TY - JOUR
T1 - A pilot model for predicting the success of prehospital endotracheal intubation
AU - Diggs, Leigh Ann
AU - Viswakula, Sameera D.
AU - Sheth-Chandra, Manasi
AU - De Leo, Gianluca
N1 - Funding Information:
The authors would like to acknowledge the generous support of the Modeling and Simulation Graduate Research Fellowship provided to Ms. Leigh Ann Diggs by Old Dominion University and VMASC during academic years 2013-2014. The authors would also like to thank Carol B. Pugh, PharmD, MS, biostatistician with the Office of EMS in the Virginia Department of Health, for extracting the HIPAA compliant research dataset for this study and for her technical assistance with the data.
Publisher Copyright:
© 2014 Elsevier Inc. All rights reserved.
PY - 2015/2/1
Y1 - 2015/2/1
N2 - Abstract Objectives We sought to evaluate the success of prehospital, non-drug-assisted endotracheal intubation (ETI) performed by Virginia prehospital care providers and to develop a model designed to predict the probability of success of ETI. Methods We conducted a retrospective observational study on prehospital, non-drug-assisted ETI (N = 4002) performed by Virginia prehospital care providers, from January 1, 2012, to December 31, 2012. Using descriptive statistics, we quantified patient, provider, and system characteristics. Success rates were calculated by provider certification level and number of ETI attempts. Procedure complications were evaluated for the entire cohort. Variables were recoded for modeling purposes. Univariate analyses using χ2 tests were performed to identify candidate parameters to be included in the model. We performed a backward stepwise logistic regression to predict ETI success. Results An overall success rate of 69.9% was found. Binary logistic regression revealed the following covariates associated with ETI success: community type, provider certification level, gender, age group, myocardial infarction, and ethnicity which were all significant (P < 0.05) with a - 2 log-likelihood value of 3705.574. This was the most parsimonious model evaluated and demonstrated good fit (Hosmer-Lemeshow test P =.646) but poor discrimination (area under the receiver operating characteristic curve = 0.595). Conclusion This study characterized prehospital ETI success using retrospective state data and found a low overall success rate. Binary logistic regression was performed to create a model and equation identifying a set of factors associated with ETI success.
AB - Abstract Objectives We sought to evaluate the success of prehospital, non-drug-assisted endotracheal intubation (ETI) performed by Virginia prehospital care providers and to develop a model designed to predict the probability of success of ETI. Methods We conducted a retrospective observational study on prehospital, non-drug-assisted ETI (N = 4002) performed by Virginia prehospital care providers, from January 1, 2012, to December 31, 2012. Using descriptive statistics, we quantified patient, provider, and system characteristics. Success rates were calculated by provider certification level and number of ETI attempts. Procedure complications were evaluated for the entire cohort. Variables were recoded for modeling purposes. Univariate analyses using χ2 tests were performed to identify candidate parameters to be included in the model. We performed a backward stepwise logistic regression to predict ETI success. Results An overall success rate of 69.9% was found. Binary logistic regression revealed the following covariates associated with ETI success: community type, provider certification level, gender, age group, myocardial infarction, and ethnicity which were all significant (P < 0.05) with a - 2 log-likelihood value of 3705.574. This was the most parsimonious model evaluated and demonstrated good fit (Hosmer-Lemeshow test P =.646) but poor discrimination (area under the receiver operating characteristic curve = 0.595). Conclusion This study characterized prehospital ETI success using retrospective state data and found a low overall success rate. Binary logistic regression was performed to create a model and equation identifying a set of factors associated with ETI success.
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U2 - 10.1016/j.ajem.2014.11.020
DO - 10.1016/j.ajem.2014.11.020
M3 - Article
C2 - 25488339
AN - SCOPUS:84924749112
SN - 0735-6757
VL - 33
SP - 202
EP - 208
JO - American Journal of Emergency Medicine
JF - American Journal of Emergency Medicine
IS - 2
M1 - 54622
ER -