PEDIATRIC TRAUMA ASSESSMENT AND MANAGEMENT DATABASEA TRAUMA REGISTRY-VPS PARTNERSHIP
VPS User Conference| March 24-26, 2015
Katherine T. Flynn-O’Brien, MDMary E. Fallat, MDTom B. Rice, MDChristine M. Gall, RN, MS, DrPHFrederick P. Rivara, MD
Outline
Motivation What we did How we did it What we discovered
I. More data (and better data)II. Risk-adjustment modeling III. Processes of care
Brainstorming
Motivation
Limited ability to study pediatric trauma NTDB / Pediatric TQIP Virtual Pediatric ICU Systems (VPS)
UDSMR, HCUP, PHIS, MarketScan
Objective
Create a comprehensive pediatric trauma database to assess quality of
care in critically injured children utilizing minimal new resources.
Methods
Merged 3 databases Trauma Registry
(TR) Virtual PICU Systems
(VPS) data PTAM-specific
RedCap 5 Level I/II PTC All children
discharged from PICU CY 2013
Process
PTAM
Trauma
Registry
(local export)
VPS(central export)
Additional
data eleme
nts(data entry)
95.5% match
Additional variables
C-spine clearance
Hgb prior to transfusion
FAST
Alcohol screening & counseling
(TQIP variables)
All CT scans
ICPM placement
Mech. VTE proph.
Lab upon arrival
Initiation of feeds
Bowel regimen
Breadth & depth
I. More data | Better data
Care continuum
Pre-hospital
ED PICU Floor Discharge
You are here.
Care continuum
• Vitals• GCS• Transfer
Pre-hospital
• Vitals• GCS• Labs*
ED arrival
• Disposition• LOS
Discharge
Care continuum
Variable Pre-hospital
ED PICU Floor
GCS X X X (X)
Pulse X X XX
Blood Pressure
X X XX
Hemoglobin XX XX
Base Deficit X X
AST X X
ALT X X
Hypoxemia X X
PT/PTT X X
CT scans X X X X
Care continuum
Variable Pre-hospital
ED PICU Floor
GCS X X X (X)
Pulse X X XX
Blood Pressure
X X XX
Hemoglobin XX XX
Base Deficit X X
AST X X
ALT X X
Hypoxemia X X
PT/PTT X X
CT scans X X X X
Care continuum
Variable Pre-hospital
ED PICU Floor
GCS X X X (X)
Pulse X X XX
Blood Pressure
X X XX
Hemoglobin XX XX
Base Deficit X X
AST X X
ALT X X
Hypoxemia X X
PT/PTT X X
CT scans X X X X
Care Continuum
What is the child’s cognitive/physiologic status immediately after injury?
What resuscitation is, or is not, occurring prior to ICU arrival?
How may this information change management in the ICU?
More data
Better data
Complications Cardiac arrest CLABSI Unplanned
return to the ICU
Pneumonia Re-intubation
Better data
Complications Cardiac arrest CLABSI Unplanned
return to the ICU
Pneumonia Re-intubation
4 37
Better data
Complications Cardiac arrest CLABSI Unplanned
return to the ICU
Pneumonia Re-intubation
1 2
Better data
Complications Cardiac arrest CLABSI Unplanned
return to the ICU
Pneumonia Re-intubation
3 14
Better data
Complications Cardiac arrest CLABSI Unplanned
return to the ICU
Pneumonia Re-intubation
5 5
Better data
Complications Cardiac arrest CLABSI Unplanned
return to the ICU
Pneumonia Re-intubation
0 20
Better data
Comorbidities Hx of CVA Prematurity Respiratory
distress
Better data
Comorbidities Hx of CVA Prematurity Respiratory
distress1 1
Better data
Comorbidities Hx of CVA Prematurity Respiratory
distress6 18
Better data
Comorbidities Hx of CVA Prematurity Respiratory
distress4 14
II. Risk adjustment modeling
Mortality
Model building Model diagnostics Multiple
imputation
PIM2 PRISM3 PELOD
Trauma Registry VPS
Mortality
Model AUC R2 value AIC
TR-only 0.9360 0.5286 127.57VPS-only 0.9917 0.6723 95.84
TR-VPS 0.9776 0.6843 91.69TR-only covariates: age, mechanism of injury, transfer status, ED systolic blood pressure, ED pulse, ED GCS motor score, max head AIS, max extremity AIS, congenital comorbiditiesVPS-only: PIM2TR-VPS: TR model + VPS-PIM2 model
Mortality
P = .0165
0.0
00
.25
0.5
00
.75
1.0
0S
ens
itivi
ty
0.00 0.25 0.50 0.75 1.001-Specificity
TRTR+VPS
VPS
ROC by Data Source
Mortality
0.0
00
.25
0.5
00
.75
1.0
0S
ensi
tivity
0.00 0.25 0.50 0.75 1.001-Specificity
TRTR+VPS
VPS
ROC by Data Source
P = .0165
Mortality
VPS Can we appropriately risk adjust
without controlling for mechanism of injury? Injury severity?
Trauma Registry Can we do better? Can we improve
model fit? Improve accuracy? Efficiency?
PCPCPOPCPELODLength of hospital stayDischarge to home (vs. rehab)
Non-mortality outcomes
Hospital disposition
What factors are most strongly associated with (poor) functional
status?
What are predictors of discharge home?
What are predictors of discharge to a rehab facility?
III. Processes of Care
VTE prophylaxis0
200
400
600
mechvte_hrs dvt_hrs
Site A
Site B
Site C
Site D
Site E
More…
Nutrition management Parenteral Enteral
Daily bowel regimen C-spine clearance Alcohol and drug screening Alcohol counseling
Limitations
Non-mortality outcomes lack precision
No quality of life measures Limited generalizability
Scope
75+VPS institutions w/ trauma ~40% ACS trauma centers ~60% state trauma centers
50+ centers can immediately merge data
Hurdles
Pediatric Trauma Assessment and
Management 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.
Thank you
Special thanks to all trauma registrars and VPS coordinators at participating
sites
Challenges…
…are worth it
Thank you
Questions?
TRVPS
Patient Outcom
es
Discharge status Pre-
hospital data
Initial vitals GCS
Injury patterns
ProceduresBedside
procedures
Lab data
PIM2 PRISMIII PELOD
PCPC POPC
Predicted
LOS
Patient population
67 % male Mean age 7.2y
(6.0) Race/Ethnicity
51% White 21% African
American 7% Hispanic
Payer 35% Private 48% Medicaid/Gov.
Injury characteristics
Mechanism of injury 32% Falls 25% MVC 4% Penetrating
Intent 84% unintentional 14% assaults
Place 31% residential
Maximum Head AIS 15% AIS 4/5 43% AIS 3
Other max AIS 67% abd AIS 3-5 57% thoracic AIS 3-
5 Injury Severity
Score 13% ISS>25 22% ISS 16-25
TRTR
Pre-hospital & ED
Physiologic data 11%
tachycardia* 3% hypotension* 9% GCS <9
EMS transport 42% ambulance 14% air
Physiologic data 29%
tachycardia* 5%
hypotension* 17% GCS <9
ED disposition 14% OR
Transfer statusTRTR *Age-based
ICU first hr & first 12 hrs
SBP 10%
hypotension* Base excess
-5.2 (4.2) Pupil reaction PF ratio
VPS
Physiologic/lab data BP, HR, RR, temp,
pH PaO2, PaCO2 Hgb, WBC Plt, PT, PTT, bili K, Na, Ca, albumin,
BUN, Cr Ventilation data Infection data VPS
ICU course & outcomes
Baseline POPC 89% Normal 10% Mild/Mod 1% Severe
Discharge POPC 34% Normal 57% Mild/Mod 4% Severe/Coma 5% Brain Death
VPS
Intensivist (98%) 83% Concurrent
care 5% Consulting only 10% Primary service
PELOD baseline, daily, POD
PRISM3 PIM2
VPS
Hospital disposition
ICU Length of stay Mean 2.8 (SD
5.0) Median 1.1 (.6-
2.6) ICU disposition
69% floor, SDU 0.7% rehab 1.3% transferred
Hosp length of stay Mean 7.3 (SD
10.9) Median 4 (IQR 2-8)
Hosp disposition 82% home 11% rehab 2% transferred
VPSTR