1-020 Advancing pericardiocentesis training: a two-year evaluation of a high-fidelity simulation programme
Bibliographic record
Abstract
Introduction Pericardiocentesis is a life-saving but high-risk procedure associated with potential complications. Despite being a core competency for cardiology trainees, there are limited opportunities for hands-on practice due to heavy clinical responsibilities and the rarity of the procedure in training settings. In most cases, pericardiocentesis is performed by senior clinicians, meaning trainees have few supervised learning opportunities. When trainees do perform the procedure, it is usually under urgent conditions without structured feedback, preventing them from refining their skills and completing Direct Observation of Procedural Skills (DOPS) assessments. As a result, there is a clear need for structured simulation-based training to enhance procedural skill performance in a safe environment. Purpose To design, evaluate, and enhance a high-fidelity pericardiocentesis simulation programme for cardiology trainees. The programme aims to enhance trainee confidence and competence while advocating for its integration into the formal training curriculum. Method A high-fidelity ultrasound-guided pericardiocentesis simulation programme was developed and introduced for cardiology trainees from February 2023. The programme was delivered one-to-one by a consultant cardiologist. Each session included: Didactic teaching, including review of core concepts, case discussions, and trainee experience-sharing. Simulation training, involving echocardiographic assessment of pericardial effusion, followed by ultrasound-guided pericardiocentesis using a high-fidelity procedural model and real pericardiocentesis instruments. The simulation setup and ultrasound-guided procedural model are shown in figure 2. Immediate feedback, with expert-led debriefing and technical correction. To assess trainee experience and confidence levels, anonymous feedback was collected after completing the simulation teaching. Results Over the two-year period, 73 cardiology registrars participated in the programme. Among them, 86.4% reported limited opportunities to practise pericardiocentesis in clinical settings due to lack of exposure, competing clinical responsibilities, and the need for consultant involvement in emergencies. Confidence scores significantly improved following training (figure 1): Pre-training confidence mean: 3.16 out of 5; Post-training confidence mean: 4.41 out of 5. 38 trainees (52.1%) improved by 1 confidence level, while 25 trainees (34.2%) improved by 2 or more levels. Trainees identified the most valuable aspects of the programme as expert instruction from experienced clinicians, realistic high-fidelity simulation equipment, immediate feedback and structured learning to enhance procedural skills and decision-making, and a multi-modal teaching approach integrating theory, case discussions, and hands-on practice. Conclusion Our two-year evaluation demonstrates significant improvements in trainee confidence and skill proficiency in performing pericardiocentesis using simulation-based training, highlighting its importance for high-risk cardiology procedures. Given the increasing demand for structured procedural training, we have recently secured approval from Health Education England (HEE) for study leave funding, ensuring financial support for trainees and the programme’s long-term sustainability. Furthermore, we are working towards expanding this initiative nationally and internationally, as demonstrated by our presentation at this congress. This study underscores the growing need for simulation-based training in cardiology, particularly for high-risk procedures. Future initiatives will focus on integrating simulation into training curricula, evaluating additional cardiology procedures, and tracking long-term trainee performance and ARCP outcomes to assess the impact of simulation-based education on career progression.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".