{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,18]],"date-time":"2026-09-18T07:03:39Z","timestamp":1789715019322,"version":"4.0.1"},"reference-count":35,"publisher":"Ovid Technologies (Wolters Kluwer Health)","issue":"7","content-domain":{"domain":["www.ahajournals.org"],"crossmark-restriction":true},"short-container-title":["JAHA"],"published-print":{"date-parts":[[2026,4,7]]},"abstract":"<jats:sec xml:lang=\"en\">\n                    <jats:title>Background<\/jats:title>\n                    <jats:p xml:lang=\"en\">Timely recognition of oral anticoagulant use is critical in acute stroke but is often hampered by impaired consciousness and unavailable medication history. We investigated whether routinely available coagulation tests, prothrombin time\u2013international normalized ratio and activated partial thromboplastin time, paired with machine learning can identify anticoagulant exposure at presentation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec xml:lang=\"en\">\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p xml:lang=\"en\">We performed a multicenter diagnostic modeling study using the Japan Stroke Data Bank. Adults with ischemic or hemorrhagic stroke admitted in 2016 to 2021 comprised the derivation cohort; those in 2022 formed a temporally distinct validation cohort. Three models (random forest, extreme gradient boosting, and neural network) used prothrombin time\u2013international normalized ratio and activated partial thromboplastin time as predictors to classify anticoagulant status: vitamin K antagonist, direct oral anticoagulant, or none. Discrimination (area under the receiver operating characteristic curve and area under the precision\u2010recall curve), calibration, and decision curve analysis were assessed. Model outputs were implemented as bedside probability heat maps.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec xml:lang=\"en\">\n                    <jats:title>Results<\/jats:title>\n                    <jats:p xml:lang=\"en\">Among 30\u2009767 patients (derivation n=24\u2009857; validation n=5910), vitamin K antagonist\/direct oral anticoagulant use was 4.5%\/8.2% in derivation and 2.8%\/9.5% in validation. In the validation cohort, the neural network yielded the best performance: area under the receiver operating characteristic curve, 0.933 (95% CI, 0.908\u20130.955) and area under the precision\u2010recall curve, 0.52 (95% CI, 0.44\u20130.61) for vitamin K antagonist; area under the receiver operating characteristic curve, 0.841 (95% CI, 0.822\u20130.858) and area under the precision\u2010recall curve, 0.39 (95% CI, 0.35\u20130.43) for direct oral anticoagulant, with preserved calibration and highest net benefit across clinically relevant thresholds. Heat maps summarized class probabilities over the prothrombin time\u2013international normalized ratio\u00d7activated partial thromboplastin time space to support rapid inference.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec xml:lang=\"en\">\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p xml:lang=\"en\">A simple machine learning model using only prothrombin time\u2013international normalized ratio and activated partial thromboplastin time classified vitamin K antagonist and direct oral anticoagulant exposure with high accuracy and practical clinical utility. This tool assists emergency decision\u2010making when medication history is unknown, supporting the safety and timeliness of acute stroke care.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1161\/jaha.125.047432","type":"journal-article","created":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T11:09:42Z","timestamp":1773832182000},"update-policy":"https:\/\/doi.org\/10.1161\/crossmarkpolicy","source":"Crossref","is-referenced-by-count":0,"title":["Development and Validation of a Machine Learning Model to Predict Oral Anticoagulant Use in Stroke From Prothrombin Time\u2013International Normalized Ratio and Activated Partial Thromboplastin Time"],"prefix":"10.1161","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2799-9243","authenticated-orcid":false,"given":"Gaku","family":"Fujiwara","sequence":"first","affiliation":[{"name":"Kyoto Prefectural University of Medicine","place":["Kyoto, Japan"]},{"name":"Japanese Red Cross Kyoto Daini Hospital","place":["Kyoto, Japan"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2203-7077","authenticated-orcid":false,"given":"Yoshinari","family":"Nagakane","sequence":"additional","affiliation":[{"name":"Japanese Red Cross Kyoto Daini Hospital","place":["Kyoto, Japan"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nobukuni","family":"Murakami","sequence":"additional","affiliation":[{"name":"Japanese Red Cross Kyoto Daini Hospital","place":["Kyoto, Japan"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eiichi","family":"Suehiro","sequence":"additional","affiliation":[{"name":"International University of Health and Welfare School of Medicine","place":["Chiba, Japan"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Naoya","family":"Hashimoto","sequence":"additional","affiliation":[{"name":"Kyoto Prefectural University of Medicine","place":["Kyoto, Japan"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4751-3538","authenticated-orcid":false,"given":"Sohei","family":"Yoshimura","sequence":"additional","affiliation":[{"name":"National Cerebral and Cardiovascular Center","place":["Suita, Japan"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8346-9845","authenticated-orcid":false,"given":"Kazunori","family":"Toyoda","sequence":"additional","affiliation":[{"name":"National Cerebral and Cardiovascular Center","place":["Suita, Japan"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2266-476X","authenticated-orcid":false,"given":"Yohei","family":"Okada","sequence":"additional","affiliation":[{"name":"Kyoto University","place":["Kyoto, Japan"]},{"name":"Duke\u2010NUS Medical School, National University of 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