1. VPS User Conference| March 24-26, 2015
PEDIATRIC TRAUMA ASSESSMENT AND MANAGEMENT DATABASE VARIATION IN THE MANAGEMENT OF TBI
Katherine T. Flynn-O’Brien, MD
Mary E. Fallat, MD
Tom B. Rice, MD
Christine M. Gall, RN, MS, DrPH
Frederick P. Rivara, MD
Thank you for the opportunity to speak today.
I am pleased to share with you data from a multi-institutional pilot study, called the Pediatric Trauma Assessment and Management Database
I hope to show you the utility of this database by taking a closer look at the management of pediatric TBI.
VPS User Conference| March 24-26, 2015

2. PTAM The problem Traumatic brain injury (TBI) 500,000 ED visits2
database
The problem
Leading cause of death and disability
Limited ability to study pediatric TBI
Traumatic brain injury (TBI)
PECARN
National Trauma Databank/Peds TQIP
UDSMR
FITBIR – Federal Interagency TBI Research
The burden of disease from pediatric trauma is profound
We are limited in our ability to study pediatric trauma the way we all would like…
By that I mean:
We know…
important…Non-mortality outcomes, functional status
essential…Processes of care that impact outcomes – improve the care we provide
current…Healthcare climate, necessary to provide benchmarking metrics - particularly in a profession focused on QI
Difficult to make of all this possible through a single database or registry
Taking a closer look at Pediatric TBI population highlights these difficulties:
- Peds TQIP – captures only the first CT scan, no pupillary data/lab data/hypexia – prevalent in the neurosurgical literature and consistently cited as prognostic factors
- UDSMR - Uniform data systems for medical rehabilitation (WeeFim/Fim)
- Fitbur – Federal Interagency Traumatic Brain Injury Research
National informatics system for TBI research | yet to see how it will work
*****************************************************************************
NTDB/on-boarding PedsTQIP – robust and provides risk adjusted mortality and LOS metrics for bench marking across sites,
 however lacks non-mortality outcomes (functional status), limited in process of care data such as imaging data, bedside procedures, labs values, and more
Specialty-specific databases/repositories –
“Towards a National Pediatric Musculoskeletal Trauma Outcomes Registry: the Pediatric Orthopaedic Trauma Outcomes Research Group (POTORG) experience.” 2006
National efforts in TBI – FITBIR (collaborative centralized informatics system for TBI research)
State-based initiates, more detailed, but limited in generalizability
With all this in mind, and in the context of the current healthcare climate’s fiscal constraints, we set out to
To….
Create a comprehensive pediatric trauma database for critically injured children combining existing databases
Extend from pre-hospital care  discharge
Include non-mortality outcomes
*********************************************************************
NSCOT – has set a precedent as the gold standard in adults
Large (5000 adults)
Multi-institutional (69 hospitals, 12 states)
(primarily) Prospective (retrospectively identified for inclusion)
Methodologically rigorous and comprehensive
In addition to mortality, outcomes included
Morbidity and QOL data at 3 and 12 months post-discharge
NSCOT-like study in pediatrics, HOWEVER - to execute this in it’s ideal form, tailored to pediatric trauma  $$$$
The importance of Morbidity and QOL data gaining increased traction in all post-surgical & post-trauma care  * SAVE BETTER LIVES
MORE CRITICAL in KIDS where MORTALITY RARE
NPTR: was promising, and even began collecting non-morality outcomes in phase II-- however operating in isolating it was not sustainable & was discontinued in 2000
Implemented in 1985
Collected data on >100,000 before discontinuation in 2000
Each stage included more detailed and pertinent information
phase II – FIM
phase III – detailed imaging information
While promising – funding was not sustainable
NTDB | Pediatric TQIP
While very robust
Limited in part by lack of non-mortality outcomes, lack of laboratory data, and lack of comprehensive imaging data
Proven successful in presenting risk adjusted analyses in pilot studies)
TBI burden
Traumatic brain injury (TBI)
1 million US children annually1
Leading cause of death1
500,000 ED visits2
1 billion USD for hospitalizations3
1 Center for Head Injury Services
2 Faul, 2010
3 Schneiler, 2006
Source: Faul M, Xu L, Wald MM, Coronado VG. Traumatic Brain Injury in the United States: Emergency Department Visits, Hospitalizations and Deaths 2002 – Atlanta (GA): Centers for Disease Control and Prevention, National Center for Injury Prevention and Control; 2010.
CDC estimates for children < 18 | WISQARS 2012 (under 18)
~ deaths : deaths, Crude rate 11.75/100,000
~ 280,000 injuries req. hospitalization : 279,907 injuries requiring hospitalization (or transfer), Crude rate of / 100,000
~ 7.2M ED visits : 7,252,434 treated and released, Crude rate /100,000
Due to deaths alone $15,079,463,000 (2005 USD) combined costs – direct medical + work loss costs
Due to hospitalizations alone $23,176,242,000 (2005 USD) combined costs – direct medical + work loss costs
Due to ED visits alone $27,263,819,000 (2005 USD) combined costs – direct medical + work loss costs
CDC estimated >65 billion USD IN combined cost of injury for children < 18 in 2012
FROM direct medical and work loss costs due to injuries

3. PTAM
database
Objectives
Create a comprehensive pediatric trauma database to assess quality of care in critically injured children utilizing minimal new resources.
Evaluate outcomes related to imaging practices and ICPM utilization in children with TBI admitted to the PICU.
With all this in mind, and in the context of the current healthcare climate’s FISCAL constraints, we set out to
To….
Create a comprehensive pediatric trauma database for critically injured children utilizing minimal NEW resources
Goal : sustainable platform
Extend from pre-hospital care  discharge
Capturing non-mortality outcomes, processes of care measures

4. PTAM
database
Objectives
Create a comprehensive pediatric trauma database to assess quality of care in critically injured children utilizing minimal new resources.
Evaluate outcomes related to imaging practices and ICPM utilization in children with TBI admitted to the PICU.
TEST THIS PLATFORM 

5. PTAM Methods Merged 3 databases 5 Level I/II PTC
Trauma Registry (TR)
Virtual PICU Systems (VPS) data
PTAM-specific RedCap
5 Level I/II PTC
All children discharged from PICU CY 2013
ACS verified PTC status
Kosair Children’s Hospital, Louiseville, KY
Helen DeVos Children’s Hospital, Grand Rapids, MI
Akron Children’s Hospital, Akron, OH (level II)
Children’s Hospital of Los Angeles, Los Angeles, CA
Children’s Hospital of Wisconsin, Milwaukee, WI

6. Initial vitals Initial GCS
PTAM
database
Big Picture
Discharge status
Medical
ICU LOS
Pre-hospital data
PCPC
POPC
Initial vitals Initial GCS
Patient Outcomes
PIM2 PRISMIII PELOD
Injury patterns
The goal – of course -- is to improve patient outcomes By
Set platform by which we can Examine important questions spanning the treatment course of the injured child, from stabilization to resuscitation to discharge
PIM: Pediatric Index of mortality – predictors of mortality (initial evaluation in ICU)
PRISM: Pediatric risk of mortality – predictors of mortality (first 12 hours in ICU)
Pediatric Logistic Organ Dysfunction - measure of illness severity (created to use as an outcome)
(what do we do in stabilization phase that minimizes secondary insults)
-- FOCUS ON SAVING – not just more lives– but better lives, more functional lives ---
Pediatric CEREBRAL performance category and pediatric OVERALL performance category - global measures of morbidity (pediatric modification to GOSE)
Predicted LOS (medical LOS and physical LOS)
Procedures
Lab data
Bedside procedures
VPS TR

7. PTAM Patient population N = 457 Head Injury 66% male
database
Patient population
N = 457 Head Injury
66% male
Mean age 6.3y (5.8)
Race/Ethnicity
54% White
20% African American
9% Hispanic
Payer
47% Medicaid/Gov.
Mechanism of injury
36% Falls
25% MVC
Maximum Head AIS
33% AIS 4/5
Injury Severity Score
16% ISS>25
25% ISS 16-25
Increased data capture = breadth
Left– demographic data, our sample population is not unlike that of the general pediatric trauma population
CLICK
Right – distribution of the mechanism and severity of injury data
14% assaults TR

8. PTAM ED/ICU admission GCS on arrival Motor GCS Pupillary response
database
ED/ICU admission
GCS on arrival
21% 3-8
7% 9-12
55%
Motor GCS
11% paralyzed
4% no motor resp
Pupillary response
91% Both reactive
6% Fixed
Lowest GCS in first 12hrs
20% GCS 3-8
13% GCS 9-12
67% GCS 13-15
Increased data capture = breadth
LEFT - -- CNS TR from arrival
Augment this…
RIGHT - CNS data initial ICU stay -- VPS
Pupillary response based on prism
PIM also gives Pupil exam – 94.5% both reactive, 5% >3mm both fixed, 1% missing
Mean (SD) TR
VPS

9. PTAM Hospital disposition Baseline PCPC Discharge PCPC
database
Hospital disposition
Baseline PCPC
94% Normal
6% Mild/Mod
1% Moderate
0.2% Severe
Discharge PCPC
66% Normal
25% Mild/Mod
4% Severe
5% Brain Death
Hosp length of stay
Mean 6.8 (SD 11.0)
Median 3 (IQR 2-7)
Hosp disposition
82% home
11% rehab
7% transferred
5% expired
19 died in the ICU to 23 died prior to discharge (1 OR, 3 other) TR
VPS

10. Imaging and procedures
PTAM
database
Imaging and procedures
PTAM: 847 Head CT
TR alone: 21 ICPM
PTAM: 34 ICPM
212 outside hospital
635 index hospital
317 before/after ICU
VPS alone ≤318
162%
vs. TR alone
Increased data capture = DEAPTH
With pre-hospital CT scans –305% increase
Without pre-hospital % increase
7 children had 2 different ICPM!
** WITHOUT CHOP**
266%
vs. VPS alone

11. Imaging practices at index hosp
PTAM
database
Imaging practices at index hosp
Head CT
Mild/Mod TBI (n= 280)
Severe TBI (n = 98)
Range
0-7
0-9
Mean (SD)
1.3 (1.0)
2.2 (2.0)
No scans (%)
57 (20)
14 (14) 1
133 (48)
35 (36) 2
62 (22)
16 (16) 3
16 (6)
12 (12) 4
9 (3)
7 (7) 5+
3 (1)
Taking a closer look at imaging practices,
Range of CTH use stratified by TBI severity – inpatient at index hospital only
98 children had a total of 216 in-patient scans
* 79 with missing ed gcs
79 missing ED GCS score

12. Imaging practices at index hosp
PTAM
database
Imaging practices at index hosp
Head CT
Mild/Mod TBI (n= 280)
Severe TBI (n = 98)
Range
0-7
0-9
Mean (SD)
1.3 (1.0)
2.2 (2.0)
No scans (%)
57 (20)
14 (14) 1
133 (48)
35 (36) 2
62 (22)
16 (16) 3
16 (6)
12 (12) 4
9 (3)
7 (7) 5+
3 (1)
Range of CTH use stratified by TBI severity – inpatient at index hospital only
98 children had a total of 216 in-patient scans
* 79 with missing ed gcs
10%
33%
79 missing ED GCS score

13. Imaging practices by site
PTAM
database
Imaging practices by site
Head CT imaging practices by site, n(%)
Site
No scan
1 CT scan
2 CT scans
3+ CT scans A
12 (11)
41 (39)
34 (32)
19 (18) B
35 (40)
37 (42)
12 (14)
3 (3) C
14 (17)
36 (44)
16 (20)
15 (19) D
14 (21)
25 (37)
10 (15)
17 (25) E
30 (26)
58 (50)
14 (12)
13 (11)
Total
105 (23)
197 (43)
86 (19)
67 (15)
Institutional variability : imaging
CT x site
– we notice a 2 fold difference in the number of children who got 2+ scans
We notice an 8 fold difference in the number of children who got 3+ CT scans by institution
 High vs. Low Utilization?

14. Imaging practices by site
PTAM
database
Imaging practices by site
Head CT imaging practices by site, n(%)
Site
No scan
1 CT scan
2 CT scans
3+ CT scans A
12 (11)
41 (39)
34 (32)
19 (18) B
35 (40)
37 (42)
12 (14)
3 (3) C
14 (17)
36 (44)
16 (20)
15 (19) D
14 (21)
25 (37)
10 (15)
17 (25) E
30 (26)
58 (50)
14 (12)
13 (11)
Total
105 (23)
197 (43)
86 (19)
67 (15)
Institutional variability : imaging
CT x site
– we notice a 2 fold difference in the number of children who got 2+ scans
We notice an 8 fold difference in the number of children who got 3+ CT scans by institution
 High vs. Low Utilization?

15. Imaging practices by site
PTAM
database
Imaging practices by site
Head CT imaging practices by site, n(%)
Site
No scan
1 CT scan
2 CT scans
3+ CT scans A
12 (11)
41 (39)
34 (32)
19 (18) B
35 (40)
37 (42)
12 (14)
3 (3) C
14 (17)
36 (44)
16 (20)
15 (19) D
14 (21)
25 (37)
10 (15)
17 (25) E
30 (26)
58 (50)
14 (12)
13 (11)
Total
105 (23)
197 (43)
86 (19)
67 (15)
Institutional variability : imaging
CT x site
– we notice a 2 fold difference in the number of children who got 2+ scans
We notice an 8 fold difference in the number of children who got 3+ CT scans by institution
 High vs. Low Utilization?

16. Imaging practices by site
PTAM
database
Imaging practices by site
Multivariable logistic regression
P-value .003
Site
3+ CT scans A
19 (18) B
3 (3) C
15 (19) D
17 (25) E
13 (11)
Total
67 (15)
Judicious in covariates chosen – including both TR and VPS covariates…..
See there is still substantial variation by institution in the number of CTH obtained PER CHILD
Multivariable poisson regression with robust se attenuates RR -
MECHANISM = MECHANISM categories, not just blunt vs. penetrating
Covariates: age, mechanism of injury (MVC, fall, struck by, etc.), maximum head AIS, Injury Severity Score (ISS), type of head injury, lowest GCS in first 12 hrs, pupils

17. Repeat CTH imaging practices after transfer
PTAM
database
Repeat CTH imaging practices after transfer
Site
Repeat scan A
38 (76) B
22 (39) C
15 (52) D
18 (62) E
18 (42)
Total
111(54)
Institutional variability : imaging
I told you before there were 207 people with CT scans at hospital prior to transfer to the index hospital  111 or 54% of those people had another CTH upon arrival to the index institution
19% had 2 or more
 High vs. Low Utilization?

18. Repeat CTH imaging practices after transfer
PTAM
database
Repeat CTH imaging practices after transfer
Site
Repeat scan A
38 (76) B
22 (39) C
15 (52) D
18 (62) E
18 (42)
Total
111(54)
aOR: 9.8 (2.9, 33.0)
Institutional variability : imaging
I told you before there were 207 people with CT scans at hospital prior to transfer to the index hospital  111 or 54% of those people had another CTH upon arrival to the index institution
Worse head injury? Warranting repeat scans?
Adjusted OR – 10.1
So then the question follows: Is this unaccounted for confounders? Reflect regional care-variation? NSG preference? What additional mediators account for this difference? Perhaps is is lack of coordination between OSH and index institutions related to image transfer or software compatibility?
 High vs. Low Utilization?

19. ICP monitor utilization
PTAM
database
ICP monitor utilization

20. ICP monitor utilization
PTAM
database
ICP monitor utilization
ICPM placement by site
Site
ICPM placement in TBI
(n = 34)
ICPM placement in severe TBI
(n = 29)
ICPM placement <6hr in severe TBI
(n = 18) A
5.7%
22.2%
5.6% B
1.1% 0% C
16.1%
52.2%
34.8% D
7.5%
19.2%
15.4% E
7.8%
33.3%
20.8%
Differences in utilization at TBI, utilization in severe TBI, and speed of placement in severe TBI
Multivariable models, sites were not statistically significantly different ICPM utilization
High (C) vs. Low (A) utilization: OR 3.2 ( )

21. PTAM Functional outcomes
database
Functional outcomes
Pediatric Cerebral Performance Category (PCPC)
Alertness
ADLs
School performance
Modeled after GOSE
Preinjury-discharge  delta
Scored 1-6
1 is normal
6 is brain death

22. PTAM Functional outcomes database Scored 1-6 1 is normal
6 is brain death

23. Preinjury-discharge PCPC by ICPM
PTAM
database
Preinjury-discharge PCPC by ICPM No
ICPM
adj β coefficient
P-value
No ICPM
Ref
ICPM
-.84 (-1.2, -.51)
<.001
ICPM
0.0
-0.5
-1.0
-1.5
Covariates: age, mechanism of injury (MVC, fall, struck by, etc.), maximum head AIS, Injury Severity Score (ISS), type of head injury, lowest GCS in first 12 hrs, pupils
∆-0.9
ICPM to no ICPM across all sites
ICPM
β = mean difference in delta PCPC
Negative – comparison worse (ICPM)
Positive – comparison better (ICPM)

24. Preinjury-discharge PCPC by ICPM & site
PTAM
database
Preinjury-discharge PCPC by ICPM & site
Site
adj β coefficient
P-value A
-.26 (-.95, .42)
.442 B
-.10 (-.55, .356
.676 C
-1.2 (-2.1, -.31)
.009 D
-.59 (-1.8, .63)
.333 E
-1.6 (-2.3, -.79)
<.001 No
ICPM
ICPM
0.0
-0.5
-1.0
-1.5
∆-0.5
Covariates: age, mechanism of injury (MVC, fall, struck by, etc.), maximum head AIS, Injury Severity Score (ISS), type of head injury, lowest GCS in first 12 hrs, pupils
Stratified analysis
ICPM
by site
β = mean difference in delta PCPC
Negative – comparison worse (ICPM)
Positive – comparison better (ICPM)

25. Results: Delta PCPC by ICPMxsite
PTAM
database
Results: Delta PCPC by ICPMxsite
Site
P-value A
.021 B
.661 C
.647 D
<.001 E
Ref ∆
∆-1.0
ICPM
vs.
no ICPM
ICPM
vs.
no ICPM
Interaction
Individual wald test statistic across all sites for interaction is p < .05
Covariates: age, mechanism of injury (MVC, fall, struck by, etc.), maximum head AIS, Injury Severity Score (ISS), type of head injury, lowest GCS in first 12 hrs, pupils
ICPM x site
β = mean difference in differences
Negative – comparison worse
Positive – comparison better

26. What does that mean?
Change in functional status associated with ICPM was different depending on the site of care

27. PTAM Limitations Small sample size Limited power Restricted analyses
database
Limitations
Small sample size
Limited power
Restricted analyses
PCPC lacks precision
No quality of life/long term outcomes
Limited generalizability

28. PTAM
database
Take Home
Successful utilization of a novel database to explore processes of care in critically injured pediatric TBI patients
Comparing H:L sites
aOR CTH
aOR 9.8 repeat CTH s/p transfer
OR 3.2 ICPM use
Site variation in functional outcomes

29. PTAM
database
Conclusion
Combining databases is an innovative, feasible, cost-effective way to evaluate management practices and to explore critical questions related to pediatric trauma management.
Discuss the utility of merging pre-hospital, hospital, and ICU-specific data to answer novel and critical questions in pediatric trauma management.
Discuss how the Pediatric Trauma Assessment and Management database may provide a venue to investigate treatment variability and contribute to improving care in pediatric trauma.

30. PTAM Recall: Dr. Mikhailov  EEN Justi O’Flynn  NAT Quick add-on
database
Quick add-on
Recall:
Dr. Mikhailov  EEN
Justi O’Flynn  NAT
Discuss the utility of merging pre-hospital, hospital, and ICU-specific data to answer novel and critical questions in pediatric trauma management.
Discuss how the Pediatric Trauma Assessment and Management database may provide a venue to investigate treatment variability and contribute to improving care in pediatric trauma.

31. PTAM NOW is the time TR/TQIP VPS Midline shift Pupils on ED arrival
database
NOW is the time
TR/TQIP
Peds QL at 6 or 12 mo
Midline shift
Pupils on ED arrival
VPS
Neurocritical care module
TBI focus

32. PTAM
database
Thank you
Special thanks to all trauma registrars and VPS coordinators at participating sites
Discuss the utility of merging pre-hospital, hospital, and ICU-specific data to answer novel and critical questions in pediatric trauma management.
Discuss how the Pediatric Trauma Assessment and Management database may provide a venue to investigate treatment variability and contribute to improving care in pediatric trauma.

33. PTAM
database
Thank you
Questions?