Learning curve in leadless pacemaker implantation: impact of center experience on procedure and fluoroscopy times in the france LEADLESS registry

Background Leadless pacemakers (LP) offer advantages over transvenous systems, including reduced infection risk and elimination of pocket-related complications. As LP adoption expands across centers, understanding the learning curve associated with this technology is essential to optimize procedural efficiency and safety. Methods We analyzed 1,249 procedures from the France LEADLESS registry performed at 39 centers between December 2023 and September 2025. To assess the impact of operator experience on procedural performance, procedures were grouped into six blocks based on cumulative implant count per center (Block 1: 1st–10th patient; Block 6: >50th patient). Two indicators were assessed: total procedure duration and fluoroscopy time. Trends were evaluated using Cuzick’s non-parametric trend test, and mixed-effects models with random intercepts by center accounted for inter-center variability. Results We observe a significant procedure duration decrease linked to increasing experience of centers (p < 0.001), with a marked decline during the first 20–40 procedures before reaching a plateau. For the first 10 procedures, the procedure median duration was 45 minutes (IQR: 33–60). This progressively decreased to reach 30 minutes beyond the 40th case. The range of procedure times also narrowed with experience: The 75th percentile fell from 60 to 45 min, although extreme values persisted (up to 150 min), reflecting occasional complex cases even in experienced centers. For fluoroscopy time, a modest decline was observed (Cuzick’s trend test, p = 0.046) during the first 30 procedures. Conclusion Our analysis demonstrates an early learning curve for leadless pacemaker implantation, with significant reductions in procedure and fluoroscopy times during the initial experience phase. The most pronounced improvements occur within the first 20–40 implantations, after which performance tends to plateau, suggesting that operators may achieve optimal procedural efficiency beyond this threshold. However, limitations include the absence of adjustment for case complexity, and individual operator experience. Future research could incorporate operator-level data and training timelines to refine learning curve assessment.