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Categorization in Mechanically Ventilated Pediatric Subjects: A Proposed Method to Improve Quality
Brian K Walsh1, Craig D Smallwood2, Jordan S Rettig3
1Department of Anesthesiology, Perioperative, and Pain Medicine, Division of Critical Care Medicine, Boston Children's Hospital and Pediatric Anesthesia, Harvard Medical School, Boston, Massachusetts. briankwalsh@me.com.
Insights
A new scoring system objectively measures mechanical ventilation quality in children. This system analyzes patient data to assess ventilation and oxygenation, aiming to improve care and reduce lung injury risks.
Area of Science:
- Pediatric critical care medicine
- Biomedical engineering
- Health informatics
Background:
- Mechanical ventilation is crucial for critically ill children but carries risks if not managed optimally.
- Variations in mechanical ventilation practices persist despite evidence-based guidelines.
- Health technology advances enable real-time data streaming for mechanical ventilation monitoring.
Purpose of the Study:
- To assess the feasibility and utility of a novel patient categorization and scoring system.
- To objectively measure compliance with mechanical ventilation standards of care in pediatric intensive care units.
- To couple physiologic and ventilation data for real-time interpretation and quality surveillance.
Main Methods:
- A retrospective analysis of 222 pediatric intensive care unit patients requiring mechanical ventilation.
- Utilized 15 rules-based algorithms to categorize ventilation and oxygenation statuses.
- Calculated scores (0-100%) for acceptable ventilation, oxygenation, barotrauma-free, and volutrauma-free states.
Main Results:
- Analyzed 1,578 days of mechanical ventilation across 222 subjects.
- Median acceptable ventilation score was 84.6%; median acceptable oxygenation score was 70.1%.
- Median barotrauma-free state was 100%, while the median volutrauma-free state was 56.1%.
Conclusions:
- The study presents the first patient categorization system using data-banking and analytics for mechanical ventilation quality surveillance.
- Further research is required to evaluate if visual feedback of variance and summaries can enhance patient outcomes.
- The system demonstrates potential for objective assessment of mechanical ventilation quality in pediatric patients.
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.).
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