Wang, Winston

Abstract: Cardiovascular disease (CVD) remains the leading cause of death worldwide. While advances in treatment have improved outcomes, the greatest gains in public health will come from primary prevention. Risk stratification is the foundation of CVD prevention. Traditional tools, such as pooled cohort equations (PCEs) and SCORE2, do not capture non-traditional risk factors and individual variability, driving efforts to refine existing approaches. We critically discuss emerging risk determinants for primary CVD prevention. Coronary artery calcium scoring can reclassify CVD risk but has limitations related to cost, access, and unvalidated improvements in clinical endpoints. Biomarkers such as apolipoprotein B and lipoprotein(a) may identify residual risk beyond LDL-C, particularly in patients with lipid discordance, but remain underused due to uncertain thresholds and the lack of prospective triallevel outcome data. Trials of colchicine in primary prevention have yielded mixed results, making the role of targeting inflammation unclear in CVD prevention. Polygenic risk scores can stratify genetic risk but face challenges in sensitivity, specificity, and generalizability. Multiomics approaches, while offering the promise of deeper phenotyping, lack established clinical applications and consensus on when their use is appropriate. Artificial intelligence may allow better prediction across cohorts but requires broader validation. Clonal hematopoiesis of indeterminate potential, a marker of inflammation and age-related CVD risk, lacks evidence supporting routine screening or intervention in younger, asymptomatic individuals. The proliferation of new risk stratification tools poses a key challenge: determining when additional data meaningfully alter clinical decisions. Until robust outcome-based evidence emerges, risk enhancers should guide, but not dictate, therapy in selected patients.

Background To compare the effectiveness of glucagon‐like peptide‐1 receptor agonists (GLP‐1 RAs) versus bariatric surgery on cardiovascular outcomes in patients with heart failure (HF) with preserved ejection fraction and obesity. Methods Using the TriNetX research network, we conducted a retrospective cohort study of adults (aged ≥18 years) with HF with preserved ejection fraction and obesity (body mass index >30 kg/m 2 ) from 2017 to 2022. Patients were categorized into 2 cohorts: those receiving GLP‐1 RAs (semaglutide or tirzepatide) and those who underwent bariatric surgery. Propensity score matching (1:1) was used to balance baseline characteristics. Primary outcomes included acute HF events and all‐cause hospitalizations. Secondary outcomes included all‐cause death, myocardial infarction, stroke, and follow‐up body mass index. Cox proportional hazard models were used to estimate hazard ratios (HRs). Results A total of 2747 patients were included per cohort (mean age, 68 years). GLP‐1 RA therapy was associated with lower incidence of acute HF events (38.9% versus 44.6%; HR, 0.78 [95% CI, 0.72–0.85]), all‐cause death (11.1% versus 14.8%; HR, 0.71 [95% CI, 0.61–0.82]), and all‐cause hospitalizations (66.4% versus 77.3%; HR, 0.62 [95% CI, 0.58–0.66]). Rates of myocardial infarction (13.8% versus 13.4%; HR 0.99 [0.86–1.14]) and stroke (9.7% versus 10.7%; HR, 0.87 [0.74–1.02]) were similar between groups. Mean body mass index at follow‐up was 38.0 in the GLP‐1 RA cohort versus 37.7 after bariatric surgery ( P =0.34). Hemoglobin A 1c at follow‐up was higher in the GLP‐1 RA group (7.4 versus 6.8; P <0.001). Conclusions In patients with HF with preserved ejection fraction and obesity, GLP‐1 RA therapy was associated with improved outcomes compared with bariatric surgery, supporting the need for prospective trials to evaluate GLP‐1 RAs as a therapeutic alternative.

Introduction: Complex congenital heart disease (CHD) patients with atrial arrhythmias have higher rates of morbidity and mortality compared to less complex CHD patients. This study aims to identify echocardiographic predictors of thromboembolism (TE) and major bleeding in patients with Tetralogy of Fallot (ToF) or Pulmonary Atresia with Intact Ventricular Septum (PA-IVS) and atrial fibrillation or flutter (AF). Methods: This cohort study included patients with ToF or PA-IVS who had a diagnosis of AF who underwent a TTE across three sites. Outcomes were incidence rate of major bleeding or TE, calculated using univariable and multivariable cox regression with interaction for anticoagulant choice and fractional area change (FAC). The HAS-BLED score was augmented with major predictors of bleeding, and receiver operator curves compared. Results: We included 300 patients (287 ToF [95.7%], 13 PA-IVS [4.3%]), mean age 45.6 years. Most received warfarin (227 [79.4%]), 40 (13%) used DOACs, and 5 (1.6%) used enoxaparin. TTE predictors of TE included large LVOT diameter ≥3cm (HR 8.92, p=0.002), small LVOT VTI (p=0.014), FAC <30% (HR 7.08, p=0.026) and moderate RV dysfunction (HR 10.75, p=0.026). Predictors of major bleeding included moderate tricuspid regurgitation (HR 2.40, p=0.022), right atrial pressure (1.12, p<0.001), severe RA enlargement (HR 2.30, p=0.048), RV mid diameter (HR 1.06, p=0.005), RV end diastolic area (HR 1.04, p=0.021), RV end systolic area (HR 1.05, p=0.016) elevated RVSP and FAC ≤45% (HR 0.37, p=0.016). Among warfarin users, impaired RV function (FAC ≤45%) was linked to reduced major bleeding events (HR 0.27, p=0.025). There was a nonsignificant trend towards higher bleeding in DOAC users with FAC >45% (HR 3.87, p=0.284), but significantly higher bleeding in DOAC users with FAC ≤45% (HR 12.41, p=0.008) vs. warfarin users with normal RV function (table 1). We developed an augmented HAS-BLED score: the standard score plus one point for moderate-to-severe pulmonic or tricuspid regurgitation and one for FAC ≤45%. This outperformed the standard HAS-BLED in predicting major bleeding (AUC 0.67 augmented vs. 0.57 un-augmented, p=0.008) (figure 1). Conclusion: Echocardiographic evidence of right heart disease is a strong predictor of major bleeding in patients with ToF or PA-IVS. FAC is a useful tool to determine excess major bleeding risk, particularly in DOAC users. Augmenting HAS-BLED with right heart parameters improves diagnostic accuracy.

Cardiac cachexia (CC) is a severe complication of advanced heart failure (HF), characterized by involuntary weight loss and muscle wasting, leading to poor outcomes and higher mortality. Despite its severity, CC remains under-recognized and undertreated, lacking targeted therapies specifically addressing its pathophysiology. Glucagon-like peptide-1 receptor agonists (GLP-1RAs), though beneficial in reducing cardiovascular risk in patients with HF, may exacerbate muscle wasting in cachectic patients, necessitating further investigation. Non-pharmacological strategies, including tailored nutritional support and exercise programs, have shown positive effects on body composition and quality of life in patients with CC. However, there remains a gap in recommendations tailored to preventive strategies and pharmacologic therapies for patients with CC and concomitant GLP-1RA use. This review highlights the multifactorial mechanisms underlying CC and current and emerging therapeutic approaches for mitigating HF-related sarcopenia while on GLP-1RAs.

Abstract Within COVID-19 there is an urgent unmet need to predict at the time of hospital admission which COVID-19 patients will recover from the disease, and how fast they recover to deliver personalized treatments and to properly allocate hospital resources so that healthcare systems do not become overwhelmed. To this end, we have combined clinically salient CT imaging data synergistically with laboratory testing data in an integrative machine learning model to predict organ-specific recovery of patients from COVID-19. We trained and validated our model in 285 patients on each separate major organ system impacted by COVID-19 including the renal, pulmonary, immune, cardiac, and hepatic systems. To greatly enhance the speed and utility of our model, we applied an artificial intelligence method to segment and classify regions on CT imaging, from which interpretable data could be directly fed into the predictive machine learning model for overall recovery. Across all organ systems we achieved validation set area under the receiver operator characteristic curve (AUC) values for organ-specific recovery ranging from 0.80 to 0.89, and significant overall recovery prediction in Kaplan-Meier analyses. This demonstrates that the synergistic use of an artificial intelligence (AI) framework applied to CT lung imaging and a machine learning model that integrates laboratory test data with imaging data can accurately predict the overall recovery of COVID-19 patients from baseline characteristics.