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PEDIATRIC TRAUMA ASSESSMENT AND MANAGEMENT DATABASE A TRAUMA REGISTRY-VPS PARTNERSHIP VPS User Conference| March 24-26, 2015 Katherine T. Flynn-O’Brien,

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Presentation on theme: "PEDIATRIC TRAUMA ASSESSMENT AND MANAGEMENT DATABASE A TRAUMA REGISTRY-VPS PARTNERSHIP VPS User Conference| March 24-26, 2015 Katherine T. Flynn-O’Brien,"— Presentation transcript:

1 PEDIATRIC TRAUMA ASSESSMENT AND MANAGEMENT DATABASE A TRAUMA REGISTRY-VPS PARTNERSHIP VPS User Conference| March 24-26, 2015 Katherine T. Flynn-O’Brien, MD Mary E. Fallat, MD Tom B. Rice, MD Christine M. Gall, RN, MS, DrPH Frederick P. Rivara, MD

2 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

3 Motivation  Limited ability to study pediatric trauma  NTDB / Pediatric TQIP  Virtual Pediatric ICU Systems (VPS)  UDSMR, HCUP, PHIS, MarketScan

4 Objective Create a comprehensive pediatric trauma database to assess quality of care in critically injured children utilizing minimal new resources.

5 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

6 Process PTAM Trauma Registry (local export) VPS (central export) Additional data elements (data entry) 95.5% match

7 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

8 Breadth & depth I. More data | Better data

9 Care continuum Pre-hospitalEDPICUFloorDischarge You are here.

10 Care continuum Vitals GCS Transfer Pre- hospital Vitals GCS Labs* ED arrival Vitals Labs Vent data ICU stay Nutrition Constipation VTE ppx Floor Disposition LOS Discharge

11 Care continuum VariablePre-hospitalEDPICUFloor GCSXXX(X) PulseXXXX Blood PressureXXXX HemoglobinXX Base DeficitXX ASTXX ALTXX HypoxemiaXX PT/PTTXX CT scansXXXX

12 Care continuum VariablePre-hospitalEDPICUFloor GCSXXX(X) PulseXXXX Blood PressureXXXX HemoglobinXX Base DeficitXX ASTXX ALTXX HypoxemiaXX PT/PTTXX CT scansXXXX

13 Care continuum VariablePre-hospitalEDPICUFloor GCSXXX(X) PulseXXXX Blood PressureXXXX HemoglobinXX Base DeficitXX ASTXX ALTXX HypoxemiaXX PT/PTTXX CT scansXXXX

14 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?

15 More data

16 Better data  Complications  Cardiac arrest  CLABSI  Unplanned return to the ICU  Pneumonia  Re-intubation

17 Better data  Complications  Cardiac arrest  CLABSI  Unplanned return to the ICU  Pneumonia  Re-intubation

18 Better data  Complications  Cardiac arrest  CLABSI  Unplanned return to the ICU  Pneumonia  Re-intubation

19 Better data  Complications  Cardiac arrest  CLABSI  Unplanned return to the ICU  Pneumonia  Re-intubation

20 Better data  Complications  Cardiac arrest  CLABSI  Unplanned return to the ICU  Pneumonia  Re-intubation

21 Better data  Complications  Cardiac arrest  CLABSI  Unplanned return to the ICU  Pneumonia  Re-intubation

22 Better data  Comorbidities  Hx of CVA  Prematurity  Respiratory distress

23 Better data  Comorbidities  Hx of CVA  Prematurity  Respiratory distress

24 Better data  Comorbidities  Hx of CVA  Prematurity  Respiratory distress

25 Better data  Comorbidities  Hx of CVA  Prematurity  Respiratory distress

26 II. Risk adjustment modeling

27 Mortality  Model building  Model diagnostics  Multiple imputation  PIM2  PRISM3  PELOD Trauma RegistryVPS

28 Mortality ModelAUCR 2 valueAIC TR-only VPS-only TR-VPS TR-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 comorbidities VPS-only: PIM2 TR-VPS: TR model + VPS-PIM2 model

29 Mortality P =.0165

30 Mortality P =.0165

31 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?

32 PCPC POPC PELOD Length of hospital stay Discharge to home (vs. rehab) Non-mortality outcomes

33 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?

34 III. Processes of Care

35 VTE prophylaxis Site A Site BSite CSite DSite E

36 More…  Nutrition management  Parenteral  Enteral  Daily bowel regimen  C-spine clearance  Alcohol and drug screening  Alcohol counseling

37 Limitations  Non-mortality outcomes lack precision  No quality of life measures  Limited generalizability

38 Scope  75+VPS institutions w/ trauma  ~40% ACS trauma centers  ~60% state trauma centers  50+ centers can immediately merge data

39 Hurdles Pediatric Trauma Assessment and Management Database

40 Conclusion Combining databases is an innovative, feasible, cost-effective way to evaluate management practices and to explore critical questions related to pediatric trauma management.

41 Thank you Special thanks to all trauma registrars and VPS coordinators at participating sites

42 Challenges…

43 …are worth it

44 Thank you

45 Questions? TRVPS Patient Outcomes Discharge status Pre-hospital data Initial vitals GCS Injury patterns Procedures Bedside procedures Lab data PIM2 PRISMIII PELOD PCPC POPC Predicted LOS

46 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.

47 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 TR

48 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 status TR *Age-based

49 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  P a O2, P a CO2  Hgb, WBC  Plt, PT, PTT, bili  K, Na, Ca, albumin, BUN, Cr  Ventilation data  Infection data VPS

50 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

51 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


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