Categorization in mechanically ventilated pediatric subjects: A proposed method to improve quality

Brian K. Walsh, Craig D. Smallwood, Jordan S. Rettig, John E. Thompson, Robert M. Kacmarek, John H. Arnold

Research output: Contribution to journalArticlepeer-review


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. ( registration NCT02184208.).

Original languageEnglish (US)
Pages (from-to)1168-1178
Number of pages11
JournalRespiratory care
Issue number9
StatePublished - Sep 1 2016
Externally publishedYes


  • Computer decision support
  • Data
  • Evidence-based practice
  • Mechanical ventilation
  • Protocols
  • Quality
  • Ventilatorinduced lung injury

ASJC Scopus subject areas

  • Pulmonary and Respiratory Medicine
  • Critical Care and Intensive Care Medicine


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