{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,29]],"date-time":"2026-10-29T16:03:14Z","timestamp":1793289794659,"version":"4.5.0"},"reference-count":94,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2025,5,25]],"date-time":"2025-05-25T00:00:00Z","timestamp":1748131200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"\u201cDunarea de Jos\u201d University of Galati","award":["VAT RO50411550"],"award-info":[{"award-number":["VAT RO50411550"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JCM"],"abstract":"<jats:p>Background: Cardiogenic shock (CS) is a life-threatening complication of ST-elevation myocardial infarction (STEMI) and remains the leading cause of in-hospital mortality, with rates ranging from 5 to 10% despite advances in reperfusion strategies. Early identification and timely intervention are critical for improving outcomes. This study investigates the utility of machine learning (ML) models for predicting the risk of CS during the early phases of care\u2014prehospital, emergency department (ED), and cardiology-on-call\u2014with a focus on accurate triage and prioritization for urgent angiography. Results: In the prehospital phase, the Extra Trees classifier demonstrated the highest overall performance. It achieved an accuracy (ACC) of 0.9062, precision of 0.9078, recall of 0.9062, F1-score of 0.9061, and Matthews correlation coefficient (MCC) of 0.8140, indicating both high predictive power and strong generalization. In the ED phase, the support vector machine model outperformed others with an ACC of 78.12%. During the cardiology-on-call phase, Random Forest showed the best performance with an ACC of 81.25% and consistent values across other metrics. Quadratic discriminant analysis showed consistent and generalizable performance across all early care stages. Key predictive features included the Killip class, ECG rhythm, creatinine, potassium, and markers of renal dysfunction\u2014parameters readily available in routine emergency settings. The greatest clinical utility was observed in prehospital and ED phases, where ML models could support the early identification of critically ill patients and could prioritize coronary catheterization, especially important for centers with limited capacity for angiography. Conclusions: Machine learning-based predictive models offer a valuable tool for early risk stratification in STEMI patients at risk for cardiogenic shock. These findings support the implementation of ML-driven tools in early STEMI care pathways, potentially improving survival through faster and more accurate decision-making, especially in time-sensitive clinical environments.<\/jats:p>","DOI":"10.3390\/jcm14113698","type":"journal-article","created":{"date-parts":[[2025,5,25]],"date-time":"2025-05-25T20:26:50Z","timestamp":1748204810000},"page":"3698","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["AI-Based Predictive Models for Cardiogenic Shock in STEMI: Real-World Data for Early Risk Assessment and Prognostic Insights"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5056-1015","authenticated-orcid":false,"given":"Elena","family":"Stamate","sequence":"first","affiliation":[{"name":"Department of Morphological and Functional Sciences, Faculty of Medicine and Pharmacy, \u201cDunarea de Jos\u201d University of Galati, 35, Al. I. Cuza Street, 800216 Galati, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anisia-Luiza","family":"Culea-Florescu","sequence":"additional","affiliation":[{"name":"Department of Electronics and Telecommunications, \u201cDunarea de Jos\u201d University of Gala\u021bi, 800008 Galati, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4536-1574","authenticated-orcid":false,"given":"Mihaela","family":"Miron","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Information Technology, \u201cDunarea de Jos\u201d University of Gala\u021bi, 800008 Galati, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9664-2128","authenticated-orcid":false,"given":"Alin-Ionut","family":"Piraianu","sequence":"additional","affiliation":[{"name":"Department of Morphological and Functional Sciences, Faculty of Medicine and Pharmacy, \u201cDunarea de Jos\u201d University of Galati, 35, Al. I. Cuza Street, 800216 Galati, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1557-9791","authenticated-orcid":false,"given":"Adrian George","family":"Dumitrascu","sequence":"additional","affiliation":[{"name":"Division of Hospital Internal Medicine, Department of Medicine, Mayo Clinic Florida, 4500 San Pablo Rd S, Jacksonville, FL 32224, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Iuliu","family":"Fulga","sequence":"additional","affiliation":[{"name":"Department of Medical, Faculty of Medicine and Pharmacy, \u201cDunarea de Jos\u201d University of Galati, 35, Al. I. Cuza Street, 800216 Galati, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ana","family":"Fulga","sequence":"additional","affiliation":[{"name":"Department of Clinical Surgical, Faculty of Medicine and Pharmacy, \u201cDunarea de Jos\u201d University of Galati, 35, Al. I. Cuza Street, 800216 Galati, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5853-393X","authenticated-orcid":false,"given":"Octavian Stefan","family":"Patrascanu","sequence":"additional","affiliation":[{"name":"Faculty of Medicine and Pharmacy, Dunarea de Jos University of Galati, 35 AL Cuza St, 800010 Galati, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Doriana","family":"Iancu","sequence":"additional","affiliation":[{"name":"Faculty of Medicine and Pharmacy, Dunarea de Jos University of Galati, 35 AL Cuza St, 800010 Galati, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4568-5237","authenticated-orcid":false,"given":"Octavian Catalin","family":"Ciobotaru","sequence":"additional","affiliation":[{"name":"Department of Clinical Surgical, Faculty of Medicine and Pharmacy, \u201cDunarea de Jos\u201d University of Galati, 35, Al. I. Cuza Street, 800216 Galati, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9173-6205","authenticated-orcid":false,"given":"Oana Roxana","family":"Ciobotaru","sequence":"additional","affiliation":[{"name":"Department of Clinical Medical, Faculty of Medicine and Pharmacy, \u201cDunarea de Jos\u201d University of Galati, 35, Al. I. Cuza Street, 800216 Galati, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,5,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"682","DOI":"10.1016\/j.cjca.2025.02.002","article-title":"The Role of a Cardiogenic Shock Team in Decision Making Surrounding Mechanical Circulatory Support","volume":"41","author":"So","year":"2025","journal-title":"Can. J. 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