The effectiveness of nasal mask vs face mask ventilation in anesthetized, apneic pediatric subjects over 2 years of age: a randomized controlled trial.

BACKGROUND: We hypothesized that anesthetized, apneic children could be ventilated equivalently or more efficiently by nasal mask ventilation (NMV) than face mask ventilation (FMV). The aim of this randomized controlled study was to test this hypothesis by comparing the expiratory tidal volume (Vte) between NMV and FMV. METHODS: After the induction of anesthesia, 41 subjects, 3-17 years of age without anticipated difficult mask ventilation, were randomly assigned to receive either NMV or FMV with neck extension. Both groups were ventilated with pressure control ventilation (PCV) at 20 cmH2 O of peak inspiratory pressure (PIP) with positive end-expiratory pressure (PEEP) levels of 0, 5, and 10 cmH2 O. An additional mouth closing maneuver (MCM) was applied for the NMV group. RESULTS: The Vte was higher in the FMV group compared with the NMV group (median difference [95% CI]: 8.4 [5.5-11.6] ml·kg(-1) ; P < 0.001) when MCM was not applied. NMV achieved less PEEP than FMV (median difference [95% CI]: 5.0 [4.3-5.3] cmH2 O at 10 cmH2 O; P < 0.001) though both groups achieved the set PIP level. In the NMV group, MCM markedly increased Vte (median increase [95% CI]: 5.9 [2.5-9.0] ml·kg(-1) ; P < 0.005) and PEEP (median increase [95% CI]: 5.0 [0.6-8.6] cmH2 O at 10 cmH2 O; P < 0.005); however, PEEP was highly variable and lower than that of FMV (median difference [95% CI]: 2.5 [0.8-8.5] cmH2 O at 10 cmH2 O; P < 0.05). CONCLUSIONS: In anesthetized, apneic children greater than 2 years of age ventilated with an anesthesia ventilator and neck extension, FMV established a greater Vte than NMV regardless of mouth status. NMV could not maintain the set PEEP level due to an air leak from the mouth. The MCM increased the Vte and PEEP.

A Systems Approach to Healthcare Innovation Using the MIT Hacking Medicine Model.

MIT Hacking Medicine is a student, academic, and community-led organization that uses systems-oriented "healthcare hacking" to address challenges around innovation in healthcare. The group has organized more than 80 events around the world that attract participants with diverse backgrounds. These participants are trained to address clinical needs from the perspective of multiple stakeholders and emphasize utility and implementation viability of proposed solutions. We describe the MIT Hacking Medicine model as a potential method to integrate collaboration and training in rapid innovation techniques into academic medical centers. Built upon a systems approach to healthcare innovation, the time-compressed but expertly guided nature of the events could enable more widely accessible preliminary training in systems-level innovation methodology, as well as creating a structured opportunity for interdisciplinary congregation and collaboration.