{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,6]],"date-time":"2026-10-06T02:46:36Z","timestamp":1791254796928,"version":"4.1.0"},"reference-count":55,"publisher":"Ovid Technologies (Wolters Kluwer Health)","issue":"10","content-domain":{"domain":["www.ahajournals.org"],"crossmark-restriction":true},"short-container-title":["JAHA"],"published-print":{"date-parts":[[2017,10,11]]},"abstract":"<jats:sec xml:lang=\"en\">\n            <jats:title>Background<\/jats:title>\n            <jats:p xml:lang=\"en\">\n              Time in the therapeutic range (\n              <jats:styled-content style=\"fixed-case\">TTR<\/jats:styled-content>\n              ) is associated with the effectiveness and safety of vitamin K antagonist (\n              <jats:styled-content style=\"fixed-case\">VKA<\/jats:styled-content>\n              ) therapy. To optimize prescribing of\n              <jats:styled-content style=\"fixed-case\">VKA<\/jats:styled-content>\n              , we aimed to develop and validate a prediction model for\n              <jats:styled-content style=\"fixed-case\">TTR<\/jats:styled-content>\n              in older adults taking\n              <jats:styled-content style=\"fixed-case\">VKA<\/jats:styled-content>\n              for nonvalvular atrial fibrillation and venous thromboembolism.\n            <\/jats:p>\n          <\/jats:sec>\n          <jats:sec xml:lang=\"en\">\n            <jats:title>Methods and Results<\/jats:title>\n            <jats:p xml:lang=\"en\">\n              The study cohort comprised patients aged \u226565 years who were taking\n              <jats:styled-content style=\"fixed-case\">VKA<\/jats:styled-content>\n              for atrial fibrillation or venous thromboembolism and who were identified in the 2\n              <jats:styled-content style=\"fixed-case\">US<\/jats:styled-content>\n              electronic health record databases linked with Medicare claims data from 2007 through 2014. With the predictors identified from a systematic review and clinical knowledge, we built a prediction model for\n              <jats:styled-content style=\"fixed-case\">TTR<\/jats:styled-content>\n              , using one electronic health record system as the training set and the other as the validation set. We compared the performance of the new models to that of a published prediction score for\n              <jats:styled-content style=\"fixed-case\">TTR<\/jats:styled-content>\n              ,\n              <jats:styled-content style=\"fixed-case\">SAM<\/jats:styled-content>\n              e\u2010\n              <jats:styled-content style=\"fixed-case\">TT<\/jats:styled-content>\n              <jats:sub>2<\/jats:sub>\n              R\n              <jats:sub>2<\/jats:sub>\n              . Based on 1663 patients in the training set and 1181 in the validation set, our optimized score included 42 variables and the simplified model included 7 variables, abbreviated as\n              <jats:styled-content style=\"fixed-case\">PROSPER<\/jats:styled-content>\n              (Pneumonia, Renal dysfunction, Oozing blood [prior bleeding], Staying in hospital \u22657\u00a0days, Pain medication use, no Enhanced [structured] anticoagulation services, Rx for antibiotics). The\n              <jats:styled-content style=\"fixed-case\">PROSPER<\/jats:styled-content>\n              score outperformed\n              <jats:styled-content style=\"fixed-case\">SAM<\/jats:styled-content>\n              e\u2010\n              <jats:styled-content style=\"fixed-case\">TT<\/jats:styled-content>\n              <jats:sub>2<\/jats:sub>\n              R\n              <jats:sub>2<\/jats:sub>\n              when predicting both\n              <jats:styled-content style=\"fixed-case\">TTR<\/jats:styled-content>\n              \u226570% (area under the receiver operating characteristic curve 0.67 versus 0.55) and the thromboembolic and bleeding outcomes (area under the receiver operating characteristic curve 0.62 versus 0.52).\n            <\/jats:p>\n          <\/jats:sec>\n          <jats:sec xml:lang=\"en\">\n            <jats:title>Conclusions<\/jats:title>\n            <jats:p xml:lang=\"en\">\n              Our geriatric\n              <jats:styled-content style=\"fixed-case\">TTR<\/jats:styled-content>\n              score can be used as a clinical decision aid to select appropriate candidates to receive\n              <jats:styled-content style=\"fixed-case\">VKA<\/jats:styled-content>\n              therapy and as a research tool to address confounding and treatment effect heterogeneity by anticoagulation quality.\n            <\/jats:p>\n          <\/jats:sec>","DOI":"10.1161\/jaha.117.006814","type":"journal-article","created":{"date-parts":[[2017,10,6]],"date-time":"2017-10-06T00:10:26Z","timestamp":1507248626000},"update-policy":"https:\/\/doi.org\/10.1161\/crossmarkpolicy","source":"Crossref","is-referenced-by-count":33,"title":["Prediction Score for Anticoagulation Control Quality Among Older Adults"],"prefix":"10.1161","volume":"6","author":[{"given":"Kueiyu Joshua","family":"Lin","sequence":"first","affiliation":[{"name":"Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA"},{"name":"Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA"},{"name":"Department of Epidemiology, Harvard T.H. 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