Measuring trauma outcomes &

processes of trauma care

The Trauma Audit & Research Network (TARN)

Data Collection session

Probability of SurvivalOnce ISS is assigned

Probability of Survival (Ps) calculated each submission

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.

History of PS

TARN developed first PS model in 2004

Remodelled in 2007, 2009, 2012

4 components used: ISS Age Gender GCS

PS 12 model example

Age Gender ISS GCS/intubation

i i i iPS 12 calculation

$PS 63%

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.

Ps is calculated using:

GCS taken on arrival in ED where unavailable

Pre Hospital GCS where unavailable

Presence of Intubation/ventilationwhere unavailable

Impute a “probable” GCS (equivalent weighting)

Probability of Survival

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).

PS 14 –PMC and true 30 day outcomeLaunched December 2014

Age Gender ISS GCS/intubation PMC

i i i i iPS 14 calculation

$Patient PS

ONS outcome linkage

*

*Charlson index (1984, revised).

PS14 WeightingsPre-Existing Medical Conditions

0 Bone conditionsConnective tissue disorderDiabetesGU diseaseHIV

Mental healthNeurological disordersNilParaplegiaPulmonary disease

1-5 AlcoholBlood conditionsCancerCVACongestive heart failure

DementiaMIOther conditionsPeripheral vascular disease

6-10 Metastatic cancer/Haematological malignancy

Renal disease

>10 Liver disease

Weight PMC group

PS 14 Weightings for Age, GCS, Gender & PMC

Age: 25 Gender: Male ISS: 25 GCS: 15 PMC: Nil

i i i i iPS 14 calculation

$Ps: 98.7%

PS 14 Weightings for Age, GCS, Gender & PMC

Age: 25 Gender: Male ISS: 25 GCS: 15 PMC: Alcohol abuse

i i i i iPS 14 calculation

$Ps: 97.8%

PS 14 Weightings for Age, GCS, Gender & PMC

Age: 25 Gender: Male ISS: 25 GCS: 15PMC:

Alcohol AbuseLiver Disease

i i i i iPS 14 calculation

$Ps: 93.7%

PS 14 Weightings for Age, GCS, Gender & PMC

Age: 25 Gender: Male ISS: 25 GCS: 3PMC:

Alcohol AbuseLiver Disease

i i i i iPS 14 calculation

$Ps: 25.3%

PS 14 Weightings for Age, GCS, Gender & PMC

Age: 65 Gender: Male ISS: 25 GCS: 3PMC:

Alcohol AbuseLiver Disease

i i i i iPS 14 calculation

$Ps: 6.5%

PS 14 Weightings for Age, GCS, Gender & PMC

Age: 65 Gender: Female ISS: 25 GCS: 3

PMC: Alcohol AbuseLiver Disease

i i i i iPS 14 calculation

$Ps: 7.06%

PS 14 Importance of accurate injury detail

Full injury detail Code

Subdural haematoma bilateral & 2cm thick on both sides 140655.5

Base of skull fracture 150200.3

Thoracic spine (T8) Major compression fracture 650434.3

Full thickness Rectal laceration 543624.3

Spiral Fracture of left Shaft of Femur 853251.3

Vertical Shear fracture to pelvis with blood loss >20% 856173.5

Open Comminuted fracture to Tibial Shaft 854272.3

Incomplete injury detail Code

Subdural haematoma 140650.4

Base of skull fracture 150200.3

Thoracic spine (T8) compression fracture 650432.2

Rectal laceration 543620.2

Fracture of left Shaft of Femur 853221.3

Pelvic Fracture 856151.2

Tibial Shaft Fracture 854221.2

Accurate ISS Accurate Ps

59 49%

Incomplete ISS

29

Ps 14 Importance of accurate injury detail

Age: 40 Gender: Male ISS: 29 GCS: 5 PMC: Nil

i i i i iPS 14 calculation

$Ps: 79%

PS breakdown: Individual Hospital Survival Rate4 years data

•Ps Bandings: Expanded for Ps14•No. of patients in each band •No. of expected survivors for each band •Actual number of survivors

•Adjusted Difference = Difference x fraction of pts on TARN database

Individual Hospital Survival Rate+1 Survival Rate

Statistically significant outcome

+1 Survival RateNot Statistically significant

• Total Ws shown• Yearly Ws shown

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)

95% confidence intervals

All hospitals

Your hospital

CATERPILLAR PLOT: Ascending Survival rate

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)

3. Outcome at 30 days or discharge (whichever is sooner)

4. True 30 day outcome (linked to ONS data)

Greater Precision: More cases (more reliable)

Lower Precision: Fewer cases (not as reliable)

FUNNEL PLOT: Precision (no. of cases)

Your hospital

All hospitals

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?

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

Questions