Will Dynamic Evaluation of Cardiogenic Shock Using Machine Learning Models Lead to Improved Survival?

Area of Science:

• Cardiology
• Medical Informatics
• Artificial Intelligence

Background:

• Cardiogenic shock (CS) is a severe complication of acute myocardial infarction (AMI) with high mortality.
• Current mechanical circulatory support (MCS) has not consistently improved survival, highlighting diagnostic delays.
• Recognizing CS at a reversible stage is critical for effective intervention.

Purpose of the Study:

• To review the pathophysiology of CS and limitations in current diagnostic methods.
• To explore challenges in conducting clinical trials for CS interventions.
• To investigate the potential of AI for early CS diagnosis and intervention.

Main Methods:

• Literature review focusing on CS pathophysiology, diagnosis, and treatment.
• Analysis of current diagnostic approaches and their limitations.
• Exploration of AI applications in acute care and decision support systems.

Main Results:

• CS diagnosis is often delayed due to its heterogeneity and reliance on complex hemodynamic and biomarker interpretation.
• AI-driven decision support systems show potential for early detection of patient deterioration.
• Integrated AI systems can dynamically phenotype patients and support clinical decision-making.

Conclusions:

• Early and accurate diagnosis of CS is essential to reduce mortality.
• AI-based approaches hold significant promise for improving the timely diagnosis and management of CS.
• Further research into integrated AI systems can enhance clinical practice and patient outcomes.