(1318) Experience-Based Intubation Algorithm in a Pediatric Intensive Care Unit

Monday, February 24, 2025

2:30pm – 3:30pm Eastern Time

Location: Connections Central - RST 13

First Author(s)

Rachel K. Marlow, MD (she/her/hers)

Phoenix Children's Hospital
Phoenix, Arizona, USA

Co-Author(s)

KG

Kelsey M. Gladen, MD

Pediatric Intensivist
Essentia Health - St. Mary's Medical Center, United States
SB

Sandra D.W. Buttram, MD

Clinical Associate Professor of Child Health
Division of Pediatric Critical Care Medicine, Phoenix Children’s; University of Arizona COM-Phoenix, United States
FW

F. Anthony Willyerd, MD

Clinical Assistant Professor
Division of Critical Care, Phoenix Children’s; Clinical Assistant Professor, Department of Child Health, University of Arizona School of Medicine - Phoenix, United States
JM

Jenny S. Mendelson, MD

Clinical Assistant Professor
Division of Pediatric Critical Care, Phoenix Children’s; Clinical Assistant Professor, Department of Child Health, University of Arizona School of Medicine - Phoenix, United States
MT

M'hamed Temkit, PhD

Research Biostatistician
Phoenix Children's, United States
Akira Nishisaki, MD, MSCE (he/him/his)

Division of Critical Care Medicine, Department of Anesthesia and Critical Care
The Children's Hospital of Philadelphia
Philadelphia, Pennsylvania, United States
DT

David Tellez, MD, FCCM

Clinical Associate Professor
Division of Critical Care, Phoenix Children’s; Clinical Assistant Professor, Department of Child Health, University of Arizona School of Medicine - Phoenix, United States

Introduction: Tracheal intubation (TI) is a paramount skill for pediatric critical care fellows to learn. Balancing the risk of adverse airway outcomes (AAOs): tracheal intubation associated events (TIAEs) and/or oxygen desaturation with fellow’s skill learning is a challenge in academic pediatric ICU (PICU). AAOs are associated with increased PICU length of stay and mortality. We hypothesized implementing a system to match the provider experience level with the anticipated risk based on predetermined airway risk factors would lead to a decrease in AAOs.

Methods: An algorithm was created to match provider experience level to patient risk factors, utilizing the pre-TI risk assessment in the NEAR4KIDS airway bundle. Provider experience was categorized into 4 levels (beginner-expert). Each provider identified their category by badge sticker at bedside. Center specific demographic and outcome data was collected from the NEAR4KIDS database. Pre-implementation period was defined as 18 months before implementation (10/2020-3/2022). Implementation period (4/2022-6/2022) was excluded from analysis. Post-implementation period was defined as 7/2022-12/2023. Data analyzed with chi-square and Wilcoxon rank sum, as appropriate, with p< 0.05 as significant.

Results: 237 patients received TIs during pre-implementation and 300 patients in post-implementation. Our algorithm compliance rate was 90% during post-implementation period, above our target of >80%. Comparing to pre-implementation, patients receiving TIs in post-implementation were younger (median in months, pre 81 [IQR 11, 172] vs. post 54 [6, 149], p=0.0139), had higher PIM2 scores (median 1.0 [IQR 0.8, 3.5] vs. 1.4 [0.9, 4.4], p=0.003), TI with video laryngoscopy (7.5% vs. 59.2%, p< 0.0001), and apneic oxygenation (68.2% vs. 85.1%, p< 0.0001). Proportion of TIs by senior (2nd/3rd) fellows and attendings did not significantly change (senior fellows: pre 43% vs. post 55%, p=0.108, attending: pre 8.4% vs. post 9.7%, p=0.654). There was significant reduction in the AAOs: pre 71/224 (31.7%) vs. post 53/289 (18.3%), p=0.0005, with absolute risk reduction of 13.4% (95% CI, 5-21%).

Conclusions: Implementing a system to match the provider experience level with the anticipated risk based on predetermined airway risk factors led to a decrease in AAOs in an academic PICU.