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| Format: | Recurso digital |
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Zenodo
2026
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| Online Access: | https://doi.org/10.5281/zenodo.18520384 |
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Table of Contents:
- <p>Cardiogenic shock requiring mechanical circulatory support (MCS) remains associated with substantial morbidity and mortality despite significant technological and therapeutic advances. Determining the optimal timing and likelihood of successful weaning from MCS remains one of the most critical and complex clinical challenges in the management of these patients. Current decision-making strategies rely heavily on clinician experience and heterogeneous hemodynamic and imaging parameters, often lacking standardized, validated tools to guide weaning readiness and predict outcomes reliably.</p> <p> </p> <p>This study proposes a novel, multidimensional predictive scoring system designed to stratify the probability of successful MCS weaning in patients with cardiogenic shock. The scoring model integrates clinical, hemodynamic, biochemical, and advanced imaging variables, including markers of myocardial recovery, end-organ perfusion, inflammatory response, and ventricular-arterial coupling. Machine learning–assisted variable selection and multivariable regression modeling were utilized to identify independent predictors of sustained hemodynamic stability following MCS withdrawal.</p> <p> </p> <p>The predictive score was derived and internally validated in a multicenter cohort of patients supported with contemporary MCS devices, including venoarterial extracorporeal membrane oxygenation and percutaneous ventricular assist devices. The model demonstrated robust discrimination and calibration in predicting successful device explantation without recurrence of shock, need for re-escalation of mechanical support, urgent transplantation, or short-term mortality. Furthermore, risk stratification using the proposed score may facilitate personalized weaning strategies, optimize resource utilization, and enhance multidisciplinary decision-making.</p> <p> </p> <p>This novel predictive framework offers a standardized and clinically applicable tool for guiding MCS weaning decisions in cardiogenic shock patients. Prospective external validation and integration with real-time hemodynamic monitoring and artificial intelligence platforms may further refine individualized care pathways and improve survival outcomes in this critically ill population.</p>