Implementation of a novel thoracostomy tube trainer with real-time feedback.

Objectives: Simulation-based training leads to improved clinical performance but may be influenced by quality and frequency of training. Within simulation training, chest tube insertion remains a challenge as one of the main pitfalls of insertion is a controlled pleural entry. This study evaluates the efficacy of a novel training model with real-time pressure monitoring, the average force to pleural entry in a model and the utility of audio and visual feedback.
Methods: This proprietary training model comprised a modified Kelly clamp device with three force sensors at the index finger (sensor 1) and two finger loops (sensors 2 and 3), and a manikin with a replaceable chest wall pad. Standard force values (Newtons (N)) were obtained by experts; expert data revealed that 3-5 s was an acceptable time range to complete the chest tube insertion. Participant level ranged from Post-graduate Year (PGY)-1 to PGY-6 with 13 total participants. Each individual was provided an introduction to the procedure and chest tube trainer. Force (N) and time (ms) measurements were obtained from entry through dermis to pleural space puncture. A significant pressure drop suggested puncturing through the chest wall (completion of the procedure).
Results: Force data were captured during each phase of the procedure-linear, plateau, and drop. Linear phase (~3000 ms) was from start of procedure to point of maximum force (<30 N). Plateau phase was from maximum force to just before a drop in pressure. Drop phase was a drop in pressure by 5+ N in a span of 150 ms signaling completion of procedure. All participants were able to complete the task successfully. Force for pleural entry ranged from 17 N to 30 N; time to pleural entry ranged from 7500 to 15 000 ms. There was variability in use of all three sensors. All participants used the index sensor, however there was variability in the use of the loop sensors depending on the handedness of the participant. Left-handed users relied more on sensors 1 and 3 while right-handed users relied more on sensors 1 and 2. Given this variability, only force measurements from sensor 1 were used for assessment.
Conclusions: This novel force-sensing chest tube trainer with continuous pressuring monitoring has a wide range of applications in simulation-based training of emergency surgical tasks. Next steps include evaluating its impact on accuracy and efficiency. Applications of real-time feedback measuring force are broad, including vascular access, trocar placement and other common procedures.
Level of Evidence: Level IV, prospective study.
Competing Interests: Competing interests: CP is an advisor to TraumaCare.AI, a machine-learning entity.
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