Data processing pipeline for cardiogenic shock prediction using machine learning

Revolutionizing Cardiology through Artificial Intelligence-Big Data from Proactive Prevention to Precise Diagnostics and Cutting-Edge Treatment-A Comprehensive Review of the Past 5 Years

BACKGROUND

Artificial intelligence (AI) can radically change almost every aspect of the human experience. In the medical field, there are numerous applications of AI and subsequently, in a relatively short time, significant progress has been made. Cardiology is not immune to this trend, this fact being supported by the exponential increase in the number of publications in which the algorithms play an important role in data analysis, pattern discovery, identification of anomalies, and therapeutic decision making. Furthermore, with technological development, there have appeared new models of machine learning (ML) and deep learning (DP) that are capable of exploring various applications of AI in cardiology, including areas such as prevention, cardiovascular imaging, electrophysiology, interventional cardiology, and many others. In this sense, the present article aims to provide a general vision of the current state of AI use in cardiology.

RESULTS

We identified and included a subset of 200 papers directly relevant to the current research covering a wide range of applications. Thus, this paper presents AI applications in cardiovascular imaging, arithmology, clinical or emergency cardiology, cardiovascular prevention, and interventional procedures in a summarized manner. Recent studies from the highly scientific literature demonstrate the feasibility and advantages of using AI in different branches of cardiology.

CONCLUSIONS

The integration of AI in cardiology offers promising perspectives for increasing accuracy by decreasing the error rate and increasing efficiency in cardiovascular practice. From predicting the risk of sudden death or the ability to respond to cardiac resynchronization therapy to the diagnosis of pulmonary embolism or the early detection of valvular diseases, AI algorithms have shown their potential to mitigate human error and provide feasible solutions. At the same time, limits imposed by the small samples studied are highlighted alongside the challenges presented by ethical implementation; these relate to legal implications regarding responsibility and decision making processes, ensuring patient confidentiality and data security. All these constitute future research directions that will allow the integration of AI in the progress of cardiology.

Novel Medical Treatments and Devices for the Management of Heart Failure with Reduced Ejection Fraction

Heart failure (HF) is a growing issue in developed countries; it is often the result of underlying processes such as ischemia, hypertension, infiltrative diseases or even genetic abnormalities. The great majority of the affected patients present a reduced ejection fraction (≤40%), thereby falling under the name of "heart failure with reduced ejection fraction" (HFrEF). This condition represents a major threat for patients: it significantly affects life quality and carries an enormous burden on the whole healthcare system due to its high management costs. In the last decade, new medical treatments and devices have been developed in order to reduce HF hospitalizations and improve prognosis while reducing the overall mortality rate. Pharmacological therapy has significantly changed our perspective of this disease thanks to its ability of restoring ventricular function and reducing symptom severity, even in some dramatic contexts with an extensively diseased myocardium. Notably, medical therapy can sometimes be ineffective, and a tailored integration with device technologies is of pivotal importance. Not by chance, in recent years, cardiac implantable devices witnessed a significant improvement, thereby providing an irreplaceable resource for the management of HF. Some devices have the ability of assessing (CardioMEMS) or treating (ultrafiltration) fluid retention, while others recognize and treat life-threatening arrhythmias, even for a limited time frame (wearable cardioverter defibrillator). The present review article gives a comprehensive overview of the most recent and important findings that need to be considered in patients affected by HFrEF. Both novel medical treatments and devices are presented and discussed.

Artificial Intelligence in the Early Prediction of Cardiogenic Shock in Acute Heart Failure or Myocardial Infarction Patients: A Systematic Review and Meta-Analysis

Cardiogenic shock (CS) may have a negative impact on mortality in patients with heart failure (HF) or acute myocardial infarction (AMI). Early prediction of CS can result in improved survival. Artificial intelligence (AI) through machine learning (ML) models have shown promise in predictive medicine. Here, we conduct a systematic review and meta-analysis to assess the effectiveness of these models in the early prediction of CS. A thorough search of the PubMed, Web of Science, Cochrane, and Scopus databases was conducted from the time of inception until November 2, 2023, to find relevant studies. Our outcomes were area under the curve (AUC), the sensitivity and specificity of the ML model, the accuracy of the ML model, and the predictor variables that had the most impact in predicting CS. Comprehensive Meta-Analysis (CMA) Version 3.0 was used to conduct the meta-analysis. Six studies were considered in our study. The pooled mean AUC was 0.808 (95% confidence interval: 0.727, 0.890). The AUC in the included studies ranged from 0.77 to 0.91. ML models performed well, with accuracy ranging from 0.88 to 0.93 and sensitivity and specificity of 58%-78% and 88%-93%, respectively. Age, blood pressure, heart rate, oxygen saturation, and blood glucose were the most significant variables required by ML models to acquire their outputs. In conclusion, AI has the potential for early prediction of CS, which may lead to a decrease in the high mortality rate associated with it. Future studies are needed to confirm the results.