1. Measuring trauma outcomes & processes of trauma care The Trauma Audit & Research Network (TARN) Data Collection session

2. Probability of Survival Once ISS is assigned Probability of Survival (Ps) calculated each submission

3. Why calculate PS?  Need to assign ‘Weight’ to deaths and survivors.  Some deaths more statistically significant than others.  Case mix adjustment.  Performances measurement: hospital and networks.

4. History of PS  TARN developed first PS model in 2004  Remodelled in 2007, 2009, 2012  4 components used: ISS Age Gender GCS

5. PS 12 model example AgeGenderISS GCS/ intubation  PS 12 calculation  PS 63%

6. How is Ps derived? PS% is retrospective measure of pts with same profile on TARN database. TARN database: past 4 years (Approx 200,000 cases) Ps = 63%, then 63 out of every 100 patients with that profile have previously survived. 37 out of every 100 patients have previously died.

7. Ps is calculated using: GCS taken on arrival in ED where unavailable Pre Hospital GCS where unavailable Presence of Intubation/ventilation where unavailable Impute a “probable” GCS (equivalent weighting) Probability of Survival

8. PS14 developments Launched December 2014 1.Pre-Existing Medical Conditions (PMC) added Charlson comorbidity index (CCI) adds ‘weighting’ PMC 2.True 30 day outcome model introduced ONS (Office of National Statistics) data linkage using NHS No (more later).

9. PS 14 –PMC and true 30 day outcome Launched December 2014 AgeGenderISS GCS/ intubation PMC  PS 14 calculation  Patient PS ONS outcome linkage * *Charlson index (1984, revised).

10. PS14 Weightings Pre-Existing Medical Conditions 0 Bone conditions Connective tissue disorder Diabetes GU disease HIV Mental health Neurological disorders Nil Paraplegia Pulmonary disease 1-5 Alcohol Blood conditions Cancer CVA Congestive heart failure Dementia MI Other conditions Peripheral vascular disease 6-10 Metastatic cancer/Haematological malignancy Renal disease >10 Liver disease WeightPMC group

11. PS 14 Weightings for Age, GCS, Gender & PMC Age: 25Gender: MaleISS: 25GCS: 15 PMC: Nil  PS 14 calculation  Ps: 98.7%

12. PS 14 Weightings for Age, GCS, Gender & PMC Age: 25Gender: MaleISS: 25GCS: 15 PMC: Alcohol abuse  PS 14 calculation  Ps: 97.8%

13. PS 14 Weightings for Age, GCS, Gender & PMC Age: 25Gender: MaleISS: 25GCS: 15 PMC: Alcohol Abuse Liver Disease  PS 14 calculation  Ps: 93.7%

14. PS 14 Weightings for Age, GCS, Gender & PMC Age: 25Gender: MaleISS: 25GCS: 3 PMC: Alcohol Abuse Liver Disease  PS 14 calculation  Ps: 25.3%

15. PS 14 Weightings for Age, GCS, Gender & PMC Age: 65Gender: MaleISS: 25GCS: 3 PMC: Alcohol Abuse Liver Disease  PS 14 calculation  Ps: 6.5%

16. PS 14 Weightings for Age, GCS, Gender & PMC Age: 65 Gender: Female ISS: 25GCS: 3 PMC: Alcohol Abuse Liver Disease  PS 14 calculation  Ps: 7.06%

17. PS 14 Importance of accurate injury detail Full injury detailCode Subdural haematoma bilateral & 2cm thick on both sides140655.5 Base of skull fracture150200.3 Thoracic spine (T8) Major compression fracture650434.3 Full thickness Rectal laceration543624.3 Spiral Fracture of left Shaft of Femur853251.3 Vertical Shear fracture to pelvis with blood loss >20%856173.5 Open Comminuted fracture to Tibial Shaft854272.3 Incomplete injury detailCode Subdural haematoma140650.4 Base of skull fracture150200.3 Thoracic spine (T8) compression fracture650432.2 Rectal laceration543620.2 Fracture of left Shaft of Femur853221.3 Pelvic Fracture856151.2 Tibial Shaft Fracture854221.2 Accurate ISSAccurate Ps 5949% Incomplete ISS 29

18. Ps 14 Importance of accurate injury detail Age: 40Gender: MaleISS: 29GCS: 5 PMC: Nil  PS 14 calculation  Ps: 79%

19. PS breakdown: Individual Hospital Survival Rate 4 years data

20. Individual Hospital Survival Rate +1 Survival Rate Statistically significant outcome +1 Survival Rate Not Statistically significant Total Ws shown Yearly Ws shown

21. Comparative Outcome Analysis (Ws graphs) Next Step: Compare Outcomes between all submitting Hospitals Four Comparative Outcome graphs included in Clinical Reports: 2 x Caterpillar plots (showing outcomes by Survival Rate) 1.Outcome at 30 days or discharge (whichever is sooner) 2.True 30 day outcome (linked to ONS data)

22. 95% confidence intervals All hospitals Your hospital CATERPILLAR PLOT: Ascending Survival rate

23. Comparative Outcome Analysis (Ws graphs) Next Step: Compare Outcomes between all submitting Hospitals Four Comparative Outcome graphs included in Clinical Reports: 2 x Caterpillar plots (showing outcomes by Survival Rate) 1.Outcome at 30 days or discharge (whichever is sooner) 2.True 30 day outcome (linked to ONS data) 2 x Funnel plots (showing outcomes by Precision –no. of cases) 1.Outcome at 30 days or discharge (whichever is sooner) 2.True 30 day outcome (linked to ONS data)

24. Greater Precision: More cases (more reliable) Lower Precision: Fewer cases (not as reliable) FUNNEL PLOT: Precision (no. of cases) Your hospital All hospitals

25.  Outcome at 30 days post injury historically used in Ws.  Many patients discharged before 30 days.  Need to know whether patients died before or after day 30.  We now receive information about deaths from the ONS.  ONS data linkage is carried out using the patients’ NHS number. Why have we added True outcome at 30 days?

26. Summary Inclusion Criteria Identifying cases Data Completeness (quantity)& Accreditation (quality) Data Entry Injury scoring & calculating the Injury Severity Score Ps14 (Probability of Survival) calculation Hospital Survival rate (Ws) Key Points document in pack

27. Questions