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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

AI-Based Predictive Models for Cardiogenic Shock in STEMI: Real-World Data for Early Risk Assessment and Prognostic Insights

by
Elena Stamate
1,†,
Anisia-Luiza Culea-Florescu
2,*,
Mihaela Miron
3,†,
Alin-Ionut Piraianu
1,*,
Adrian George Dumitrascu
4,
Iuliu Fulga
5,
Ana Fulga
6,
Octavian Stefan Patrascanu
7,
Doriana Iancu
7,
Octavian Catalin Ciobotaru
6 and
Oana Roxana Ciobotaru
8
1
Department of Morphological and Functional Sciences, Faculty of Medicine and Pharmacy, “Dunarea de Jos” University of Galati, 35, Al. I. Cuza Street, 800216 Galati, Romania
2
Department of Electronics and Telecommunications, “Dunarea de Jos” University of Galați, 800008 Galati, Romania
3
Department of Computer Science and Information Technology, “Dunarea de Jos” University of Galați, 800008 Galati, Romania
4
Division of Hospital Internal Medicine, Department of Medicine, Mayo Clinic Florida, 4500 San Pablo Rd S, Jacksonville, FL 32224, USA
5
Department of Medical, Faculty of Medicine and Pharmacy, “Dunarea de Jos” University of Galati, 35, Al. I. Cuza Street, 800216 Galati, Romania
6
Department of Clinical Surgical, Faculty of Medicine and Pharmacy, “Dunarea de Jos” University of Galati, 35, Al. I. Cuza Street, 800216 Galati, Romania
7
Faculty of Medicine and Pharmacy, Dunarea de Jos University of Galati, 35 AL Cuza St, 800010 Galati, Romania
8
Department of Clinical Medical, Faculty of Medicine and Pharmacy, “Dunarea de Jos” University of Galati, 35, Al. I. Cuza Street, 800216 Galati, Romania
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
J. Clin. Med. 2025, 14(11), 3698; https://doi.org/10.3390/jcm14113698
Submission received: 21 April 2025 / Revised: 19 May 2025 / Accepted: 21 May 2025 / Published: 25 May 2025
(This article belongs to the Section Cardiology)
Versions Notes

Abstract

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—prehospital, emergency department (ED), and cardiology-on-call—with 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—parameters 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.
Keywords: STEMI; cardiogenic shock; machine learning; early triage; angiography prioritization; predictive models STEMI; cardiogenic shock; machine learning; early triage; angiography prioritization; predictive models

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MDPI and ACS Style

Stamate, E.; Culea-Florescu, A.-L.; Miron, M.; Piraianu, A.-I.; Dumitrascu, A.G.; Fulga, I.; Fulga, A.; Patrascanu, O.S.; Iancu, D.; Ciobotaru, O.C.; et al. AI-Based Predictive Models for Cardiogenic Shock in STEMI: Real-World Data for Early Risk Assessment and Prognostic Insights. J. Clin. Med. 2025, 14, 3698. https://doi.org/10.3390/jcm14113698

AMA Style

Stamate E, Culea-Florescu A-L, Miron M, Piraianu A-I, Dumitrascu AG, Fulga I, Fulga A, Patrascanu OS, Iancu D, Ciobotaru OC, et al. AI-Based Predictive Models for Cardiogenic Shock in STEMI: Real-World Data for Early Risk Assessment and Prognostic Insights. Journal of Clinical Medicine. 2025; 14(11):3698. https://doi.org/10.3390/jcm14113698

Chicago/Turabian Style

Stamate, Elena, Anisia-Luiza Culea-Florescu, Mihaela Miron, Alin-Ionut Piraianu, Adrian George Dumitrascu, Iuliu Fulga, Ana Fulga, Octavian Stefan Patrascanu, Doriana Iancu, Octavian Catalin Ciobotaru, and et al. 2025. "AI-Based Predictive Models for Cardiogenic Shock in STEMI: Real-World Data for Early Risk Assessment and Prognostic Insights" Journal of Clinical Medicine 14, no. 11: 3698. https://doi.org/10.3390/jcm14113698

APA Style

Stamate, E., Culea-Florescu, A.-L., Miron, M., Piraianu, A.-I., Dumitrascu, A. G., Fulga, I., Fulga, A., Patrascanu, O. S., Iancu, D., Ciobotaru, O. C., & Ciobotaru, O. R. (2025). AI-Based Predictive Models for Cardiogenic Shock in STEMI: Real-World Data for Early Risk Assessment and Prognostic Insights. Journal of Clinical Medicine, 14(11), 3698. https://doi.org/10.3390/jcm14113698

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