{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,17]],"date-time":"2026-10-17T07:37:09Z","timestamp":1792222629484,"version":"4.4.0"},"reference-count":33,"publisher":"Ovid Technologies (Wolters Kluwer Health)","issue":"5","content-domain":{"domain":["www.ahajournals.org"],"crossmark-restriction":true},"short-container-title":["JAHA"],"published-print":{"date-parts":[[2024,3,5]]},"abstract":"<jats:sec sec-type=\"background\" xml:lang=\"en\">\n            <jats:title>Background<\/jats:title>\n            <jats:p xml:lang=\"en\">Recent studies have indicated high rates of future major adverse cardiovascular events in patients with Takotsubo cardiomyopathy (TC), but there is no well\u2010established tool for risk stratification. This study sought to evaluate the prognostic value of several artificial intelligence\u2010augmented ECG (AI\u2010ECG) algorithms in patients with TC.<\/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              This study examined consecutive patients in the prospective and observational Mayo Clinic Takotsubo syndrome registry. Several previously validated AI\u2010ECG algorithms were used for the estimation of ECG\u2010 age, probability of low ejection fraction, and probability of atrial fibrillation. Multivariable models were constructed to evaluate the association of AI\u2010ECG and other clinical characteristics with major adverse cardiac events, defined as cardiovascular death, recurrence of TC, nonfatal myocardial infarction, hospitalization for congestive heart failure, and stroke. In the final analysis, 305 patients with TC were studied over a median follow\u2010up of 4.8\u2009years. Patients with future major adverse cardiac events were more likely to be older, have a history of hypertension, congestive heart failure, worse renal function, as well as high\u2010risk AI\u2010ECG findings compared with those without. Multivariable Cox proportional hazards analysis indicated that the presence of 2 or 3 high\u2010risk findings detected by AI\u2010ECG remained a significant predictor of major adverse cardiac events in patients with TC after adjustment by conventional risk factors (hazard ratio, 4.419 [95% CI, 1.833\u201310.66],\n              <jats:italic>P<\/jats:italic>\n              =0.001).\n            <\/jats:p>\n          <\/jats:sec>\n          <jats:sec xml:lang=\"en\">\n            <jats:title>Conclusions<\/jats:title>\n            <jats:p xml:lang=\"en\">The combined use of AI\u2010ECG algorithms derived from a single 12\u2010lead ECG might detect subtle underlying patterns associated with worse outcomes in patients with TC. This approach might be beneficial for stratifying high\u2010risk patients with TC.<\/jats:p>\n          <\/jats:sec>","DOI":"10.1161\/jaha.123.031859","type":"journal-article","created":{"date-parts":[[2024,2,23]],"date-time":"2024-02-23T10:43:11Z","timestamp":1708684991000},"update-policy":"https:\/\/doi.org\/10.1161\/crossmarkpolicy","source":"Crossref","is-referenced-by-count":8,"title":["Predictive Value of Artificial Intelligence\u2010Enabled Electrocardiography in Patients With Takotsubo Cardiomyopathy"],"prefix":"10.1161","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6535-4377","authenticated-orcid":false,"given":"Yoshihisa","family":"Kanaji","sequence":"first","affiliation":[{"name":"Department of Cardiovascular Medicine Mayo Clinic Rochester MN USA"},{"name":"Division of Cardiovascular Medicine Tsuchiura Kyodo General Hospital Ibaraki 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