Daily subcutaneous bulevirtide (BLV) 2 mg has been approved as first-line treatment for chronic hepatitis D (CHD), but long-term real-world adherence data are limited. We assessed adherence rates during BLV therapy and their impact on treatment response. HERACLIS_BLV_D study (NCT05928000) included adult CHD patients initiating BLV 2 mg/day and followed in routine clinical practice. Virological response (VR) was defined as HDV RNA < 57.5 IU/mL or decline > 2 log10 and biochemical response (BR) as normal ALT (≤ 40 IU/L). Adherence was evaluated through the national prescription system based on executed monthly BLV prescriptions. Treatment discontinuation was defined as no executed BLV prescription for > 3 months at the end of follow-up. Seventy-six patients were included. VR/BR rates were 73%/71% at 12 and 93%/74% at 24 months. Thirteen (17%) patients discontinued BLV (none due to adverse events); 6%-7% per year. Mean adherence among treated patients was 98% ± 6% in 1st year declining to 93% ± 13% in 2nd and 91% ± 17% in 3rd year (p ≤ 0.010). No baseline characteristic was associated with poor (< 90%) or good (≥ 90%) adherence to BLV therapy. Patients with poor vs. good adherence had lower VR rates (1st year: 33% vs. 77%, p = 0.038; 2nd year: 60% vs. 97%, p = 0.030). In conclusion, in clinical practice, < 10% of CHD patients discontinue BLV therapy annually. Adherence is excellent in the first year and remains > 90% until the third year although gradually declines. Poor (< 90%) adherence cannot be predicted but adversely affects the VR rates emphasizing the need for strategies to support long-term treatment retention.
Frailty and functional decline strongly influence treatment tolerance and outcomes in older patients with cancer, yet assessment is typically limited to infrequent clinic visits. We propose a multimodal wearable framework to estimate frailty-related functional change between visits in elderly breast cancer patients enrolled in the multicenter CARDIOCARE study. Free-living smartwatch physical activity and sleep features are combined with ECG-derived heart rate variability (HRV) features from a chest strap and organized into patient-horizon bags aligned to month 3 (M3) and month 6 (M6) follow-ups. Our innovation is an attention-based multiple instance learning (MIL) formulation that fuses irregular, multimodal wearable instances under real-world missingness and weak supervision. An attention-based MIL model with modality-specific multilayer perceptron (MLP) encoders with embedding dimension 128 aggregates variable-length and partially missing longitudinal instances to predict discretized change-from-baseline classes (worsened, stable, improved) for FACIT-F and handgrip strength. Under subject-independent leave-one-subject-out (LOSO) evaluation, the full multimodal model achieved balanced accuracy/F1 of 0.68 +/- 0.08/0.67 +/- 0.09 at M3 and 0.70 +/- 0.10/0.69 +/- 0.08 at M6 for handgrip, and 0.59 +/- 0.04/0.58 +/- 0.06 at M3 and 0.64 +/- 0.05/0.63 +/- 0.07 at M6 for FACIT-F. Ablation results indicated that smartwatch activity and sleep provide the strongest predictive information for frailty-related functional changes, while HRV contributes complementary information when fused with smartwatch streams.
Psychological stress is clinically relevant in cardio-oncology, yet it is typically assessed only through patient-reported outcome measures (PROMs) and is rarely integrated into continuous cardiotoxicity surveillance. We estimate perceived stress in an elderly, multicenter breast cancer cohort (CARDIOCARE) using multimodal wearable data from a smartwatch (physical activity and sleep) and a chest-worn ECG sensor. Wearable streams are transformed into heterogeneous visual representations, yielding a weakly supervised setting in which a single Perceived Stress Scale (PSS) score corresponds to many unlabeled windows. A lightweight pretrained mixture-of-experts backbone (Tiny-BioMoE) embeds each representation into 192-dimensional vectors, which are aggregated via attention-based multiple instance learning (MIL) to predict PSS at month 3 (M3) and month 6 (M6). Under leave-one-subject-out (LOSO) evaluation, predictions showed moderate agreement with questionnaire scores (M3: R^2=0.24, Pearson r=0.42, Spearman rho=0.48; M6: R^2=0.28, Pearson r=0.49, Spearman rho=0.52), with global RMSE/MAE of 6.62/6.07 at M3 and 6.13/5.54 at M6.
Healthcare systems are responsible for a significant share of global greenhouse gas emissions, and cardiac catheterization laboratories (CCLs) are among the most resource-intensive hospital units due to their reliance on single-use materials, high-energy imaging systems, and strict infection-control protocols. Understanding and mitigating their environmental impact is essential for achieving sustainable cardiovascular care. The aim of this review was to summarize and analyze available quantitative evidence on waste generation, recycling potential, and carbon emissions in interventional cardiology, and to identify practical strategies for transitioning toward a "green cath lab." A structured literature search was conducted across PubMed, Scopus, and Web of Science for the period 2015-2025, supplemented by Google Scholar and the cardiovascular society websites. Studies were eligible if they provided quantitative data on waste output, recycling rates, or carbon footprint in CCLs. Out of 357 retrieved records, only four met all inclusion criteria. Diagnostic coronary angiography produced an average carbon footprint of 12.5 kg CO₂-equivalent per case, with single-use disposables (40%) and imaging energy (25%) as the largest contributors. Waste audits showed 1.5-3 kg of waste per procedure, 36%-40% of which was recyclable. Optimizing procedural packs, improving heating, ventilation, and air conditioning or HVAC efficiency, and structured waste segregation achieved 20%-30% reductions in waste and emissions without affecting safety. Evidence indicates that interventional cardiology generates measurable and avoidable environmental burdens. Implementing targeted, low-cost interventions can markedly reduce both waste and carbon footprint. These findings establish a quantitative foundation for sustainability frameworks, carbon auditing, and policy initiatives toward a net-zero cath lab.
Background: Prompt initiation of lipid-lowering therapy (LLT) following acute coronary syndrome (ACS) is crucial for preventing secondary cardiovascular events. However, there are gaps in clinical implementation of the 2019 ESC/EAS guideline-recommended low-density lipoprotein cholesterol (LDL-C) goal of <55 mg/dL in patients post-ACS. Methods: This multicenter, real-world, retrospective, 12-month study of adult patients in Greece hospitalized for ACS from September 2019 to November 2022 assessed the attainment of target LDL-C (<55 mg/dL) during the first year post-ACS. Eligible patients had elevated LDL-C at hospitalization (>130 mg/dL if LLT naïve; >100 mg/dL if on statin monotherapy; >70 mg/dL if on a statin plus ezetimibe) and ≥1 LDL-C measurement within 12 months post-ACS. Results: Overall, 212 eligible patients of mean (SD) age 59.9 (±11.1) years were enrolled. Type 2 diabetes and hypertension were reported in 19.8 % (42/212) and 50.9 % (108/212) of patients, respectively. Median (Q1, Q3) LDL-C was 138.0 (106.5, 158.0) mg/dL at hospitalization (n = 212). In patients with LDL-C availability at 12 months posthospitalization (n = 197), median (Q1, Q3) LDL-C was 64.0 (53.0, 76.0) mg/dL, with 27.9 % of patients (55/197) attaining LDL-C <55 mg/dL. Although 73.9 % of patients (199/212) were discharged from the hospital on statin monotherapy, 50 % of patients (106/212) were receiving statin-ezetimibe LLT and 1.4 % (3/212) were receiving statin-ezetimibe-PCSK9 inhibitor LLT 12 months posthospitalization. Conclusion: LDL-C goal attainment is suboptimal in the first year after ACS hospitalization in Greece, indicating an unmet need to improve the treatment of patients with hypercholesterolemia during the post-ACS period by optimizing lipid management through earlier LLT intensification.
