Edwards Lifesciences (United States)

To examine the determinants of unmet transcatheter aortic valve replacement (TAVR) needs and their impact on patient survival among Medicare beneficiaries with aortic stenosis. We developed a county-level mismatch score measuring the gap between actual TAVR procedures performed and expected need based on population differences. Counties were classified as metropolitan, semiurban, or rural. Factors associated with larger mismatches were identified, and mortality rates among aortic stenosis (AS) patients were examined in relation to mismatch scores. We analyzed Medicare data from 2016 to 2022 across 3129 US counties. The mismatch score was developed to account for population differences and county urbanicity classification. Statistical analyses identified factors associated with TAVR mismatch and its relationship to mortality outcomes. We found substantial geographic variation in TAVR delivery. Counties with higher TAVR mismatch scores showed associations with fewer TAVR-providing hospitals, less market concentration, higher AS prevalence, and lower household incomes. Counties with greater gaps between needed and actual TAVR procedures were also associated with higher mortality rates. This relationship between mismatch and mortality was particularly strong in semiurban counties. Our findings identify associations between TAVR access gaps and patient outcomes, as well as factors linked to these access patterns. Counties with higher TAVR mismatch scores showed correlations with healthcare capacity constraints, geographic location, and socioeconomic factors. These associations suggest that mismatches may be addressed through targeted approaches based on local needs to improve care delivery for patients with AS in regions currently experiencing access challenges.

ABSTRACTPurposePulmonary hypertension (PH) is a heterogeneous disease with patient‐specific variability and vessel‐specific remodeling, which eventually lead to right ventricular (RV) failure. The gold standard for RV assessment—pressure–volume (PV) loop acquisition—is invasive and limited to specialized settings. This study aims to develop a patient‐specific lumped‐parameter model that quantifies vessel‐specific remodeling and simulates RV PV loops across PH phenotypes using routine clinical data.MethodsA lumped‐parameter model was calibrated using right heart catheterization and echocardiography data. Model agreement was assessed by R2 values for pressure and flow goodness‐of‐fit, and model‐derived hemodynamic metrics were compared with clinical values. A dimensionality reduction approach was applied to investigate how well different PH phenotypes could be separated.ResultsAcross the cohort, the lumped‐parameter model showed good agreement with clinical data. Model‐derived vessel‐specific (pulmonary arterial, capillary, venular) parameters highlighted physiological distinctions among phenotypes. Predicted RV PV loops revealed phenotype‐specific differences in right ventricular volumes, pressures, and stroke work. The linear discriminant analysis (LDA) demonstrated qualitative separability, indicating that model‐derived, nonmeasurable features offer additional discriminatory information.ConclusionOur results demonstrate that lumped‐parameter models can be calibrated to clinical data to quantify vessel‐specific remodeling and simulate RV pressure–volume dynamics to provide useful information for distinguishing among different PH phenotypes. This underscores the potential of computational models as noninvasive, clinically feasible tools for assessing in‐depth pulmonary vascular and RV function in PH.

BackgroundA classification system describing clinical presentation before aortic valve replacement (AVR) has recently been proposed (stable valve syndrome [SVS]), mildly symptomatic (progressive valve syndrome), or with acute and severe signs and symptoms (acute valve syndrome [AVS]). We aimed to evaluate the clinical impact, health care costs, and resource use associated with the mode of clinical presentation at the time of AVR among patients presenting with aortic stenosis.MethodsUsing the Market Clarity database (2017–2023), patients >18 years old with aortic stenosis and undergoing AVR (transcatheter or surgical) were included. Patients were categorized as SVS, progressive valve syndrome, or AVS based on their clinical presentation during the 1 year before AVR. All‐cause death, heart failure hospitalization, total health care costs (index hospitalization plus 1‐year follow‐up), and resource use were compared across clinical presentation groups using adjusted regression models.ResultsOf the 24 075 patients undergoing AVR for aortic stenosis, 270 (1.1%) had SVS, 10 195 (42.3%) progressive valve syndrome, and 13 610 (56.5%) AVS. Compared with SVS, AVS presentation was associated with increased risk of 1‐year death (adjusted hazard ratio [aHR], 2.93 [95% CI, 1.1–7.8]; P=0.03) and heart failure hospitalization (aHR, 4.15 [95% CI, 1.6–11.1]; P<0.01) after AVR. The total costs of AVR and 1‐year follow‐up were significantly higher in both the progressive valve syndrome (difference $27 410 [$13 507–$41 314]) and AVS groups (difference $36 267 [$22 302–$50 232]) compared with patients with SVS.ConclusionsAVS was frequent among patients undergoing AVR and was associated with increase in death, heart failure hospitalization, health care costs, and resource use 1 year after AVR, and SVS presentation was associated with the best clinical and economic outcomes.

The U.S. retail sector, a $7 trillion economic engine, is undergoing rapid transformation fueled by digital commerce, shifting consumer expectations, and intensified competition. At the heart of this shift lies the imperative to modernize customer service infrastructure. This paper explores how Salesforce Service Cloud—a leading Customer Relationship Management (CRM) platform—can be strategically deployed to optimize service delivery, reduce churn, and support omnichannel responsiveness across the retail value chain. We begin by contextualizing the urgency of service innovation, drawing on recent data showing that U.S. companies lose over $75 billion annually due to poor customer experiences. We then examine Salesforce Service Cloud’s capabilities, including AI-driven case management, real-time analytics, automation, and integration with e-commerce platforms. Through use-case illustrations and performance metrics, we demonstrate how these features enable faster resolution times, improved customer satisfaction, and more efficient use of the retail workforce. Importantly, we highlight the platform’s role in enhancing employment efficiency for over 2.7 million Americans working in customer-facing roles, enabling frontline staff to shift from routine inquiries to high-value engagement. Finally, we position CRM modernization within the broader context of U.S. economic priorities, referencing Executive Order 14058 and other federal mandates that call for customer experience transformation. Our findings support the conclusion that Service Cloud is more than a software solution—it is critical infrastructure for ensuring that the U.S. retail sector remains competitive, resilient, and aligned with the digital economy of the future. Keywords: Customer Experience, Salesforce, Retail Sector, Digital Commerce.