Background: Thousands of children require mechanical ventilation each year. Although mechanical ventilation is lifesaving, it is also associated with adverse events if not properly managed. The systematic implementation of evidence-based practice through the use of guidelines and protocols has been shown to mitigate risk, yet variation in care remains prevalent. Advances in health-care technology provided the ability to stream data about mechanical ventilation and therapeutic response. Through these advances, a computer system was developed to enable the coupling of physiologic and ventilation data for real-time interpretation. Our aim was to assess the feasibility and utility of a newly developed patient categorization and scoring system to objectively measure compliance with standards of care.
Methods: We retrospectively categorized the ventilation and oxygenation statuses of subjects within our pediatric ICU utilizing 15 rules-based algorithms. Targets were predetermined based on generally accepted practices. All patient categories were calculated and presented as a percent score (0-100%) of acceptable ventilation, acceptable oxygenation, barotrauma-free, and volutrauma-free states.
Results: Two hundred twenty-two subjects were identified and analyzed encompassing 1,578 d of mechanical ventilation. Median age was 3 y, median ideal body weight was 14.7 kg, and 63% were male. The median acceptable ventilation score was 84.6%, and the median acceptable oxygenation score was 70.1% (100% being maximally acceptable). Potential for ventilator-induced lung injury was broken into 2 components: barotrauma and volutrauma. There was very little potential for barotrauma, with a median barotrauma-free state of 100%. Median potential for a volutrauma-free state was 56.1%.
Conclusions: We demonstrate the first patient categorization system utilizing a coordinated data-banking system and analytics to determine patient status and a surveillance of mechanical ventilation quality. Further research is needed to determine whether interventions such as visual display of variance from goal and patient categorization summaries can improve outcomes. (ClinicalTrials.gov registration NCT02184208.).
Keywords: computer decision support; data; evidence-based practice; mechanical ventilation; protocols; quality; ventilator-induced lung injury.
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