{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T14:11:18Z","timestamp":1779286278648,"version":"3.51.4"},"reference-count":31,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T00:00:00Z","timestamp":1777852800000},"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>Background<\/jats:title>\n                    <jats:p>Older adults with acute myocardial infarction (AMI) represent a particularly high-risk population for early in-hospital deterioration. Life-threatening complications\u2014including sustained ventricular tachycardia or fibrillation (sVT\/VF), cardiogenic shock (CS), and acute heart failure (AHF)\u2014are major contributors to in-hospital adverse events (IAE), as these events may herald an increased risk of poor post-discharge prognosis. However, a simple and visually intuitive tool for early risk stratification in this population remains lacking.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We conducted a retrospective cohort study of consecutive patients aged &amp;gt;60 years with AMI admitted between January 2019 and December 2024. The primary endpoint was a composite of in-hospital death, sVT\/VF, CS, or AHF. After multiple imputation for missing data, a multi-stage feature selection strategy incorporating least absolute shrinkage and selection operator (LASSO) logistic regression and random forest analysis was applied.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Among 4,897 included patients, 434 (8.9%) experienced the composite endpoint. The final model incorporated 10 readily available clinical variables: age, atrial fibrillation (AF), pulse rate, systolic blood pressure (SBP), neutrophil-to-lymphocyte ratio (NLR), albumin, N-terminal pro-B-type natriuretic peptide (NT-proBNP), estimated glomerular filtration rate (eGFR), glucose, and alanine aminotransferase (ALT). The model demonstrated good discrimination [area under the receiver operating characteristic curve (AUC) 0.808, 95% confidence interval (CI) 0.786\u20130.830;\n                      <jats:italic>p<\/jats:italic>\n                      \u202f&amp;lt;\u202f0.001] and satisfactory calibration. The risk score heatmap allows intuitive estimation of individual risk based on cumulative point scores.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>We developed an internally validated 10-variable prediction model, visualized as a risk score heatmap, to estimate IAE in older adults with AMI. This intuitive tool may support early bedside risk stratification and personalized management in clinical practice.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fmed.2026.1828310","type":"journal-article","created":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T05:34:13Z","timestamp":1777872853000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Development and validation of a risk score heatmap for in-hospital adverse events in older adults with acute myocardial infarction"],"prefix":"10.3389","volume":"13","author":[{"given":"Zizhu","family":"Lian","sequence":"first","affiliation":[{"name":"Department of Cardiovascular Surgery, The First Affiliated Hospital of Xi\u2019an Jiaotong University","place":["Xi\u2019an, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yue","family":"Xu","sequence":"additional","affiliation":[{"name":"Xi\u2019an Jiaotong University Health Science Center","place":["Xi\u2019an, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shikang","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Cardiovascular Medicine, The First Affiliated Hospital of Xi\u2019an Jiaotong University","place":["Xi\u2019an, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaohong","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Cardiovascular Surgery, The First Affiliated Hospital of Xi\u2019an Jiaotong University","place":["Xi\u2019an, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1965","published-online":{"date-parts":[[2026,5,4]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"197","DOI":"10.1016\/S0140-6736(16)30677-8","article-title":"Acute myocardial 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