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

• Published in: Curēus (Palo Alto, CA) Vol. 15; no. 12; p. e50395
• Main Authors: Popat, Apurva, Yadav, Sweta, Patel, Sagar K, Baddevolu, Sasanka, Adusumilli, Susmitha, Rao Dasari, Nikitha, Sundarasetty, Manoj, Anand, Sunethra, Sankar, Jawahar, Jagtap, Yugandha G
• Format: Journal Article
• Language: English
• Published: United States Cureus Inc 12.12.2023; Cureus
• Subjects: Algorithms; Artificial intelligence; Cardiology; Clinical decision making; Cohort analysis; Data collection; Deep learning; Electronic health records; Healthcare Technology; Heart attacks; Heart failure; Hospitalization; Intensive care; Internal Medicine; Machine learning; Medical prognosis; Meta-analysis; Mortality; Stroke; Systematic review; United States > US; prediction model in medicine; artificial intelligence in cardiology; cardiogenic shock; artificial intelligence in medicine; machine learning in medicine
• ISSN: 2168-8184; 2168-8184
• DOI: 10.7759/cureus.50395

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.