BACKGROUND
Tricuspid transcatheter edge-to-edge repair (T-TEER) is an effective treatment for severe tricuspid regurgitation (TR), but predictors of outcome and clinical benefit after T-TEER remain limited.
METHODS
In 144 patients with severe symptomatic TR undergoing T-TEER, left and right ventricular stroke work indices (LVSWI, RVSWI) were calculated from invasive hemodynamics by right heart catheterization. Patients were stratified by median values (LVSWI 27 cJ/m2, RVSWI 6 cJ/m2) into four subgroups. The primary endpoint was one-year all-cause mortality.
RESULTS
After T-TEER, all-cause mortality was 28%. Outcomes differed significantly across subgroups: LVSWI high, RVSWI high: 15%, LVSWI high, RVSWI low: 15%, LVSWI low, RVSWI high: 55%, and LVSWI low, RVSWI low: 31% (Kaplan-Meier, log-rank p = 0.00027). Patients with low LVSWI but high RVSWI had the poorest survival, characterized by elevated pulmonary artery pressures, pulmonary capillary wedge pressure, and left ventricular transmural pressure. In univariate Cox regression, LVSWI below the median predicted mortality (≤ 27 cJ/m2; HR 4.73, 95% CI 2.07-10.8; p < 0.001), whereas RVSWI did not. LVSWI remained an independent predictor after adjusting in multivariate analysis (HR 0.44, 95% CI 0.27-0.71; p < 0.001).
CONCLUSION
LVSWI is a robust, independent prognostic marker after T-TEER, whereas RVSWI alone lacks predictive value. A discordant profile of low LVSWI with high RVSWI identifies a particularly high-risk subgroup with >50% mortality within one year. Incorporating stroke work indices into pre-procedural assessment may refine risk stratification and optimize management strategies in T-TEER candidates.
U. Hanses, Kathrin Diehl, Shiyar Alo et al.· Canadian Journal of Cardiolo...· 0 citations
Abstract Background Synthetic data hold substantial potential to address practical challenges in epidemiology due to restricted data access and privacy concerns. However, many current methods suffer from limited quality, high computational demands, and complexity for non-experts. Furthermore, common evaluation strategies for synthetic data often fail to directly reflect statistical utility and measure privacy risks sufficiently. Against this background, a critical underexplored question is whether synthetic data can reliably reproduce key findings from epidemiological research while preserving privacy. Methods We propose adversarial random forests (ARF) as an efficient and convenient method for synthesizing tabular epidemiological data. To evaluate its performance, we replicated statistical analyses from six epidemiological publications covering blood pressure, anthropometry, myocardial infarction, accelerometry, loneliness, and diabetes, from the German National Cohort (NAKO Gesundheitsstudie), the Bremen STEMI Registry U45 Study, and the Guelph Family Health Study. We further assessed how dataset dimensionality and variable complexity affect the quality of synthetic data, and contextualized ARF’s performance by comparison with commonly used tabular data synthesizers in terms of utility, privacy, generalization, and runtime. Results Across all replicated studies, results on ARF-generated synthetic data consistently aligned with original findings. Even for datasets with relatively low sample size-to-dimensionality ratios, replication outcomes closely matched the original results across descriptive and inferential analyses. Reduced dimensionality and variable complexity further enhanced synthesis quality. ARF demonstrated favourable performance regarding utility, privacy preservation, and generalization relative to other synthesizers and superior computational efficiency. Conclusions In summary, ARF reliably generates high-quality synthetic data that replicate diverse epidemiological analyses while offering a competitive privacy–utility trade-off.
J. Kapar, Kathrin Günther, L. Vallis et al.· International Journal of Epi...· 1 citation
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