Early detection of atrial fibrillation (AFib) is crucial for altering its natural progression and complication profile. Traditional demographic and lifestyle factors often fail as predictors of AFib. This study investigated pre-operative, circulating microRNAs (miRNAs) as potential biomarkers for post-operative AFib (POAF) in patients undergoing coronary artery bypass grafting (CABG). We used an array polymerase chain reaction method to detect pre-operative, circulating miRNAs in seven patients who subsequently developed POAF after CABG (cases) and eight patients who did not develop POAF after CABG (controls). The top 10 miRNAs from 84 candidates were selected and assessed for their performance in predicting POAF using machine learning models, including Random Forest, K-Nearest Neighbors (KNN), XGBoost, and Support Vector Machine (SVM). The Random Forest and XGBoost models showed superior predictive performance, with test area under the curve (AUC) values of 0.76 and 0.83, respectively. Differential expression analysis revealed four upregulated miRNAs—hsa-miR-96-5p, hsa-miR-184, hsa-miR-17-3p, and hsa-miR-200-3p—that overlapped with the POAF-miRNA signature. The POAF-miRNA signature was significantly associated with various cardiovascular diseases, including acute myocardial infarction, hypertrophic cardiomyopathy, and heart failure. Biological pathway analysis indicated these miRNAs target key signaling pathways involved in cardiovascular pathology, such as the MAPK, PI3K-Akt, and TGF-beta signaling pathways. The identified miRNAs demonstrate significant potential as predictive biomarkers for AFib post-CABG, implicating critical cardiovascular pathways and highlighting their role in POAF development and progression. These findings suggest that miRNA signatures could enhance predictive accuracy for POAF, offering a novel, noninvasive approach to early detection and personalized management of this condition.

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.

Atrial fibrillation (AF) carries a stroke risk, often necessitating anticoagulation, especially in patients with risk factors. With the advent of implantable and wearable heart monitors, episodes of short bouts of atrial arrhythmias called atrial high-rate episodes (AHREs) or subclinical AF (SCAF) are commonly identified. The necessity of anticoagulation in patients with SCAF is unclear. However, recent randomized controlled trials, the NOAH-AFNET 6 and ARTESIA, have offered insights into this matter. Furthermore, a study-level meta-analysis combining data from both these trials has provided more detailed information. Reviewing the information thus far, we can conclude that DOACs can result in a notable reduction in the risk of ischemic stroke and can potentially decrease the risk of debilitating stroke, albeit with an increased risk of major bleeding. Thus, informed, shared decision-making is essential, weighing the potential benefits of stroke prevention against the risk of major bleeding when considering anticoagulation in this patient population.