzky","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shlomo","family":"Vinker","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nicola","family":"Toukan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"David","family":"Stepensky","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Eyal","family":"Schwarzberg","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1965","published-online":{"date-parts":[[2025,10,16]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"1138","DOI":"10.18553\/jmcp.2020.26.9.1138","article-title":"Medication adherence trajectories: a systematic literature review","volume":"26","author":"Alhazami","year":"2020","journal-title":"JMCP"},{"key":"B2","doi-asserted-by":"publisher","first-page":"e021601","DOI":"10.1161\/JAHA.121.021601","article-title":"Long-term medication adherence trajectories to direct oral anticoagulants and clinical outcomes in patients with atrial fibrillation","volume":"10","author":"An","year":"2021","journal-title":"J. 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