{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T00:48:32Z","timestamp":1787014112405,"version":"build-2736575974"},"reference-count":28,"publisher":"Wiley","issue":"5","license":[{"start":{"date-parts":[[2026,4,24]],"date-time":"2026-04-24T00:00:00Z","timestamp":1776988800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,4,24]],"date-time":"2026-04-24T00:00:00Z","timestamp":1776988800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100004587","name":"Instituto de Salud Carlos III","doi-asserted-by":"publisher","award":["DTS23\/00010"],"award-info":[{"award-number":["DTS23\/00010"]}],"id":[{"id":"10.13039\/501100004587","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Eur J Clin Investigation"],"published-print":{"date-parts":[[2026,5]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>ST\u2010elevation myocardial infarction (STEMI) exhibits substantial clinical heterogeneity complicating prehospital risk stratification. Traditional risk assessment tools often fail to capture the complexity of this condition. Machine learning offers opportunities to identify complex clinical patterns not readily apparent during prehospital care.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Aim<\/jats:title>\n                    <jats:p>To identify distinct phenotypes in STEMI patients using unsupervised machine learning algorithms based on prehospital parameters, and to determine their association with short\u2010term mortality and cardiovascular outcomes.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Prospective multicenter observational cohort study including adult patients with prehospital STEMI code activation transported by emergency medical services from January 2022 to August 2025. Only EMS\u2010transported patients were included; those who self\u2010presented to the emergency department were excluded. Prehospital variables, including demographic, clinical, and procedural data, were used for clustering. Factor Analysis of Mixed Data and a two\u2010step clustering: hierarchical clustering (exploring structure and number of clusters) and k\u2010means (clustering assigning patients to phenotypes). A Random Forest classifier with SHapley Additive exPlanations values was used to identify variables contributing to cluster assignment. The primary outcome was 30\u2010day all\u2010cause mortality, assessed through follow\u2010up records.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Among 744 patients (median age, 65\u2009years; 76.3% male) unsupervised clustering identified three distinct phenotypes: Phenotype\u20101 (70.3%) characterized by hemodynamic stability, vessel locations, Killip class I presentation (70.6%), and favourable laboratory parameters; Phenotype\u20102 (24.3%) presented higher comorbidity burden and metabolic derangements; and Phenotype\u20103 (5.4%) exhibiting profound hemodynamic instability, severe respiratory failure, out\u2010of\u2010hospital cardiac arrest with return of spontaneous circulation (87.5%), Killip class IV presentation (67.5%), and marked metabolic derangements. The 30\u2010day mortality rates were: 3.4% in Phenotype\u20101, 22.1% in Phenotype\u20102, and 75.0% in Phenotype\u20103.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Three clinically distinct STEMI phenotypes were identified with markedly different mortality risks and treatment requirements during prehospital care. Phenotypes derived from readily available prehospital parameters may facilitate early risk stratification, optimize triage decisions, and guide individualized therapeutic strategies.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/eci.70214","type":"journal-article","created":{"date-parts":[[2026,4,24]],"date-time":"2026-04-24T12:47:55Z","timestamp":1777034875000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Prehospital Risk Stratification Using Unsupervised Machine Learning in\n                    <scp>STEMI<\/scp>"],"prefix":"10.1111","volume":"56","author":[{"given":"Ana","family":"Ramos\u2010Rodr\u00edguez","sequence":"first","affiliation":[{"name":"Emergency Department Hospital Universitario Rio Hortega  Valladolid Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ra\u00fal","family":"L\u00f3pez\u2010Izquierdo","sequence":"additional","affiliation":[{"name":"Emergency Department Hospital Universitario Rio Hortega  Valladolid Spain"},{"name":"Faculty of Medicine Universidad de Valladolid  Valladolid Spain"},{"name":"CIBER of Respiratory Diseases (CIBERES) Institute of Health Carlos III  Madrid Spain"},{"name":"Valladolid Health Research Institute (IBioVALL)  Valladolid Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Carlos","family":"del Pozo Vegas","sequence":"additional","affiliation":[{"name":"Faculty of Medicine Universidad de Valladolid  Valladolid Spain"},{"name":"Valladolid Health Research Institute (IBioVALL)  Valladolid Spain"},{"name":"Emergency Department Hospital Cl\u00ednico Universitario  Valladolid Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mar\u00eda","family":"Plaza\u2010Mart\u00edn","sequence":"additional","affiliation":[{"name":"Cardiology Department Hospital Cl\u00ednico Universitario de Valladolid  Valladolid Spain"},{"name":"CIBER of Cardiovascular Diseases (CIBERCV) Institute of Health Carlos III  Madrid Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cristina","family":"Tapia\u2010Ballesteros","sequence":"additional","affiliation":[{"name":"Cardiology Department Hospital Universitario Rio Hortega  Valladolid Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Juan F.","family":"Delgado Benito","sequence":"additional","affiliation":[{"name":"Emergency Medical Services (SACYL)  Valladolid Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5024-5108","authenticated-orcid":false,"given":"Ancor","family":"Sanz\u2010Garc\u00eda","sequence":"additional","affiliation":[{"name":"Faculty of Health Sciences Universidad de Castilla La Mancha  Talavera de la Reina Spain"},{"name":"Technological Innovation Applied to Health Research Group (ITAS Group), Faculty of Health Sciences University of de Castilla\u2010La Mancha  Talavera de la Reina Spain"},{"name":"Health Care Evaluation Research Group (ECUSAL) Instituto de Investigaci\u00f3n Sanitaria de Castilla\u2010La Mancha (IDISCAM)  Toledo Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Francisco","family":"Mart\u00edn\u2010Rodr\u00edguez","sequence":"additional","affiliation":[{"name":"Faculty of Medicine Universidad de Valladolid  Valladolid Spain"},{"name":"Valladolid Health Research Institute (IBioVALL)  Valladolid Spain"},{"name":"Emergency Medical Services (SACYL)  Valladolid Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,4,24]]},"reference":[{"key":"e_1_2_11_2_1","doi-asserted-by":"publisher","DOI":"10.1093\/eurheartj\/ehae466"},{"key":"e_1_2_11_3_1","doi-asserted-by":"publisher","DOI":"10.1161\/CIR.0000000000001039"},{"key":"e_1_2_11_4_1","doi-asserted-by":"publisher","DOI":"10.1161\/CIRCINTERVENTIONS.119.008537"},{"key":"e_1_2_11_5_1","doi-asserted-by":"publisher","DOI":"10.1161\/JAHA.119.015539"},{"key":"e_1_2_11_6_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41514-025-00238-9"},{"key":"e_1_2_11_7_1","doi-asserted-by":"publisher","DOI":"10.1136\/bmjhci-2024-101292"},{"key":"e_1_2_11_8_1","doi-asserted-by":"publisher","DOI":"10.1186\/s13049-020-0713-4"},{"key":"e_1_2_11_9_1","doi-asserted-by":"publisher","DOI":"10.1093\/eurheartj\/ehad191"},{"key":"e_1_2_11_10_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jacc.2023.06.025"},{"key":"e_1_2_11_11_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10916-022-01825-z"},{"key":"e_1_2_11_12_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijcard.2024.132272"},{"key":"e_1_2_11_13_1","doi-asserted-by":"publisher","DOI":"10.1186\/s12944-021-01475-z"},{"key":"e_1_2_11_14_1","doi-asserted-by":"publisher","DOI":"10.1186\/s12944-024-02128-7"},{"key":"e_1_2_11_15_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ebiom.2023.104930"},{"key":"e_1_2_11_16_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jcin.2022.03.036"},{"key":"e_1_2_11_17_1","doi-asserted-by":"publisher","DOI":"10.1161\/CIRCIMAGING.117.006955"},{"key":"e_1_2_11_18_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijcard.2019.11.123"},{"key":"e_1_2_11_19_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jcin.2020.04.004"},{"key":"e_1_2_11_20_1","doi-asserted-by":"publisher","DOI":"10.1161\/JAHA.124.040681"},{"key":"e_1_2_11_21_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41569-019-0221-2"},{"key":"e_1_2_11_22_1","doi-asserted-by":"publisher","DOI":"10.1097\/SHK.0000000000001356"},{"key":"e_1_2_11_23_1","doi-asserted-by":"publisher","DOI":"10.1093\/ehjacc\/zuab020.163"},{"key":"e_1_2_11_24_1","doi-asserted-by":"publisher","DOI":"10.1186\/s12933-023-01812-9"},{"key":"e_1_2_11_25_1","doi-asserted-by":"publisher","DOI":"10.1002\/joa3.12708"},{"key":"e_1_2_11_26_1","doi-asserted-by":"publisher","DOI":"10.1186\/s12872-025-05180-y"},{"key":"e_1_2_11_27_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.carrev.2025.07.017"},{"key":"e_1_2_11_28_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.rec.2021.12.005"},{"key":"e_1_2_11_29_1","doi-asserted-by":"publisher","DOI":"10.1186\/s13049-025-01329-y"}],"container-title":["European Journal of Clinical Investigation"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1111\/eci.70214","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/full-xml\/10.1111\/eci.70214","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1111\/eci.70214","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,22]],"date-time":"2026-05-22T07:01:36Z","timestamp":1779433296000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1111\/eci.70214"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,24]]},"references-count":28,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2026,5]]}},"alternative-id":["10.1111\/eci.70214"],"URL":"https:\/\/doi.org\/10.1111\/eci.70214","archive":["Portico"],"relation":{},"ISSN":["0014-2972","1365-2362"],"issn-type":[{"value":"0014-2972","type":"print"},{"value":"1365-2362","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,4,24]]},"assertion":[{"value":"2026-02-17","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-04-17","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-04-24","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"e70214"}}