University of Southern California Dissertations and Theses

Simulation-based training is associated with lower risk-adjusted mortality in ACS Pediatric TQIP centers

(USC Thesis Other)

Simulation-based training is associated with lower risk-adjusted mortality in ACS Pediatric TQIP centers

PDF

Download a page range

Download transcript

Copy asset link

Request this asset

Transcript (if available)

Content Master’s Thesis

Simulation-Based Training is Associated with Lower
Risk-Adjusted Mortality in ACS Pediatric TQIP Centers

Author:
Aaron R Jensen MD MEd

Collaborators:
Cory McLaughlin MD, Haris Subacius MA, Katie McAuliff PhD,
Avery B Nathens MD MPH PhD, Carolyn Wong PhD,
Daniella Meeker PhD,

Randall S Burd MD PhD,
7

Henri R Ford MD MHA, and Jeffrey S Upperman MD

Thesis Committee Members:
Jeffrey S Upperman MD (Chair), Todd Chang MD MAcM,
Daniella Meeker PhD, and Cecilia Patino-Sutton MD MEd PhD

Conferring Major/Program:
Clinical, Biomedical, and Translational Investigation

Degree Being Conferred:
Master of Science (MS)

University of Southern California
Keck School of Medicine
Department of Preventive Medicine

Degree Conferral Date:
May 10, 2019

Page 2 of 2 9

Simulation and Pediatric Trauma Outcomes

TABLE OF CONTENTS
Abstract 3
Introduction 4
Methods 6
Results 11
Discussion 13
Conclusions 17
Bibliography 18
Tables 21
Figures 27
Funding Acknowledgement 28
Human Subjects Approval Information 29

Page 3 of 2 9

Simulation and Pediatric Trauma Outcomes

ABSTRACT
Background: Although use of simulation-based team training for pediatric trauma resuscitation
has increased, its impact on patient outcomes has not yet been shown. The purpose of this
study was to determine the association between simulation use and patient outcomes.
Methods: Trauma Centers that participate in the American College of Surgeons (ACS)
Pediatric Trauma Quality Improvement Program (TQIP) were surveyed to determine frequency
of simulation use in 2014 and 2015. Center-specific clinical data for 2016 and 2017 were
abstracted from the ACS TQIP registry (n=57,916 patients) and linked to survey responses.
Center-specific risk-adjusted mortality was estimated using multivariable hierarchical logistic
regression and compared across four levels of simulation-based training use: no training, low-
volume training, high-volume training, and survey non-responders (unknown training use).
Results: Survey response rate was 75% (94/125 centers) with 78% of the responding centers
(73/94) reporting simulation use. The average risk-adjusted odds of mortality was lower in
centers with a high-volume of training compared to centers not using simulation (OR 0.58, 95%
CI 0.37-0.92). The times required for resuscitation processes, evaluations, and critical
procedures (endotracheal intubation, head computed tomography, craniotomy, and surgery for
hemorrhage control) were not different between centers based on levels of simulation use.
Conclusions: Risk-adjusted mortality is lower in TQIP-Pediatric centers using simulation-
based training, but this improvement in mortality may not be mediated by a reduction in time to
critical procedures. Further investigation into alternative mediators of improved mortality
associated with simulation use is warranted, including assessment of resuscitation quality,
improved communication, enhanced teamwork skills, and decreased errors.
Level of Evidence: Level III therapeutic / care management
Key Words: pediatric; trauma; resuscitation; simulation-based training; outcomes
Page 4 of 2 9

Simulation and Pediatric Trauma Outcomes

INTRODUCTION
Injury remains the leading cause of mortality in children 1-18 years old.
1,2
More than 50%
of deaths after injury are within the first 24 hours.
3
This early mortality shows the importance of
improving the quality of the resuscitation phase of care for critically injured children. Children
may have better outcomes when treated at pediatric trauma centers,
4–8
but a minority of
severely injured children are initially resuscitated at pediatric centers.
9,10
Due to the rarity of
severe injury in children, providers in pediatric centers may lack experience caring for an injured
child in extremis.
11
Trauma resuscitation is a time-dependent process, and time to completion of
critical evaluation and intervention may play a critical role in improving outcomes.
12–17
Achieving
a timely and high-quality multidisciplinary resuscitation requires experienced trauma providers
working as a coordinated team.
Simulation-based training has been associated with improved team performance during
trauma resuscitation.
18–22
Simulation use has also been associated with improved outcomes for
pediatric in-hospital cardiac arrest.
23
The use of simulation-based training for pediatric trauma
resuscitation has been reported by several single-center studies and is being increasingly
utilized,
18,22,24,25
but not in a standardized fashion.
26
Demonstrated benefits of multidisciplinary
simulation-based team training for trauma resuscitation also include faster time to completion of
the primary survey, faster time to critical procedures (time to endotracheal intubation and time to
computed tomography completion), and faster time to emergency surgery in single-center
studies of adult trauma patients,
21,27,28
but the impact of simulation use on patient outcomes has
not been studied widely and has not been studied specifically for injured children.
The purpose of this study was to determine if the use of simulation-based training for
trauma resuscitation is associated with improved performance measured by 1) time to critical
evaluations and procedures, and 2) risk-adjusted mortality in pediatric trauma patients. We
hypothesized that pediatric TQIP centers that use simulation-based training would have lower
Page 5 of 2 9

Simulation and Pediatric Trauma Outcomes

risk-adjusted mortality and faster times to critical evaluations and procedures for injured
children.

Page 6 of 2 9

Simulation and Pediatric Trauma Outcomes

METHODS
Study Population
All trauma centers participating in the American College of Surgeons Pediatric Trauma
Quality Improvement Program (ACS Pediatric TQIP) in 2016 (N=125) were selected for
inclusion in this cross-sectional cohort study. Participation in the ACS Pediatric TQIP Program
requires either state designation or ACS verification as a pediatric trauma center, a threshold
minimum of one trauma registrar for 500 annual trauma admissions, and an annual fee of
$12,000 USD. Trauma centers participating in the ACS Pediatric TQIP program contribute data
to a national registry in accordance with the National Trauma Data Standard. The Pediatric
TQIP registry includes patients eighteen years and under with at least one injury with an
Abbreviated Injury Scale, AIS, post-dot severity score of two or higher. Patients are excluded
from the registry if presenting without signs of life, are discharged home from the emergency
department, have a pre-existing advanced directive, or have a major burn injury.
29
Transfer-in
patients were excluded from all analyses, as simulation use would not be expected to impact
the outcomes patients that underwent initial resuscitation at a referring facility. Transfer-in
patients were, however, stratified out and used as an internal center-specific control for risk-
adjusted modeling of mortality. Human Subjects approval was obtained from the Children’s
Hospital Los Angles Institutional Review Board.
Survey Development, Implementation, and Exposure Definition
A seventeen-item survey was developed by the study team and piloted for readability by
a group of trauma program managers. Full details of the survey have been previously
described.
26
The survey was administered electronically via Qualtrics online software to trauma
program managers at each participating center and subsequently sent to trauma medical
directors at initially nonresponding centers. Additional follow-up by direct phone survey was
Page 7 of 2 9

Simulation and Pediatric Trauma Outcomes

attempted by ACS TQIP program staff, with verbal survey administration of the survey. Survey
items included annual number of simulation-based training sessions for calendar years 2014
and 2015 (scaled 0-12 and 13+). Trauma center simulation use was defined as no training
(zero simulation-based training sessions in two years), low-volume training (1-10 simulation-
based training sessions over two years), high-volume training (11 or more simulation-based
training sessions over two years), or unknown training (survey non-responders).
Outcome Measures and Cohort Definitions
The primary outcome measure for this study was risk-adjusted mortality compared
across levels of training. Secondary outcomes included time to critical interventions
(endotracheal intubation), evaluations (head CT), and procedures (emergent craniotomy and
surgery for hemorrhage control). For secondary analyses, ‘traumatic brain injury (TBI)’ was
defined as any patient with an AIS head post-dot score ≥ 1, excluding scalp laceration or skull
fracture codes. ‘Isolated TBI’ was defined as any patient with TBI with no other AIS post-dot
score ≥ 2 other than facial injuries. ‘Emergent craniotomy’ was defined as any craniotomy in a
patient that had an ED disposition of ‘Operating Room’ and only received one head CT before
ED discharge. Frequency of endotracheal intubation (as a marker of resuscitation quality) and
time to endotracheal intubation were assessed for all trauma patients with initial known GCS
total ≤ 8, as well as for patients with confirmed TBI and initial known GCS total ≤ 8. Time to first
head CT was assessed for isolated TBI patients with an initial known GCS total ≤ 8, and for
polytrauma patients with TBI and an initial known GCS total ≤ 8. Time to emergent craniotomy
was assessed for isolated TBI patients and for polytrauma patients with TBI. Time to surgery
for hemorrhage control was assessed for any patient that received packed red blood cells within
four hours of ED admission, had a surgical (non-angiographic) procedure for control of bleeding
(laparotomy, thoracotomy, sternotomy, neck exploration, extremity exploration, skin or soft
Page 8 of 2 9

Simulation and Pediatric Trauma Outcomes

tissue operation, or mangled extremity procedure), and had an ED disposition of ‘Operating
Room’.
Patients were excluded from analyses of time to intubation if they had prehospital
intubation (defined by time to procedure variable as 0), tracheostomy (defined by ICD-10
procedure codes) performed in the emergency room, or endotracheal intubation performed after
discharge from the emergency room. Patients were excluded from analyses of time to head CT
if the scan time was the same as the patient arrival time or if the scan occurred after ED
discharge. The location and timing of endotracheal intubation or head CT origination was
determined using time to procedure variables and ED length of stay variables.
Confounding Covariates
The TQIP program uses a validated multivariable model for risk-adjusted benchmarking
of mortality between pediatric trauma centers.
30
Covariates in this model include gender, race
(white, black, Asian, other), age, comorbidities (respiratory diseases, substance abuse, major
psychiatric illness, bleeding disorder, functional dependence, diabetes mellitus, hypertension,
congenital anomalies, and prematurity), injury-specific survival risk ratios calculated and
validated based on historic datasets, age-normalized initial ED systolic blood pressure, age-
normalized initial ED heart rate, initial ED GCS motor score, maximum AIS post-dot severity
scores by body region (head, face, neck, chest, abdomen, spine, upper extremity, lower
extremity), mechanism of injury (fall, motor vehicle occupant, motorcyclist, struck by object,
firearm, cut/pierce, pedestrian struck by bicycle, other), pre-hospital cardiac arrest, and
interaction terms for AIS head by age, AIS head by infant, and systolic blood pressure by
firearm injury. Patients were stratified based on transfer-in status to compare ED resuscitation
patients with transfer-in patients as a center-specific internal control population of patients that
were resuscitated at a referring facility should not have been impacted by any effect of
simulation-based training. Trauma center state designation status, ACS verification level, and
Page 9 of 2 9

Simulation and Pediatric Trauma Outcomes

annual admission volume are specifically not included in the TQIP mortality model. We therefore
performed sensitivity analyses including these covariates to assess for unmeasured
confounding due to trauma center resources and due to trauma admission volume as a proxy
for provider and team experience.
Adjusted analyses for all secondary outcomes included these same covariates in
addition to several other specific factors. Additional covariates considered included ED
respiratory rate, ED respiratory assistance, ED oxygen saturation, ED supplemental oxygen,
and injury related to child abuse. Adjusted analyses for time to head CT, time to craniotomy,
and time to surgery for hemorrhage control also included factors that may impact ED length of
stay, including prehospital intubation, chest tube placement in the emergency room, transfusion
in the emergency room, placement of a surgical airway in the emergency room, and CT of the
abdomen and pelvis in the emergency room. Transfer-in patients were excluded from all
secondary analyses.
Statistical Analyses
Baseline center characteristics assessed for differences across levels of simulation with
omnibus tests of significance, including ANOVA for continuous variables and chi-squared test of
independence for categorical variables. Missing physiological data for center-level mortality
comparisons was managed using multiple imputation. The imputation model included age,
gender, race, transfer status, the presence of a serious body region injury (AIS>=2 of the spine,
abdomen, lower extremity, and upper extremity), and injury mechanism (fall, firearm,
motorcyclist, motor vehicle occupant, pedestrian, struck, and other mechanism), and age by
vital interactions in the imputation model. Mortality was modeled using center-level odds ratios
from Fall 2017 TQIP risk-adjusted models as outcomes in an analysis of variance model.
Because of positive skew, resuscitation process times were normalized by log transformation.
Missing data for multivariable time-to-event analyses were imputed by drawing from a random
Page 1 0 of 2 9

Simulation and Pediatric Trauma Outcomes

distribution with sample mean and standard deviation or from a binary distribution for
proportions to minimize bias and preserve variability. To account for clustering of patients within
centers, these times were analyzed using hierarchical linear regression across levels of
simulation-based training use. All significance tests were two-tailed, with α=0.05. All analyses
were performed using SAS software v. 9.4 (SAS Institute Inc., Cary, NC).

Page 1 1 of 2 9

Simulation and Pediatric Trauma Outcomes

RESULTS
One hundred twenty-five ACS TQIP-Pediatric trauma centers were surveyed (Table 1a).
Survey response rate was 75% (N = 94/125 centers). One center that responded to the survey
did not submit registry data for 2016 and was therefore not included in the clinical outcomes
analysis, leaving 124 centers for our analysis sample. Simulation use in 2014-15 was reported
in 54 centers (43% of all centers and 58% among respondents). Among centers reporting
simulation use in 2014-15, 19 (15% of all centers and 20% among respondents) centers
reported low volume simulation use (median [IQR] of 6 [3-8] sessions over two years). Thirty-
five (28% of all centers and 37% among respondents) centers reported high-volume simulation
use (22 [14-26] sessions over two years). High-volume simulation centers were found to have
significantly lower overall trauma volume and annual trauma admissions with serious injury
(ISS>16). No significant differences in pediatric (age ≤ 14) admissions were observed across
levels of training. Centers reporting any level of training were more likely to be an ACS-verified
or state-designated level 1 pediatric trauma center and were more likely to have a pediatric
intensive care unit.
The mortality analysis included 57,916 patients treated at 124 centers. Patient
characteristics for patients treated at pediatric TQIP centers of differing levels of simulation use
demonstrate statistically significant differences for most patient-level characteristics, but the
differences were small (effect size ≤0.20) for all variables except mechanism of injury (Table
1b). Centers with a high-volume of simulation-based training use had significantly less motor
vehicle crash occupants (14% versus 21-23%, p<0.01, d=-0.25) and significantly more patients
treated after a fall (53% versus 41%, p<0.01, d=0.24) when compared to centers with a low
volume of or no simulation-based training use.
Average center-specific unadjusted mortality rate was lowest in centers with a high
volume of simulation-based training use (Table 2). Centers using both low- and high-volume
Page 1 2 of 2 9

Simulation and Pediatric Trauma Outcomes

training had significantly lower mortality when compared to centers that do not use simulation.
Additional adjustment for ACS trauma center verification level and for annual trauma admission
volume did not impact these results. Using transfer patients within centers as internal controls,
we did not see a significant impact of simulation use on risk-adjusted mortality for these patients
that underwent initial resuscitation at a referring center (Figure 1).
We observed no significant difference in time to intubation, head CT, emergent
craniotomy, or surgery for hemorrhage control between centers of differing levels of simulation-
based training use (Table 3). Trauma patients with an initial ED GCS total ≤ 8 had a slightly
higher frequency of intubation at centers using high-volume training compared to centers not
using simulation-based training (94% versus 91%, p=0.02, Table 4).

Page 1 3 of 2 9

Simulation and Pediatric Trauma Outcomes

DISCUSSION
This retrospective study of simulation-based training use in ACS Pediatric TQIP centers
found an association between increased use of training and lower risk-adjusted mortality for
injured children. We did not find a difference in time to critical interventions, evaluations, or
procedures, but found an increased rate of intubation for patients with a GCS ≤ 8 in centers with
a high volume of training. These findings suggest that simulation-based training may improve
resuscitation quality and outcomes in pediatric trauma patients, but improved outcomes may not
be mediated by faster time to critical interventions, evaluations, and procedures.
Simulation-based training has been shown to improve trauma team performance, mainly
measured by number of tasks completed and time to task completion in acute resuscitation.
18–22

Few studies have found an association between training with simulation and improved trauma
resuscitation performance in a real-world setting.
19,21,27
Simulation-based training has been
associated with faster times to critical procedures and evaluations, including time to
endotracheal intubation, time to head CT, and time to the operating room in experimental
studies using this methodology as an educational intervention.
21
Systematic implementation of
simulation-based training for all providers in a trauma center led to faster time to critical
operations after implementation.
27
In addition to faster resuscitation processes, simulation-
based training has also been shown to decrease missed critical steps during trauma
resuscitation.
19
While none of these single-center studies demonstrated a mortality benefit from
training with simulation, they all postulate benefit from improved resuscitation times and faster
time to critical procedures.
Based on our literature review, we hypothesized that simulation-based training would be
associated with a more efficient resuscitation with shorter times to critical evaluations and
procedures, but we found no difference in time to intubation, head CT, emergent craniotomy or
emergent surgery for hemorrhage control. Conversely, we did demonstrate an association with
Page 1 4 of 2 9

Simulation and Pediatric Trauma Outcomes

decreased risk-adjusted mortality, which brings into question the mechanism by which training
with simulation may improve mortality. Our findings suggest that earlier performance of these
procedures is not necessarily the only factor associated with lower mortality. Faster
resuscitation time has been associated with improved survival,
31
and early intubation
13
and
faster time to laparotomy
12,14,15
have been shown to improve outcomes. Among patients with
severe TBI requiring craniotomy, however, the association between time to surgery and
outcome is not certain.
12,16,17,32–34
The outcome of some reversible clinical scenarios, such as
hemorrhagic shock, may be more dependent on rapid intervention.
14,15,35
We did not find an
association between the use of simulation-based training and time to surgery for hemorrhage
control, but the median time to laparotomy was much longer than observed by previous adult
data (10-36 min).
14,15
This longer time to surgery for hemorrhage control in our cohort suggests
that many of the patients we defined as ‘emergent’ may have not truly had immediate life-
threatening hemorrhage, and we would not expect simulation-based training to have a profound
impact on less urgent operative times. Furthermore, using our definition of ‘emergent
craniotomy’, we demonstrated times to OR of approximately two hours – again questioning the
the true emergent nature of these operations, as one would expect a “crash” craniotomy to be in
the operating room in 30-60 minutes from arrival to the trauma bay. In the absence of more
granular physical exam findings and GCS trends over time, we are not able to better define a
cohort of ‘emergent’ craniotomy patients using the TQIP dataset which may have led to our lack
of demonstrating an impact on time to critical operations.
The survival advantage found in our study may be attributed to center-specific factors
that are not measured in this study. While we have attributed the impact on mortality to
simulation-based training, simulation use may alternatively serve as a proxy for other factors,
such as organizational culture that embraces teamwork, communication, and quality
improvement. These latter factors may directly improve outcomes in the centers that have
Page 1 5 of 2 9

Simulation and Pediatric Trauma Outcomes

adopted simulation use. The use of simulation may also be a marker for more institutional
resources, as the use of simulation requires significant financial investment. Children treated at
pediatric trauma centers may have improved mortality compared to those treated at adult
trauma centers,
4,6,7,36
but we found no impact of center verification level (as a marker for
resources) on the association of simulation use with improved mortality. Centers that use a
higher-volume of simulation may also be more likely to adopt evidence-based practices. We
attempted to control for these unmeasured factors using transfer-in patients from the same
institutions as internal controls, assuming that hospital-wide factors would have an impact on
both acutely resuscitated patients and transferred-in patients equally. We only saw an
association with a mortality benefit in acutely resuscitated patients and not in transfer-in patients
that were resuscitated at referring institutions – suggesting that whatever factor we are
measuring does lead to some improvement in the initial resuscitation.
There are several limitations to this study. Our analysis was limited to centers that
participate in ACS Pediatric TQIP – centers that, by definition, have significant resources. Our
findings, therefore, may not be generalizable to all trauma centers. We would expect, however,
the impact of simulation to be larger in lower-resourced centers that have more opportunity for
improvement in initial resuscitation practices. Response bias may have contributed to the
results as the frequency of simulation use was based on a survey in which we had a 25% non-
response rate. Outcomes in non-response centers were similar to those in centers that do not
use simulation suggesting the effect size may indeed be larger than we have shown. We
accounted for this limitation by including non-responders as a separate category in our
multivariable analysis. Outcomes in non-response centers were similar to those in centers that
do not use simulation, suggesting the effect size may indeed be larger than we observed.
Our findings are inherently subject to issues common to a retrospective design, most
prominent of which is that the level of simulation-based training was not randomly assigned.
Page 1 6 of 2 9

Simulation and Pediatric Trauma Outcomes

High-volume simulation centers had lower annual trauma admission volumes, suggesting the
use of SBT may be an attempt to supplement provider and team experience in the presence of
lower clinical volumes. We noted minor differences in patient demographics and injury
characteristics and significant differences in mechanism of injury between trauma centers using
differing levels of simulation-based training. These variables were all adjusted for in the
multivariable model, which should limit the impact of these differences. The cross-sectional
design limits our ability to assess the impact of simulation on individual programs over time.
Low-volume and lower-performing centers may have implemented SBT to address inefficiencies
in resuscitation. The lack of difference, therefore, may reflect a true improvement from baseline
at these centers. Unmeasured confounding likely remained despite controlling for known
independent predictors of mortality. It also should be noted that the median frequency of
simulation use in high-volume centers was 22 sessions over two years – or slightly less than
once per month. While the ideal frequency with which teams should undergo simulation-based
training, the expected effect size from this infrequent use may be modest.

Page 1 7 of 2 9

Simulation and Pediatric Trauma Outcomes

CONCLUSIONS
Risk-adjusted mortality is lower among children treated at pediatric TQIP centers that
use simulation. Whether this effect is directly attributable to simulation use or a center-level
factor that simulation-based training use serves as a proxy for remains unknown. Simulation use
was not associated with faster time to critical evaluations and procedures, but intubation
frequency for patients with low GCS was higher in centers using high-volume simulation,
suggesting that quality (or fewer missed steps) may be a greater mediator of mortality than the
speed of the resuscitation or its critical components. Prospective evaluation of simulation-based
training within trauma centers is needed to show a casual improvement in mortality over time,
as is further delineation of the mechanisms by which simulation contributes to a clinical benefit
for trauma patients.

Page 1 8 of 2 9

Simulation and Pediatric Trauma Outcomes

BIBLIOGRAPHY
1. Borse NN, Gilchrist J, Dellinger AM, et al. Unintentional childhood injuries in the United
States: key findings from the CDC childhood injury report. J Saf Res. 2009;40:71–74.
2. National Center for Injury Prevention and Control, CDC using WISQARS. Ten Leading
Causes of Death by Age Group, United States - 2015 Available from:
https://www.cdc.gov/injury/wisqars/LeadingCauses.html. Accessed May 17, 2017.
3. McLaughlin C, Zagory JA, Fenlon M, et al. Timing of Mortality in Pediatric Trauma Patients:
A National Trauma Databank Analysis. J Pediatr Surg. . Epub ahead of print October 8,
2017. DOI: 10.1016/j.jpedsurg.2017.10.006.
4. Amini R, Lavoie A, Moore L, et al. Pediatric trauma mortality by type of designated hospital
in a mature inclusive trauma system. J Emerg Trauma Shock. 2011;4:12–19.
5. Potoka DA, Schall LC, Ford HR. Improved functional outcome for severely injured children
treated at pediatric trauma centers. J Trauma. 2001;51:824-832; discussion 832-834.
6. Potoka DA, Schall LC, Gardner MJ, et al. Impact of pediatric trauma centers on mortality in
a statewide system. J Trauma. 2000;49:237–245.
7. Sathya C, Alali AS, Wales PW, et al. Mortality Among Injured Children Treated at Different
Trauma Center Types. JAMA Surg. 2015;150:874–881.
8. Walther AE, Falcone RA, Pritts TA, et al. Pediatric and adult trauma centers differ in
evaluation, treatment, and outcomes for severely injured adolescents. J Pediatr Surg.
2016;51:1346–1350.
9. Chen EH, Cho CS, Shofer FS, et al. Resident exposure to critical patients in a pediatric
emergency department. Pediatr Emerg Care. 2007;23:774–778.
10. Avraham JB, Bhandari M, Frangos SG, et al. Epidemiology of paediatric trauma presenting
to US emergency departments: 2006-2012. Inj Prev J Int Soc Child Adolesc Inj Prev. .
Epub ahead of print October 22, 2017. DOI: 10.1136/injuryprev-2017-042435.
11. Mittiga MR, Geis GL, Kerrey BT, et al. The spectrum and frequency of critical procedures
performed in a pediatric emergency department: implications of a provider-level view. Ann
Emerg Med. 2013;61:263–270.
12. Davidson GH, Maier RV, Arbabi S, et al. Impact of operative intervention delay on pediatric
trauma outcomes. J Trauma Acute Care Surg. 2012;73:162–167.
13. Miraflor E, Chuang K, Miranda MA, et al. Timing is everything: delayed intubation is
associated with increased mortality in initially stable trauma patients. J Surg Res.
2011;170:286–290.
14. Meizoso JP, Ray JJ, Karcutskie CA, et al. Effect of time to operation on mortality for
hypotensive patients with gunshot wounds to the torso: The golden 10 minutes. J Trauma
Acute Care Surg. 2016;81:685–691.
Page 1 9 of 2 9

Simulation and Pediatric Trauma Outcomes

15. Barbosa RR, Rowell SE, Fox EE, et al. Increasing time to operation is associated with
decreased survival in patients with a positive FAST examination requiring emergent
laparotomy. J Trauma Acute Care Surg. 2013;75:S48-52.
16. Dinh MM, Bein K, Roncal S, et al. Redefining the golden hour for severe head injury in an
urban setting: the effect of prehospital arrival times on patient outcomes. Injury.
2013;44:606–610.
17. Tien HCN, Jung V, Pinto R, et al. Reducing time-to-treatment decreases mortality of
trauma patients with acute subdural hematoma. Ann Surg. 2011;253:1178–1183.
18. Auerbach M, Roney L, Aysseh A, et al. In situ pediatric trauma simulation: assessing the
impact and feasibility of an interdisciplinary pediatric in situ trauma care quality
improvement simulation program. Pediatr Emerg Care. 2014;30:884–891.
19. Steinemann S, Berg B, Skinner A, et al. In situ, multidisciplinary, simulation-based
teamwork training improves early trauma care. J Surg Educ. 2011;68:472–477.
20. Holcomb JB, Dumire RD, Crommett JW, et al. Evaluation of trauma team performance
using an advanced human patient simulator for resuscitation training. J Trauma.
2002;52:1078-1085; discussion 1085-1086.
21. Capella J, Smith S, Philp A, et al. Teamwork training improves the clinical care of trauma
patients. J Surg Educ. 2010;67:439–443.
22. Falcone RA Jr, Daugherty M, Schweer L, et al. Multidisciplinary pediatric trauma team
training using high-fidelity trauma simulation. J Pediatr Surg. 2008;43:1065–1071.
23. Andreatta P, Saxton E, Thompson M, et al. Simulation-based mock codes significantly
correlate with improved pediatric patient cardiopulmonary arrest survival rates. Pediatr Crit
Care Med. 2011;12:33–38.
24. Popp J, Yochum L, Spinella PC, et al. Simulation training for surgical residents in pediatric
trauma scenarios. Conn Med. 2012;76:159–162.
25. Couto TB, Kerrey BT, Taylor RG, et al. Teamwork skills in actual, in situ, and in-center
pediatric emergencies: performance levels across settings and perceptions of comparative
educational impact. Simul Healthc J Soc Simul Healthc. 2015;10:76–84.
26. Jensen AR, McLaughlin C, Wong CF, et al. Simulation-based training for trauma
resuscitation among ACS TQIP-Pediatric centers: Understanding prevalence of use,
associated center characteristics, training factors, and implementation barriers. Am J Surg.
2019;217:180–185.
27. Murphy M, Curtis K, Lam MK, et al. Simulation-based multidisciplinary team training
decreases time to critical operations for trauma patients. Injury. 2018;49:953–958.
28. Marr M, Hemmert K, Nguyen AH, et al. Team play in surgical education: a simulation-
based study. J Surg Educ. 2012;69:63–69.
Page 2 0 of 2 9

Simulation and Pediatric Trauma Outcomes

29. American College of Surgeons. Pediatric TQIP Benchmark Report Cycle References:
Spring 2016 Cycle. Chicago, IL.
30. Newgard CD, Fildes JJ, Wu L, et al. Methodology and analytic rationale for the American
College of Surgeons Trauma Quality Improvement Program. J Am Coll Surg.
2013;216:147–157.
31. Townsend RN, Clark R, Ramenofsky ML, et al. ATLS-based videotape trauma
resuscitation review: education and outcome. J Trauma. 1993;34:133–138.
32. Walcott BP, Khanna A, Kwon C-S, et al. Time interval to surgery and outcomes following
the surgical treatment of acute traumatic subdural hematoma. J Clin Neurosci Off J
Neurosurg Soc Australas. 2014;21:2107–2111.
33. Wilberger JE, Harris M, Diamond DL. Acute subdural hematoma: morbidity and mortality
related to timing of operative intervention. J Trauma. 1990;30:733–736.
34. Matsushima K, Inaba K, Siboni S, et al. Emergent operation for isolated severe traumatic
brain injury: Does time matter? J Trauma Acute Care Surg. 2015;79:838–842.
35. Fox EE, Holcomb JB, Wade CE, et al. Earlier Endpoints are Required for Hemorrhagic
Shock Trials Among Severely Injured Patients. Shock Augusta Ga. 2017;47:567–573.
36. Webman RB, Carter EA, Mittal S, et al. Association Between Trauma Center Type and
Mortality Among Injured Adolescent Patients. JAMA Pediatr. 2016;170:780–786.

Page 2 1 of 2 9

Simulation and Pediatric Trauma Outcomes

TABLES

Table 1a. Center Characteristics by Level of Simulation Use.

Total Number of Pediatric Simulation Sessions, 2014-2015
Omnibus
p-value
†

Unknown None 1-10 11+
Number of Facilities 30 40 19 35

Quantification of Clinical Trauma Volume

Annual Trauma Admissions 1,682.5 [873-2,690] 1,829.5 [1,233-2,460] 1,462 [747-2,847] 1,367 [776-1,987] 0.01
Annual Admissions with ISS >16, Excluding Transfers 297.5 [162-543] 346 [204.5-622.5] 289 [105-855] 182 [116-500] 0.02
Annual Admissions, Age ≤ 14yo 398.5 [169-607] 229.5 [155.5-588.5] 347 [261-555] 355 [185-922] 0.32
Annual Admissions, Age ≤ 14yo with ISS >16 57 [24-83] 37 [21.5-60] 54 [33-82] 46 [18-115] 0.82
Annual Admissions, Age ≤ 14yo with ISS > 16, excluding
transfers
21 [10-34] 17 [11.5-31] 24 [11-33] 16 [10-47] 0.65
Hospital Teaching Status

University Hospital 19 (63.3) 25 (62.5) 13 (68.4) 26 (74.3)
0.75 Community Teaching Hospital 10 (33.3) 12 (30.0) 5 (26.3) 9 (25.7)
Nonteaching Hospital 1 (3.3) 3 (7.5) 1 (5.3) 0 (0.0)
Hospital Type

For Profit 3 (10.0) 3 (7.5) 1 (5.3) 0 (0.0)
0.28
Not for Profit 27 (90.0) 37 (92.5) 18 (94.7) 35 (100.0)
ACS Pediatric Verification

Level 1 Pediatric Verification 8 (28.6) 7 (18.0) 9 (47.4) 17 (50.0)
0.01 Level 2 Pediatric Verification 6 (21.4) 16 (41.0) 7 (36.8) 5 (14.7)
Not Verified for Pediatric Trauma 14 (50.0) 16 (41.0) 3 (15.8) 12 (35.3)
State Pediatric Designation

Level 1 State Designation 12 (40.0) 15 (37.5) 8 (42.1) 23 (65.7)
0.03 Level 2 State Designation 5 (16.7) 16 (40.0) 4 (21.1) 4 (11.4)
No State Designation 13 (43.3) 9 (22.5) 7 (36.8) 8 (22.9)
Patient Care Characteristics

Associated with a Pediatric Hospital 24 (80.0) 32 (80.0) 16 (84.2) 29 (82.9) 0.99
Have a Pediatrics Ward 30 (100.0) 39 (97.5) 19 (100.0) 35 (100.0) 1.00
Have a Pediatric Intensive Care Unit 28 (93.3) 40 (100.0) 19 (100.0) 35 (100.0) 0.08
Transfer severely injured children to other centers 5 (16.7) 3 (7.5) 1 (5.3) 1 (2.9) 0.25
Provide all Acute Care Services to Injured Children 29 (96.7) 37 (92.5) 18 (94.7) 35 (100.0) 0.40
Data expressed as median [IQR] or N(%).
†
One-way analysis of variance (ANOVA) for continuous variables and Fisher’s exact test for categorical variables.
Page 2 2 of 2 9

Simulation and Pediatric Trauma Outcomes

Table 1b: Demographic, Physiologic, and Injury Characteristics for Children Treated at Pediatric TQIP Centers
by Level of Simulation Use.

Unknown No Sim Use 0-10 hours 11+ hours

Number of Facilities 30 39 19 35

Patients (All) 14,576 17,118 8,645 19,114

Transfer In 7,307 (50.1) 7,434 (43.4) 4,556 (52.7) 8,976 (47.0)

Patients (No Transfers) 7,269 9,684 4,089 10,138 p-value effect size
Race - White 4263 (59.6) 6031 (65.7) 2296 (57.1) 6292 (64.1) <0.01 ≤0.18
Gender - Male 4619 (63.5) 6267 (64.7) 2620 (64.1) 6387 (63) 0.08

Age 8 (4-14) 9 (4-15) 10 (4-15) 8 (4-13) <0.01 ≤0.17
ED GCS Motor Score 6 (6-6) 6 (6-6) 6 (6-6) 6 (6-6) <0.01 ≤0.09
ED Systolic Blood Pressure 120 (110-132) 120 (110-132) 122 (111-134) 119 (108-130) <0.01 ≤0.20
ED Pulse 103 (88-121) 103 (88-121) 104 (88-123) 104 (88-121) 0.36 ≤0.04
Prehospital Cardiac Arrest 62 (0.9) 83 (0.9) 29 (0.7) 51 (0.5) <0.01 ≤0.09
Comorbidity

Functional Dependence 18 (0.2) 45 (0.5) 14 (0.3) 35 (0.3) 0.11

Substance Abuse 194 (2.7) 302 (3.2) 169 (4.1) 172 (1.7) <0.01 ≤0.15
Congenital Anomalies 73 (1) 160 (1.7) 62 (1.5) 182 (1.8) <0.01 ≤0.06
Prematurity 111 (1.5) 162 (1.7) 60 (1.5) 173 (1.7) 0.62

Mechanism

Firearm 206 (2.8) 376 (3.9) 164 (4) 228 (2.2) <0.01 ≤0.10
Motorcyclist 42 (0.6) 121 (1.2) 40 (1) 60 (0.6) <0.01 ≤0.07
Motor Vehicle Crash - Occupant 1247 (17.2) 2069 (21.4) 962 (23.5) 1407 (13.9) <0.01 ≤0.25
†

Cut/pierce 109 (1.5) 149 (1.5) 51 (1.2) 151 (1.5) 0.62

Fall 3495 (48.1) 4013 (41.4) 1661 (40.6) 5341 (52.7) <0.01 ≤0.24
‡

Pedestrian 704 (9.7) 1105 (11.4) 429 (10.5) 1043 (10.3) <0.01 ≤0.06
Other 657 (9) 765 (7.9) 374 (9.1) 797 (7.9) <0.01 ≤0.05
Worst AIS Post-dot Severity Score

Head 0 (0-2) 0 (0-2) 0 (0-2) 0 (0-2) <0.01 ≤0.19
Face 0 (0-0) 0 (0-1) 0 (0-0) 0 (0-0) <0.01 ≤0.10
Neck 0 (0-0) 0 (0-0) 0 (0-0) 0 (0-0) <0.01 ≤0.04
Chest 0 (0-0) 0 (0-0) 0 (0-0) 0 (0-0) <0.01 ≤0.18
Abdomen 0 (0-0) 0 (0-0) 0 (0-0) 0 (0-0) <0.01 ≤0.09
Spine 0 (0-0) 0 (0-0) 0 (0-0) 0 (0-0) <0.01 ≤0.04
Upper Extremity 0 (0-2) 0 (0-2) 0 (0-2) 0 (0-2) <0.01 ≤0.15
Lower Extremity 0 (0-2) 0 (0-2) 0 (0-2) 0 (0-2) 0.04 ≤0.03
Severe TBI
1
266 (3.7) 422 (4.4) 198 (4.8) 287 (2.8) <0.01 ≤0.10
Infant with Severe TBI
1
13 (4.9) 10 (2.4) 6 (3.0) 10 (3.5) 0.35

Firearm Injury with Hypotension 3 (1.1) 10 (2.4) 5 (2.5) 8 (2.8) 0.53

Data expressed as frequency (%) or median (interquartile range).
1
AIS Head ≥3 and GCS≤8. Reported effect sizes represent the
largest post-hoc pairwise difference between groups. Variables with effect size differences >0.20 are detailed as follows: Mechanism-
MVC Occupant: unknown simulation versus no simulation, p<0.01, d=0.11; no simulation versus 0-10 hours, p<0.01, d=0.05; 0-10
hours versus 11+ hours of simulation, p<0.01, d=0.25. Mechanism-Fall: unknown simulation versus no simulation, p<0.01, d=0.13; no
simulation versus 0-10 hours, p=0.37; 0-10 hours versus 11+ hours of simulation, p<0.01, d=0.24.
Page 2 3 of 2 9

Simulation and Pediatric Trauma Outcomes

Table 2: Center-Specific Mortality, Risk-Adjusted Center-Specific Mortality, and Sensitivity Analysis of Center-Specific Risk-Adjusted Mortality
Including Additional Adjustment for ACS Pediatric Trauma Center Verification and for Annual Trauma Volume for Pediatric TQIP Centers by Level of
Simulation Use.

Total Number of Pediatric Simulation Sessions, 2014-2015

Unknown None 1-10 11+

N=30 N=40 N=19 N=35
Unadjusted center-specific mortality Rate 1.37% 1.88% 1.59% 1.03%
Risk-adjusted mortality (TQIP model) 0.80 [0.51-1.26] Ref 0.55 [0.32-0.96] 0.58 [0.37-0.92]
Risk-adjusted mortality with additional adjustment for ACS
verification level and annual trauma volume 0.80 [0.51-1.27] Ref 0.56 [0.33-0.94] 0.65 [0.41-1.03]

Data presented as percentages or odds ratio with 95% confidence interval. TQIP: Trauma Quality Improvement Program.
Page 2 4 of 2 9

Simulation and Pediatric Trauma Outcomes

Table 3: Resuscitation Process Times for Centers of Varying Levels of Simulation Use and Adjusted Odds Ratios Comparing Centers Using High-
Volume Simulation to Centers Using No Simulation.

Unknown Simulation No Simulation Low Volume Simulation High Volume Simulation Univariate Multivariable

N Mean [95% CI] N Mean [95% CI] N Mean [95% CI] N Mean [95% CI] Omnibus p-value Adjusted OR [95% CI]
Time to Endotracheal Intubation

All Patients with GCS 8 or less 111 8 [7-10] 175 9 [8-11] 83 9 [7-11] 117 10 [8-12] 0.44 1.00 [0.80-1.26]
Isolated TBI with GCS 8 or less 41 9 [6-11] 48 8 [6-11] 21 8 [5-11] 33 12 [8-16] 0.31 0.80 [0.55-1.16]
Time to Head CT

All Patients with GCS 8 or less 183 21 [18-26] 296 27 [23-33] 136 27 [21-34] 220 25 [21-29] 0.30 1.17 [0.91-1.50]
Isolated TBI with GCS 8 or less 53 21 [16-27] 68 23 [18-29] 38 22 [16-29] 61 24 [19-31] 0.84 1.0 [0.74-1.4]
Time to Emergent Craniotomy

All patients 43 107 [91-128] 83 128 [113-146] 35 113 [93-136] 53 128 [109-150] 0.32 1.07 [0.84-1.35]
Isolated TBI 24 107 [85-135] 33 122 [101-148] 21 117 [92-148] 28 124 [101-152] 0.80 0.92 [0.72-1.17]
Time to Surgery for

Hemorrhage Control 55 70 [54-90] 101 53 [43-66] 54 51 [39-68] 70 61 [48-76] 0.31 0.97 [0.64-1.21]
Page 2 5 of 2 9

Simulation and Pediatric Trauma Outcomes

Table 4: Frequency of Endotracheal Intubation for Pediatric Trauma Patients that Present with a Glasgow Coma Scale of 8 or Less
by Trauma Center Level of Simulation Use.

Total Number of Pediatric Simulation Sessions, 2014-2015

No Survey No Simulation 1-10 11+ TOTAL
Omnibus p-
value
†

N 784 932 486 882 3,084

Not intubated 72 (9.1) 84 (9.0) 55 (11.3) 53 (6.0) 264 0.02
Intubated 712 (90.8) 848 (91.0) 431 (88.7) 829 (94.0) 2,820

Data expressed as N(%).
†
Chi-square test of independence.

Page 2 6 of 2 9

Simulation and Pediatric Trauma Outcomes

Supplemental Table 1: Independent Predictors of Mortality for Acutely Resuscitated Pediatric Trauma Patients (Excluding Patients
Transferred-In) Treated at Pediatric TQIP Centers from 2015-2017.
Mortality Risk Factor Odds Ratio [95% CI]
Age 0.98 [0.95 - 1.00]
Male 0.86 [0.67 - 2.71]
White Race 0.81 [0.64 - 1.04]
GCS Motor Score 0.56 [0.52 - 0.61]
Age-Normalized ED Systolic Blood Pressure 1.02 [1.01 - 1.03]
Age-Normalized ED Pulse 1.03 [1.00 - 1.02]
Prehospital Cardiac Arrest 14.66 [10.17 - 21.13]
Functional Dependence 2.25 [0.37 - 13.75]
Substance Abuse 0.16 [0.06 - 0.40]
Single Worst Injury Risk Ratio 0.39 [0.33 - 0.47]
Single Worst Injury Risk Ratio*TBI 1.28 [1.15 - 1.44]
Mechanism - Firearm Injury 3.52 [2.19 - 5.67]
Mechanism - Motorcyclist 3.41 [1.12 - 10.35]
Mechanism - Motor Vehicle Occupant 1.87 [1.26 - 2.79]
Mechanism - Pedestrian 2.09 [1.33 - 3.28]
Mechanism - Other 1.89 [1.26 - 2.84]
AIS Face 0.81 [0.70 - 0.85]
AIS Abdomen 1.22 [1.12 - 1.32]
AIS Spine 0.76 [0.68 - 0.85]
Severe TBI 3.40 [2.00 - 5.78]

N=57,916 patients across 124 pediatric TQIP centers.
Page 2 7 of 2 9

Simulation and Pediatric Trauma Outcomes

FIGURES
Figure 1. Risk-Adjusted Mortality in Transferred and Non-Transferred Trauma Patients Treated
at Centers Using No Simulation, Low-Volume (0-10 hrs) Simulation, High-Volume (11+ hrs)
Simulation, and Unknown Simulation Use (Survey Non-Respondents).

Page 2 8 of 2 9

Simulation and Pediatric Trauma Outcomes

FUNDING ACKNOWLEDGEMENT
This work was supported by grant #KFVS6290 from the National Institute for Child Health and
Development (NICHD) and grant #KL2TR001854 from the National Center for Advancing
Translational Science (NCATS) of the U.S. National Institutes of Health. The content is solely
the responsibility of the authors and does not necessarily represent the official views of the
National Institutes of Health.

Page 2 9 of 2 9

Simulation and Pediatric Trauma Outcomes

HUMAN SUBJECTS APPROVAL INFORMATION

Children’s Hospital Los Angeles Institutional Review Board Exemption # CHLA-16-00341.

Abstract (if available)

Linked assets

University of Southern California Dissertations and Theses

Conceptually similar

PDF

Cardiac function in children and young adults treated with MEK inhibitors: a retrospective cohort study of routinely collected health data

PDF

Association of Pediatric Early Warning Score with early intensive care unit readmission

PDF

Medical comorbidities and multiple sclerosis in the Hispanic population: linking healthcare disparities

PDF

The association between Echocardiography use in the Intensive Care Unit and 28-day mortality among patients with septic shock: a systematic review

PDF

The effect of cytomegalovirus on gene expression of pediatric acute lymphoblastic leukemia

PDF

Clinical outcomes of allogeneic hematopoietic stem cell transplant in acute lymphoblastic leukemia patients: a quality improvement project and systematic review meta-analysis

PDF

Association of chronic obstructive pulmonary disease and mortality following thoracic and complex endovascular aortic repair: a retrospective cohort study

PDF

Predictors of thrombosis in hospitalized children with central venous catheters: a multi-center predictive study from the CHAT Consortium

PDF

The cost of opioid use in high-risk hospitalized infants

PDF

Integrated management of atrial fibrillation in women in an underserved, safety-net health care system: a multicenter, single health system randomized control efficacy trial protocol

PDF

A cross-sectional study of the association of PTH on bone quality across levels of propionic acid among adult patients with uremia

PDF

Risk factors for unanticipated hospitalizations in children and youth with spina bifida at an urban children’s hospital: a cross-sectional study

PDF

Racial/ethnic differences in colorectal cancer patient experiences, health care utilization and their association with mortality: findings from the SEER-CAHPS data

PDF

The association between inhaled nitric oxide treatment and ICU mortality and 28-day ventilator free days in children with acute respiratory distress syndrome

PDF

Statistical analysis of a Phase II study of AMG 386 versus AMG 386 combined with anti-VEGF therapy in patients with advanced renal cell carcinoma

PDF

Survival trends and related outcomes of survivors of childhood and young adult cancer

PDF

A novel risk-based treatment strategy evaluated in pediatric head and neck non-rhabdomyosarcoma soft tissue sarcomas (NRSTS) patients: a survival analysis from the Children's Oncology Group study...

PDF

An assessment of impact of early local progression on subsequent risk for the treatment failure in adolescent and young adult patients with non-metastatic osteosarcoma

PDF

Healthcare affordability among adults with liver disease from 2004-2018 in the United States: a national cohort study

PDF

Chronic eye disease epidemiology in the multiethnic ophthalmology cohorts of California study

Asset Metadata

Creator Jensen, Aaron R. (author)

Core Title Simulation-based training is associated with lower risk-adjusted mortality in ACS Pediatric TQIP centers

School Keck School of Medicine

Degree Master of Science

Degree Program Clinical, Biomedical and Translational Investigations

Publication Date 10/27/2020

Defense Date 02/20/2019

Publisher University of Southern California (original), University of Southern California. Libraries (digital)

Tag OAI-PMH Harvest,outcomes,pediatric,resuscitation,simulation-based training,trauma

Format application/pdf (imt)

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Upperman, Jeffrey S. (committee chair), Chang, Todd (committee member), Meeker, Daniella (committee member), Patino-Sutton, Cecilia (committee member)

Creator Email aaron.jensen@ucsf.edu,arjensenmd@gmail.com

Permanent Link (DOI) https://doi.org/10.25549/usctheses-c89-153481

Unique identifier UC11660965

Identifier etd-JensenAaro-7333.pdf (filename),usctheses-c89-153481 (legacy record id)

Legacy Identifier etd-JensenAaro-7333.pdf

Dmrecord 153481

Document Type Thesis

Format application/pdf (imt)

Rights Jensen, Aaron R.

Type texts

Source University of Southern California (contributing entity), University of Southern California Dissertations and Theses (collection)

Access Conditions The author retains rights to his/her dissertation, thesis or other graduate work according to U.S. copyright law. Electronic access is being provided by the USC Libraries in agreement with the a...

Repository Name University of Southern California Digital Library

Repository Location USC Digital Library, University of Southern California, University Park Campus MC 2810, 3434 South Grand Avenue, 2nd Floor, Los Angeles, California 90089-2810, USA