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

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 http://www.headinjuryctr-stl.org/statistics.html Source: Faul M, Xu L, Wald MM, Coronado VG. Traumatic Brain Injury in the United States: Emergency Department Visits, Hospitalizations and Deaths 2002 – 2006. 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) ~ 16000 deaths : 15703 deaths, Crude rate 11.75/100,000 ~ 280,000 injuries req. hospitalization : 279,907 injuries requiring hospitalization (or transfer), Crude rate of 379.75/ 100,000 ~ 7.2M ED visits : 7,252,434 treated and released, Crude rate 9855.74 /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

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

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 

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

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

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

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% 13-15 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

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

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 -- 228% increase 7 children had 2 different ICPM! ** WITHOUT CHOP** 266% vs. VPS alone

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

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

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?

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?

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?

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

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?

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?

ICP monitor utilization PTAM database ICP monitor utilization http://news.wustl.edu/news/Pages/24689.aspx

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 (1.2-8.8)

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

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

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)

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)

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 ∆-2.0 - ∆-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

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

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

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 aOR8.59 3+ CTH aOR 9.8 repeat CTH s/p transfer OR 3.2 ICPM use Site variation in functional outcomes

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.

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.

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

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.

PTAM database Thank you Questions? flynnobr@uw.edu