{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,27]],"date-time":"2026-08-27T06:18:45Z","timestamp":1787811525073,"version":"build-2784847793"},"reference-count":56,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,8,27]],"date-time":"2026-08-27T00:00:00Z","timestamp":1787788800000},"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. Med."],"abstract":"<jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>In this study, we aimed to develop and validate an easy-to-use model for screening type 2 myocardial infarction (T2MI) in geriatric patients with acute coronary syndrome (ACS).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Method<\/jats:title>\n                    <jats:p>From January 2017 to December 2022, 891 geriatric patients who were clinically diagnosed with ACS and who underwent coronary angiography visited the Chest Pain Center of the Emergency Department at Jiading District Central Hospital affiliated Shanghai University of Medicine &amp;amp; Health Sciences were included. Univariate and multivariate logistic regression analyses were then performed to identify risk factors for T2MI among ACS patients. Subsequently, these factors were used to establish a scoring system, from which a nomogram was developed using R software.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Result<\/jats:title>\n                    <jats:p>\n                      This study included 891 geriatric patients diagnosed with ACS who underwent coronary angiography and met the criteria. Among these patients, 21.3% (190\/891) were diagnosed with T2MI. Univariate analysis identified 10 factors significantly associated with T2MI occurrence in geriatric ACS patients (\n                      <jats:italic>P<\/jats:italic>\n                      \u2009&amp;lt;\u20090.05). Multivariate logistic regression analysis revealed atrial fibrillation, COPD, anemia, chronic heart failure, presence of atypical symptoms, and non-ST-segment elevation as independent risk factors for T2MI in geriatric ACS patients. The nomogram model, constructed using troponin and the six identified risk factors, demonstrated a AUC of training set of 0.803 [95% confidence interval (CI): 0.765\u20130.841], indicating good discrimination. Moreover, calibration and clinical decision curve analyses further confirmed the reliability and clinical value of the proposed model.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The predictive nomogram model, based on clinical features of geriatric ACS patients in the emergency department, enables emergency physicians to quickly screen T2MI. Consequently, early identification guides personalized treatment.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fmed.2026.1907612","type":"journal-article","created":{"date-parts":[[2026,8,27]],"date-time":"2026-08-27T05:39:30Z","timestamp":1787809170000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["An easy-to-use model for screening type 2 myocardial infarction in geriatric patients with acute coronary syndrome: a retrospective cohort study"],"prefix":"10.3389","volume":"13","author":[{"given":"Yun-yue","family":"Luo","sequence":"first","affiliation":[{"name":"Shanghai University of Medicine & Health Sciences, Shanghai University of Traditional Chinese Medicine","place":["Shanghai, China"]},{"name":"Department of Emergency and Critical Care Medicine, Jiading District Central Hospital Affiliated Shanghai University of Medicine & Health Sciences","place":["Shanghai, 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