{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,9]],"date-time":"2026-10-09T05:51:45Z","timestamp":1791525105342,"version":"4.3.4"},"reference-count":33,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,10,9]],"date-time":"2026-10-09T00:00:00Z","timestamp":1791504000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100005046","name":"Natural Science Foundation of Heilongjiang Province","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100005046","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Cardiovasc. Med."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Prognosis among patients with acute myocardial infarction (AMI) shows substantial heterogeneity, making precise identification of high-risk phenotypes essential for improving long-term outcomes. Machine learning techniques were used to identify such phenotypes by integrating multidimensional data and to examine their association with major adverse cardiovascular events (MACE).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A total of 1,139 patients with AMI were enrolled from the First Affiliated Hospital of Harbin Medical University between March 2023 and June 2024. Multidimensional data, including clinical features, metabolic characteristics, and behavioral patterns, were collected. Phenotyping was performed using a hierarchical clustering algorithm. Associations between derived phenotypes and 1-year MACE were evaluated using Kaplan\u2013Meier survival analysis and multivariable Cox regression models. Shapley Additive Explanations (SHAP) values derived from an XGBoost model were used to quantify the contributions of individual variables to cluster assignment.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Hierarchical clustering identified two clinical phenotypes with differing pathophysiological characteristics. Cluster 2 (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u2009243, 21.3%) exhibited a high-risk \u201cfrailty\u2013inflammation\u201d phenotype, characterized by impaired activities of daily living, elevated risk of falls and pressure ulcers, increased high-sensitivity C-reactive protein (hs-CRP) levels, and poor health behaviors. Compared with Cluster 1, patients in Cluster 2 showed a higher hazard of 1-year MACE (unadjusted HR\u2009=\u20094.17; 95% CI: 3.237\u20135.371;\n                      <jats:italic>P<\/jats:italic>\n                      \u2009&amp;lt;\u20090.001) and a markedly increased risk of all-cause mortality (HR\u2009=\u200912.162; 95% CI: 7.445\u201319.868;\n                      <jats:italic>P<\/jats:italic>\n                      \u2009&amp;lt;\u20090.001). After adjustment using the minimal sufficient confounding model (M3, incorporating 17 prespecified confounders), Cluster 2 membership remained significantly associated with an increased risk of 1-year MACE (adjusted HR\u2009=\u20092.726; 95% CI 1.986\u20133.743;\n                      <jats:italic>P<\/jats:italic>\n                      \u2009&amp;lt;\u20090.001) and all-cause mortality (adjusted HR\u2009=\u20094.35; 95% CI 2.393\u20137.905;\n                      <jats:italic>P<\/jats:italic>\n                      \u2009&amp;lt;\u20090.001). In multivariable Cox regression analysis, Cluster 2 membership was identified as an independent predictor for MACE.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Integration of multidimensional data using machine learning identifies a high-risk \u201cfrailty\u2013inflammation\u201d phenotype in patients with AMI. This phenotype offers a comprehensive biological\u2013functional\u2013behavioral framework for risk stratification and may support early and precise clinical interventions to reduce long-term adverse outcomes. However, despite its potential clinical value, the current evidence is insufficient to support direct translation into routine clinical practice until independent cohort validation is performed. Future multi-center studies are warranted to confirm the robustness and generalizability of this approach, and to prospectively evaluate whether phenotype-guided intervention strategies can improve long-term outcomes.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fcvm.2026.1881280","type":"journal-article","created":{"date-parts":[[2026,10,9]],"date-time":"2026-10-09T05:30:23Z","timestamp":1791523823000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Phenotyping of AMI patients using machine learning: integration of clinical, metabolic, and behavioral features to identify high-risk \u201cfrailty\u2013inflammation\u201d phenotype"],"prefix":"10.3389","volume":"13","author":[{"given":"Yisha","family":"Zhao","sequence":"first","affiliation":[{"name":"The First Affiliated Hospital of Harbin Medical University","place":["Harbin, Heilongjiang, 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