{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,17]],"date-time":"2026-06-17T03:01:14Z","timestamp":1781665274211,"version":"3.54.5"},"reference-count":41,"publisher":"Ovid Technologies (Wolters Kluwer Health)","issue":"21","content-domain":{"domain":["www.ahajournals.org"],"crossmark-restriction":true},"short-container-title":["JAHA"],"published-print":{"date-parts":[[2025,11,4]]},"abstract":"<jats:sec xml:lang=\"en\">\n                    <jats:title>Background<\/jats:title>\n                    <jats:p xml:lang=\"en\">Current tools for assessing ischemic stroke and bleeding risks in patients with atrial fibrillation focus on warfarin users and initial therapy decisions; however, dynamic models for reassessing these risks in direct oral anticoagulant (DOAC) users are lacking. We aimed to develop a tool that enables the dynamic evaluation of thromboembolic and bleeding risks in patients with atrial fibrillation receiving DOAC therapy.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec xml:lang=\"en\">\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p xml:lang=\"en\">Using a national claims database, we included 42\u2009450 patients with atrial fibrillation who were newly prescribed DOACs and divided them into development (2018) and temporal\u2010validation (2019) cohorts. We applied a landmarking approach and developed dynamic prediction models incorporating time\u2010dependent variables. The Fine\u2013Gray subdistribution hazard model, which accounts for death as a competing risk, was used to derive these models.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec xml:lang=\"en\">\n                    <jats:title>Results<\/jats:title>\n                    <jats:p xml:lang=\"en\">\n                      Dynamic prediction models incorporating time\u2010dependent variables, such as DOAC adherence, were developed to estimate the 1\u2010year thromboembolic and major bleeding risks over a 2\u2010year period following DOAC initiation. The thromboembolic and major bleeding risk prediction models achieved a concordance index of 0.715 (95% CI, 0.701\u20130.729), and 0.697 (95% CI, 0.682\u20130.710), respectively. The thromboembolic model outperformed CHA\n                      <jats:sub>2<\/jats:sub>\n                      DS\n                      <jats:sub>2<\/jats:sub>\n                      \u2010VASc (concordance index, 0.664 [95% CI, 0.652\u20130.678]) by 0.051 (95% CI, 0.038\u20130.062), and the major bleeding model outperformed HAS\u2010BLED (concordance index, 0.627 [95% CI, 0.611\u20130.641]) by 0.070 (95% CI, 0.055\u20130.084). Calibration plots showed good alignment between the predicted and observed probabilities. Nomograms were constructed for each model to enhance its clinical utility.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec xml:lang=\"en\">\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p xml:lang=\"en\">These dynamic prediction models offer a novel approach for reassessing thromboembolic and major bleeding risks in patients with atrial fibrillation receiving DOACs, demonstrating superior performance compared with existing models. Enabling regular risk updates based on the patient\u2019s current status, these tools support personalized and dynamic risk assessments in clinical practice.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1161\/jaha.125.043979","type":"journal-article","created":{"date-parts":[[2025,10,28]],"date-time":"2025-10-28T10:50:03Z","timestamp":1761648603000},"update-policy":"https:\/\/doi.org\/10.1161\/crossmarkpolicy","source":"Crossref","is-referenced-by-count":3,"title":["Development and Validation of Dynamic Prediction Model for Thromboembolic and Bleeding Risk in Patients With Atrial Fibrillation Initiating Direct Oral Anticoagulants"],"prefix":"10.1161","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3626-3999","authenticated-orcid":false,"given":"Kyu\u2010Nam","family":"Heo","sequence":"first","affiliation":[{"name":"Seoul National University","place":["Seoul, Republic of 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University College of Medicine","place":["Seoul, Republic of Korea"]},{"name":"Seoul National University Hospital","place":["Seoul, Republic of Korea"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6101-7127","authenticated-orcid":false,"given":"Hyung\u2010Min","family":"Kwon","sequence":"additional","affiliation":[{"name":"Seoul National University College of Medicine","place":["Seoul, Republic of Korea"]},{"name":"Seoul Metropolitan Government\u2010Seoul National University Boramae Medical Center","place":["Seoul, Republic of Korea"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2261-7330","authenticated-orcid":false,"given":"Ju\u2010Yeun","family":"Lee","sequence":"additional","affiliation":[{"name":"Seoul National University","place":["Seoul, Republic of 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