Artificial intelligence in age-related cardiogenic shock prediction: a systematic review and meta-analysis

Cardiogenic shock is a life-threatening condition that significantly impacts the mortality of patients suffering from acute myocardial infarction and heart failure. Despite advancements in medical care, early identification of cardiogenic shock remains a critical challenge, with delayed diagnosis contributing to poor outcomes. Recent developments in artificial intelligence and machine learninghave provided innovative solutions for predictive medicine, offering new opportunities to enhance early cardiogenic shock detection and intervention. This mini-review highlights the application of machine learning models, particularly in the context of artificial intelligence in age-related cardiogenic shock prediction, for predicting cardiogenic shock in hospitalized patients with acute myocardial infarction and heart failure. A thorough search of the PubMed, Web of Science, Cochrane, and Scopus databases was conducted from inception to November 2, 2023, to find relevant studies. The studies analyzed were drawn from various cohorts, focusing on the predictive accuracy of machine learning algorithms in clinical settings. Six studies were included in our review, including four retrospective studies and two prospective cohort studies. The results revealed that machine learning models achieved a pooled mean area under the curve of 0.808 (95% confidence interval: 0.727, 0.890), indicating reliable performance. The area under the curve in the included studies ranged from 0.77 to 0.91. Machine learning models performed well, with accuracy ranging from 0.88 to 0.93 and sensitivity and specificity of 58%–78% and 88%–93%, respectively. Key predictive variables across studies included age, blood pressure, heart rate, oxygen saturation, and blood glucose—all of which were routinely measured in hospital settings. These findings suggest that machine learning can support clinicians by automating risk assessment and providing early alerts, leading to better patient stratification and timely interventions. The integration of machine learning into clinical workflows can bridge the gap between data analysis and decision-making, potentially reducing the workload on medical professionals and enhancing patient outcomes. The use of machine learning models for early cardiogenic shock prediction represents a promising development in cardiology, harnessing artificial intelligence to improve diagnostic precision and facilitate proactive care. Continued research and validation are necessary to refine these models, optimize data management, and expand their applicability. By overcoming current limitations, machine learning can be seamlessly integrated into healthcare to support early detection and improve survival rates in patients at risk for cardiogenic shock.