Inflammatory bowel disease (IBD), including Crohn’s disease (CD) and ulcerative colitis (UC), is a prevalent condition associated with chronic noninfectious inflammation of the gastrointestinal tract. It has been hypothesized that chronic inflammation can predispose patients to atrial fibrillation (AF), however, no clear evidence exists to support this. A systematic literature search was conducted using major databases aimed at studies focusing on AF development in patients with IBD. Further subgroup analyses were performed for ulcerative colitis (UC) and crohn’s disease (CD). Risk ratios (RR) with their corresponding 95 % confidence intervals (CI) were pooled using a random-effects model in the Review Manager Software. Statistical significance was set at p < 0.05. Seven studies with 88,893,407 patients were included (1,002,719 and 87, 890, 688 patients in the IBD and non-IBD groups, respectively). IBD patients were at an increased risk of developing AF [RR: 1.52; 95 % CI: 1.19–1.95; p = 0.0009] compared to the non-IBD group. In subgroup analyses, patients with UC were at an increased risk of developing AF [RR: 1.29; 95 % CI: 1.08–1.53; p = 0.004], as were CD patients [RR: 1.30; 95 % CI: 1.07–1.58; p = 0.008] compared to the non-UC and non-CD groups, respectively. Patients with IBD are at nearly 1.5 times the risk of developing AF compared to the non-IBD population. Our meta-analysis was limited by heterogeneity among the studies, highlighting the importance of further large-scale prospective studies to establish more robust evidence.

Infective endocarditis (IE) is a severe infection of the inner lining of the heart, known as the endocardium. It is characterized by a range of symptoms and has a complicated pattern of occurrence, leading to a significant number of deaths. IE poses significant diagnostic and treatment difficulties. This evaluation examines the utilization of artificial intelligence (AI) and machine learning (ML) models in addressing information extraction (IE) management. It focuses on the most recent advancements and possible applications. Through this paper, we observe that AI/ML can significantly enhance and outperform traditional diagnostic methods leading to more accurate risk stratification, personalized therapies as well and real-time monitoring facilities. For example, early postsurgical mortality prediction models like SYSUPMIE achieved ‘very good’ area under the curve (AUROC) values exceeding 0.81. Additionally, AI/ML has improved diagnostic accuracy for prosthetic valve endocarditis, with PET-ML models increasing sensitivity from 59% to 72% when integrated into ESC criteria and reaching a high specificity of 83%. Furthermore, inflammatory biomarkers such as IL-15 and CCL4 have been identified as predictive markers, showing 91% accuracy in forecasting mortality, and identifying high-risk patients with specific CRP, IL-15, and CCL4 levels. Even simpler ML models, like Naïve Bayes, demonstrated an excellent accuracy of 92.30% in death rate prediction following valvular surgery for IE patients. Furthermore, this review provides a vital assessment of the advantages and disadvantages of such AI/ML models, such as better-quality decision support approaches like adaptive response systems on one hand, and data privacy threats or ethical concerns on the other hand. In conclusion, Al and ML must continue, through multi-centric and validated research, to advance cardiovascular medicine, and overcome implementation challenges to boost patient outcomes and healthcare delivery. Export

Background: Gastric outlet obstruction (GOO) refers to mechanical obstruction at the level of the gastric outlet and is associated with significantly impacted quality of life and mortality. Duodenal stenting (DS) offers a minimally invasive approach to managing GOO but is associated with a high risk of stent obstruction. Endoscopic ultrasound-guided gastroenterostomy (EUS-GE) is a novel intervention that uses lumen-apposing metal stents to open the restricted lumen. The current evidence comparing EUS-GE to DS is limited and inconsistent. Methods: We conducted a systematic literature search on PubMed, Embase, Cochrane, Scopus, and clinicaltrials.gov to retrieve studies comparing EUS-GE to DS for GOO. Odds ratios (OR) and mean differences (MD) with their 95% confidence intervals (CI) were pooled using the DerSimonian-Laird inverse variance random-effects model. Statistical significance was set at P < .05. Results: Ten studies with a total of 1275 GOO patients (585: EUS-GE and 690: DS) were included. EUS-GE was associated with statistically significant higher clinical success [OR: 2.52; 95% CI: 1.64, 3.86; P < .001], lower re-intervention rate [OR: 0.12; 95% CI: 0.06, 0.22; P < .00001], longer procedural time [MD: 20.91; 95% CI: 15.48, 26.35; P < .00001], and lower risk of adverse events [OR: 0.49; 95% CI: 0.29, 0.82; P = .007] than DS. Technical success [OR: 0.62; 95% CI: 0.31, 1.25] and the length of hospital stay [MD: −2.12; 95% CI: −5.23, 0.98] were comparable between the 2 groups. Conclusion: EUS-GE is associated with higher clinical success, longer total procedural time, lower re-intervention rate, and lower risk of adverse events than DS. Technical success and the length of hospital stay were comparable between the 2 groups. EUS-GE appears to be a safe and effective procedure for managing GOO. Further large, multicentric randomized controlled trials are warranted to investigate the safety and outcomes of EUS-GE in patients with malignant GOO.

Patients with congenital heart disease (CHD) are more likely to experience ischemic and hemorrhagic stroke due to factors such as arrhythmias, residual shunts and related cardiovascular complications. However, guidelines for identifying CHD patients at the highest risk of stroke remain unclear. In this study, we aimed to evaluate the risk of developing stroke in patients with CHD. A systematic literature search was performed on PubMed, Scopus, Cochrane, and Embase databases to retrieve studies that evaluated stroke risk in patients with CHD. Random effects model was used to pool the hazard ratios (HR) with 95% confidence intervals (CI). Subgroup analysis was conducted on age, type of stroke, type of study and region. Publication bias was assessed by Egger’s regression test. Statistical significance was set at p < 0.05. All the analysis was performed using R studio V4.3.1. Eleven studies (5,490,412 participants) were included in this systematic review and meta-analysis. Patients with CHD were at a higher risk of stroke [Pooled HR: 3.25; 95% CI: 2.25, 4.68; p < 0.01; I2: 100%] than the control group. In subgroup analysis, patients with CHD were at a higher risk of ischemic stroke [Pooled HR: 4.45; 95% CI: 2.24, 8.85; p < 0.01; I2: 100%] and hemorrhagic stroke [Pooled HR: 4.70; 95% CI: 1.70, 12.96; p < 0.01; I2: 99%] than the control group. Our meta-analysis indicates a significantly increased stroke risk in patients with CHD. Subgroup analyses showed higher stroke risk in European regions compared to Asia and USA, and among adults compared to pediatric populations. Future studies should focus on addressing regional and data limitations to better inform clinical strategies for managing stroke risk in CHD patients.

Background: Non-alcoholic fatty liver disease (NAFLD) is estimated to affect approximately 25% of the global population. Both, coronary artery disease and NAFLD are linked to underlying insulin resistance and inflammation as drivers of the disease. Coronary flow reserve parameters, including coronary flow reserve velocity (CFRV), baseline diastolic peak flow velocity (DPFV), and hyperemic DPFV, are noninvasive markers of coronary microvascular circulation. The existing literature contains conflicting findings regarding these parameters in NAFLD patients. Methods: A comprehensive systematic search was conducted on major electronic databases from inception until May 8, 2024, to identify relevant studies. We pooled the standardized mean differences (SMD) with 95% confidence intervals (CI) using the inverse-variance random-effects model. Statistical significance was set at P < .05. Results: Four studies with 1139 participants (226 with NAFLD and 913 as controls) were included. NAFLD was associated with a significantly lower CFRV (SMD: −0.77; 95% CI: −1.19, −0.36; P < .0002) and hyperemic DPFV (SMD: −0.73; 95% CI: −1.03, −0.44; P < .00001) than the controls. NAFLD demonstrated a statistically insignificant trend toward a reduction in baseline DPFV (SMD: −0.09; 95% CI: −0.38, 0.19; P = .52) compared to healthy controls. Conclusion: Patients with NAFLD are at a higher risk of coronary microvascular dysfunction, as demonstrated by reduced CFRV and hyperemic DPFV. The presence of abnormal coronary flow reserve in patients with NAFLD provides insights into the higher rates of cardiovascular disease in these patients. Early aggressive targeted interventions for impaired coronary flow reserve in subjects with NAFLD may lead to improvement in clinical outcomes.