Assessing and Training Manual Ventilation Skills
Multiple studies show that manual ventilation performance is widely variable—and often inadequate—even among providers with years of training and experience. This inconsistency in skill level puts patients’ lives at risk.
If your students or medical personnel weren’t trained well enough in manual, or bag-valve mask ventilation, how would you know? And if you did know, what could you do about it to improve patient outcomes?
The Challenge: Assessing and Developing Competency
William Malley, program director at the Indiana University of Pennsylvania (IUP)/West Penn Hospital School of Respiratory Care in Pittsburgh, recently oversaw a student project that set out to address these important questions in a study using the RespiTrainer Advance. The RespiTrainer Advance is an intubation and manual ventilation training system that provides instant feedback on manual ventilation skills. It consists of three components: an intubation head, an adjustable test lung, and a performance feedback system that sends data wirelessly to a computer in real time. The portable system gives users hands-on experience with different patient lung conditions for training, allows them to see actual delivered ventilation in realtime and also provides exportable records for competence testing. In their project, Malley’s students used an analysis tool available from IngMar Medical for research with the RespiTrainer Advance.
The Solution: Data Collection and Performance Feedback
The students first established an ideal minute ventilation range for a simulated patient—a 5-foot-9, 180-pound man in cardiac arrest. They then tested the manual ventilation performance of 23 junior and senior respiratory care students and first- and second-year nursing students in this clinical scenario. Most had little actual bag-mask ventilation experience. The students were asked to bag the simulated patient on the RespiTrainer Advance as they saw fit. During testing, subjects could not see the performance data and were not using any feedback. The IUP/WPH team also evaluated the skill level of a group of 17 Registered Respiratory Therapists (RRTs) and instructors—several with more than 20 years practice in the field—on the same “patient.” Proper manual ventilation was measured by the participants’ ability to keep their patient’s minute ventilation within the ideal range. The researchers found that many students, RRTs and instructors performed inconsistent ventilation that didn’t stay within the recommended limits; overall, the tidal volumes trended low with breath rates too high. After this pretest, the student participants were given five minutes of training using the RespiTrainer Advance real-time feedback on actual volume, flow and pressure delivery. Their manual ventilation performance was then retested and compared to their baseline skill level before the training session.
“It was the ability for students to see immediate performance feedback with the RespiTrainer that changed behavior to improve their ventilation skills.”William Malley, MS, RRT, FARC; Program Director, IUP/West Penn Hospital School of Respiratory Care
The Findings: Improved Performance
The performance of both the respiratory care and nursing students improved markedly after training with the RespiTrainer Advance, with more ventilation data points clustered in the ideal range. Post-training, there also were fewer “over-pressurization” events, where airway pressure was applied at dangerous levels that could have grave consequences for a real patient. Moreover, the effectiveness of the training had a lasting effect; when the experiment was repeated 10 weeks later, the students’ pretest results were improved compared to their first trial. These findings are consistent with a recent study in the Journal of Perinatal Medicine on manual neonatal ventilation training.
The Future: Optimizing training and testing
The students’ findings about improvement and retention of skills raise further questions about how manual ventilation training could be optimized. The RespiTrainer’s data collection capabilities and analysis tool provides a means to investigate the impact of training variables such as repetition and frequency on skills development and retention.