A Model to Predict Pre-Hospital Endotracheal Intubation Success

<p>Background: Pre-hospital endotracheal intubation (ETI) is one of the most critical skills performed by paramedics and is considered the “gold standard” of airway management. Pre-hospital ETI success rates are variable, ranging from 33% to 100% across EMS systems in the U.S. Previous investigative efforts have identified factors associated with pre-hospital ETI success, but the generalizability of findings is limited. Few researchers have controlled for the concurrent effects of multiple factors when examining pre-hospital ETI success. Methods: In this retrospective exploratory study, we used national data from the National Emergency Medical Services Information System (NEMSIS) and data from a four state regional representation of the U.S. emergency medical services (EMS) system for 2013 to generate National and Comprehensive State models. Hierarchical logistic regression was used to evaluate what variables predicted pre-hospital ETI success. Results: Type of service requested, U.S. census region, EMS total call time, Center for Medicare and Medicaid Services service level, provider certification level, race, chief complaint organ system, and cardiac arrest were structure factors significantly associated with pre-hospital ETI success (p < .001). Number of pre-hospital ETI attempts and response mode to scene were process factors significantly associated with pre-hospital ETI success (p < .001). Conclusion: Future researchers should examine systems with the best patient outcomes and use Utstein-style templates to frame data collection for airway management. These approaches will help clarify the use of advanced airway management and help to develop evidence-based guidelines for EMS provider.</p>

Name thesis:degree_name

Doctor of Philosophy (PhD)

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Health Services Research

Year dc:date.available

2016

Author dc:creator

• Diggs, Leigh Ann

Contributors dc:contributor

• Kimberly Adams Tufts
• Andrea Parodi
• N. Clay Mann

• Emergency medical services
• Endotracheal intubation
• Logistic regression
• Model
• Pre-hospital
• Emergency Medicine
• Medicine and Health Sciences

Identifier

9781339843353

OAI identifier oai:identifier

oai:digitalcommons.odu.edu:healthservices_etds-1001