Thomas Nestelberger

Introduction: Transcatheter aortic valve replacement (TAVR) has become a widely used, comparably efficient and safe alternative to surgical aortic valve replacement (SAVR). Its utilization continues to grow, especially among younger patients. Despite improvements in durability, degeneration and subsequent re-interventions of failed prosthetic valves are still common. Even though valve-in-valve procedures have become more frequent, little is known about the trends over time or about clinical and echocardiographic long-term outcomes. Materials and Methods: Patients who underwent a valve-in-valve procedure between December 2011 and December 2022 in a large tertiary university hospital were analyzed. Primary outcomes were defined as procedural and device successes as well as event-free survival. Secondary analyses between subsets of patients divided by index valve and date of procedure were performed. Results: Among 1407 procedures, 58 (4%) were valve-in-valve interventions, with an increased frequency observed over time. Overall, technical success was achieved in 88% and device success in 85% of patients. Complications were predominantly minor, with similar success rates among TAVR-in-SAVR (TiSAVR) and TAVR-in-TAVR (TiTAVR). Notably, there were significant and lasting improvements in mean echocardiographic gradients at 1 year. Event-free survival was 76% at one month and 69% at one year. Conclusions: Over the last decade, a rising trend of valve-in-valve procedures was observed. Despite an increase in procedures, complications show a contrasting decline with improved technical and device success over time. TiSAVR and TiTAVR showed comparable rates of procedural and device success as well as similar outcomes, highlighting the utility of valve-in-valve procedures in an aging population.

Aims Risk stratification of patients with chest pain and a high-sensitivity cardiac troponin T (hs-cTnT) concentration <upper reference limit (URL) is challenging. The aim of this study was to develop and externally validate clinical models for risk prediction of 90-day death or myocardial infarction in patients presenting to the emergency department with chest pain and an initial hs-cTnT concentration <URL. Methods and results Four machine-learning-based models and one logistic regression (LR) model were trained on 4075 patients (single-centre Spanish cohort) and externally validated on 3609 patients (international prospective Advantageous Predictors of Acute Coronary syndromes Evaluation cohort). Models were compared with GRACE and HEART scores and a single undetectable hs-cTnT-based strategy (u-cTn; hs-cTnT < 5 ng/L and time from symptoms onset >180 min). Probability thresholds for safe discharge were derived in the derivation cohort. The endpoint occurred in 105 (2.6%) patients in the training set and 98 (2.7%) in the external validation set. Gradient boosting full (GBf) showed the best discrimination (area under the curve = 0.808). Calibration was good for the reduced neural network and LR models. Gradient boosting full identified the highest proportion of patients for safe discharge (36.7 vs. 23.4 vs. 27.2%; GBf vs. LR vs. u-cTn, respectively) with similar safety (missed endpoint per 1000 patients: 2.2 vs. 3.5 vs. 3.1, respectively). All derived models were superior to the HEART and GRACE scores (P < 0.001). Conclusion Machine-learning and LR prediction models were superior to the HEART, GRACE, and u-cTn for risk stratification of patients with chest pain and a baseline hs-cTnT <URL. Gradient boosting full models best balanced discrimination, calibration, and efficacy, reducing the need for serial hs-cTnT determination by more than one-third. Clinical trial registration ClinicalTrials.gov number, NCT00470587, https://clinicaltrials.gov/ct2/show/NCT00470587.