Background: Transcatheter Aortic Valve Implantation (TAVI) procedures are rapidly expanding, necessitating a more extensive stratification of patients with aortic stenosis. Especially in the high-risk group, some patients fail to derive optimal or any benefits from TAVI, leading to the risk of futile interventions. Despite consensus among several experts regarding the importance of recognizing and anticipating such interventions, the definition, and predictive criteria for futility in TAVI remain ambiguous. Aim: The purpose of this study is to explore the literature addressing the definition, predictive criteria, and medical predictive models for futility in cases of TAVI. Design: A scoping review was conducted by two researchers and reported in accordance with the PRISMA-ScR guidelines. Eligibility criteria: Studies addressing futility in TAVI, including definitions, predictive variables, and models, were included without restrictions on study design but were excluded study only on surgical valve replacement, valve in valve or aortic stenosis causes by other pathology than calcification. Information sources: We identified 129 studies from five key sources: CINAHL, PUBMED, the Cochrane Library, ClinicalTrials.gov, and EMBASE. The literature search was conducted in two rounds—first in February 2024 and again in October 2024—using no restrictions on the year of publication or the language of the studies. Additional references were included through cross-referencing. Results: The definition of futility is not unanimous, although most researchers agreed on 1-year survival as a cutoff. The majority of studies focused on single variables that can predict 1-year survival, employing either prospective or retrospective designs. Frailty was the major concept studied. Numerous predictive models have been identified, but no consensus was found. Conclusion: Futility concepts generate interest in the TAVI procedure. In this review, numerous articles state that 1-year mortality serves as a cutoff to define futile procedures. Some variables, cardiac or otherwise, are independent predictors of 1-year mortality. Medical predictive models showed moderate sensitivity and specificity, except for machine learning, which shows promise for the future. However, few articles delve deeply into non-quantifiable parameters such as patient goals and objectives or ethical questions. More studies should focus on these parameters.

The number of patients undergoing transcatheter aortic valve implantation (TAVI) is rising. While TAVI improves survival, symptoms, and quality of life, ESC guidelines highlight the lack of validated tools to predict futile outcomes. Around 20% of intermediate- and high-risk patients die within one-year post-TAVI, underscoring the need for robust risk stratification. To develop and validate a multivariable model predicting one-year mortality after TAVI, using real-world data from two Swiss centers. We analysed 2,053 patients who underwent TAVI between 2014 and 2022 at two centers in the Swiss TAVI Registry. The primary endpoint was all-cause mortality at one-year. Candidate variables were selected based on a 2024 scoping review, clinical expertise, and data availability. Variables associated with mortality (p < 0.2) in univariate Cox regression were included in a multivariate Cox model. Model performance was evaluated using ROC curves and Youden’s index. At 30 days, independent predictors of mortality were reduced left ventricular ejection fraction, COPD, peripheral artery disease, and age. At one-year, mortality was associated with atrial fibrillation, lower LVEF, mitral regurgitation grade, age, corticosteroid use, coronary artery disease, insulin-dependent diabetes, and reduced glomerular filtration rate. The model achieved an AUC of 0.814 for 30-day mortality and 0.763 at one-year, outperforming the STS score for short-term prediction (AUC 0.785). This study demonstrates the feasibility of building a one-year mortality prediction model after TAVI using standard clinical variables. With 2,053 patients from two centers, the model is a promising step toward Swiss-specific risk tools. Incorporating frailty or functional status may further improve performance. This model may support heart teams in shared decision-making.

Background: Accurate operative risk stratification is essential for treatment selection in severe aortic stenosis. We developed an automated workflow using large language models (LLMs) to replicate Heart Team risk assessment. Methods: We retrospectively analyzed 231 consecutive patients with severe aortic stenosis evaluated by multidisciplinary Heart Teams (January 2022–December 2024). An automated system using GPT-4o was developed, comprising the following: (1) structured data extraction from clinical dossiers; (2) EuroSCORE II calculation via two methods (algorithmic vs. LLM-based); (3) clinical vignette generation; and (4) risk stratification comparing EuroSCORE-based thresholds versus guideline-integrated LLM approaches with/without EuroSCORE values. The primary endpoint was the risk stratification accuracy of each method compared to Heart Team decisions. Results: Mean age was 79.5 ± 7.7 years, with 58.4% female. The automated workflow processed patients in 32.6 ± 6.4 s. The LLM-calculated EuroSCORE II showed a lower mean difference from Heart Team values (−1.42%, 95% CI −2.32 to −0.53) versus algorithmic calculation (−1.88%, 95% CI −2.38 to −1.38). For risk stratification, the guideline-integrated LLM without EuroSCORE achieved the highest accuracy (90.0%) and AUC (0.93), outperforming both the EuroSCORE-based (accuracy 50.2% for high-risk, AUC 0.63) and guideline-integrated LLM with EuroSCORE approaches (accuracy 82.4%, AUC 0.76). However, systematic hallucinations occurred for cardiovascular risk factors when data were missing. Conclusions: LLMs accurately calculated EuroSCORE II and achieved 90% concordance with multidisciplinary Heart Team decisions. However, hallucinations, reproducibility concerns, and the absence of clinical outcome validation preclude direct clinical application.

Aim: Percutaneous coronary interventions require an arterial approach and administration of antithrombotic drugs. This may lead to bleeding complications. The aim of this study was to test whether “The Secret” – a pagan prayer – is effective in reducing post-interventional bleeding. Design: Randomised controlled trial. Setting: Monocentric, tertiary care centre. Participants: From January to July 2022, 200 patients (aged >18 years) undergoing elective coronary angiography were included in the study. Intervention: The intervention group received “The Secret” in addition to the normal procedure. The control group was treated according to standard practice. Main outcome measures: The primary outcome was the rate of in-hospital bleeding according to the Bleeding Academic Research Consortium (BARC) consensus definition. Results: The rate of bleeding was similar in both groups (“The Secret” group vs control group) with 16% versus 14% (p=0.69) of BARC 1, 12% versus 13% (p=0.81) of BARC 2, and 0% versus 0% of BARC 3 and 5 (p=1.00). Most (76%) of the participants believed that “The Secret” would be efficient in preventing bleeding. Conclusions: This study demonstrates no effect on bleeding after percutaneous coronary procedures. A large majority of our study population believe that “The Secret” can have a positive effect on their hospital care.

Background: The inverse association between HDL‐cholesterol (HDL‐C) levels and the risk of cardiovascular diseases is evident. Hypothesis: Our hypothesis was that higher levels of HDL‐C predisposed patients with coronary artery disease (CAD) to chronic coronary syndromes (CCS) rather than acute coronary syndrome (ACS), which has not clearly been demonstrated yet. Aim: To determine, in an unselected population of patients treated with percutaneous coronary intervention (PCI), if HDL‐C levels are independently higher in subjects presenting with CCS compared with those with ACS. Methods and Results: In this retrospective study, we identified 4215 patients who underwent PCI in the Cardio‐FR database. A total of 2942 patients met the selection criteria and completed at least a 1‐year follow‐up: 1686 suffered an ACS whereas 1256 presented with CCS. We found that low HDL‐C levels were significantly associated with ACS and high HDL‐C levels with CCS. This was consistent across all BMI categories in both genders. In addition, higher HDL‐C levels reduced the likelihood of CAD subjects developing ACS. Specifically, each mmol/L increase in HDL‐C decreased the odds of ACS by 79%. Interestingly, the CCS group had more polymorbid subjects than the ACS group. At 2‐year follow‐up, no difference in patient‐oriented composite endpoint was seen between high and low HDL‐C groups. Conclusion: In an unselected population of coronary patients, HDL‐C levels were consistently lower in individuals presenting with ACS compared with CCS, independently of BMI and gender. This finding strengthens the hypothesis that HDL‐C plays a role in long‐term protection against atherosclerotic plaque vulnerability. Considering HDL‐C level still makes sense. Trial Registration: ClinicalTrials.gov identifier: NCT04185285