1. Probability of Survival (Ps) & Hospital Survival Rate (Ws) The Trauma Audit & Research Network (TARN)

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 in Ps since 2004:
ISS
Age
Gender
GCS

5. PS 12 model
Age
Gender
ISS
GCS/ intubation i
PS 12 calculation $
63.0%

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

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

9. PS 14 –PMC and true 30 day outcome
Launched December 2014
Age
Gender
ISS
GCS/ intubation
PMC i
PS 14 calculation $
Patient PS
ONS outcome linkage *
*Charlson index (1984, revised).

10. Pre-Existing Medical Conditions
PS14 Weighting groups
Pre-Existing Medical Conditions
Weight
PMC group
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

11. Weightings for Age, GCS, Gender & PMC
PS 14
Weightings for Age, GCS, Gender & PMC
Age: 25
Gender: Male
ISS: 25
GCS: 15
PMC:
Nil i
PS 14 calculation $
Ps: 98.7%

12. Weightings for Age, GCS, Gender & PMC
PS 14
Weightings for Age, GCS, Gender & PMC
Age: 25
Gender: Male
ISS: 25
GCS: 15
PMC:
Alcohol abuse i
PS 14 calculation $
Ps: 97.8%

13. Weightings for Age, GCS, Gender & PMC
PS 14
Weightings for Age, GCS, Gender & PMC
Age: 25
Gender: Male
ISS: 25
GCS: 15
PMC:
Alcohol Abuse
Liver Disease i
PS 14 calculation $
Ps: 93.7%

14. Weightings for Age, GCS, Gender & PMC
PS 14
Weightings for Age, GCS, Gender & PMC
Age: 25
Gender: Male
ISS: 25
GCS: 3
PMC:
Alcohol Abuse
Liver Disease i
PS 14 calculation $
Ps: 25.3%

15. Weightings for Age, GCS, Gender & PMC
PS 14
Weightings for Age, GCS, Gender & PMC
Age: 65
Gender: Male
ISS: 25
GCS: 3
PMC:
Alcohol Abuse
Liver Disease i
PS 14 calculation $
Ps: 6.5%

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

17. Importance of accurate injury detail
PS 14
Importance of accurate injury detail
Full injury detail
Code
Subdural haematoma bilateral & 2cm thick on both sides
Base of skull fracture
5 ribs fractured on left
Full thickness Rectal laceration
Spiral Fracture of left Shaft of Femur
Vertical Shear fracture to pelvis with blood loss >20%
Open Comminuted fracture to Tibial Shaft
Accurate ISS
Accurate Ps 59
49%
Incomplete injury detail
Code
Subdural haematoma
Base of skull fracture
Ribs fractured on left
Rectal laceration
Fracture of left Shaft of Femur
Pelvic Fracture
Tibial Shaft Fracture
Incomplete ISS 29

18. Importance of accurate injury detail
Ps 14
Importance of accurate injury detail
Age: 40
Gender: Male
ISS: 29
GCS: 5
PMC:
Nil i
PS 14 calculation $
Ps: 79%

19. PS breakdown: 4 years Calendar data: Shown on Website
Ps Bandings: Expanded for PS14
No. of patients in each band
No. of expected survivors for each band
Actual number of survivors
Difference = Actual – Expected x 100
No. in group
Adjusted Difference = Difference x fraction of pts on TARN database

20. Hospital Survival Rate: 4 years Calendar data
+1.2 Survivors
Statistically significant outcome
(+0.39 to +1.97)
+1.2 Survivors
Not Statistically significant
(-1.83 to +4.2)
Total Ws shown
Yearly Ws shown

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

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

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

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