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

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Presentation on theme: "Probability of Survival (Ps) & Hospital Survival Rate (Ws) The Trauma Audit & Research Network (TARN)"— Presentation transcript:

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 AgeGenderISS GCS/ intubation  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 Ps is calculated using: GCS taken on arrival in ED at first receiving hospital 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.

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 Weighting groups 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 5 ribs fractured on left450203.3 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 Ribs fractured on left450210.2 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: 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 Example patients: Ps:45-65% PsAgeSexISSGCSIntOutcomePMCMechanismInjuriesSubmission ID 58.8%46Male163YesDeathN/KFall >2mSkull Vault # SDH NFS (AIS4) Brain swelling 999900004552 PsAgeSexISSGCSIntOutcomePMCMechanismInjuriesSubmission ID 46.8%37Female575YesAliveDepressionFall >2mDAI (AIS5) Major Kidney Lac (AIS4) Hemomediastinum (AIS4) 999900004279 PsAgeSexISSGCSIntOutcomePMCMechanismInjuriesSubmission ID 53.9%94Female3814NoDeathN/KFall <2mT cord transection (AIS5) BOS # (AIS3) Pelvic # (AIS2) 999900004517 PsAgeSexISSGCSIntOutcomePMCMechanismInjuriesSubmission ID 63.9%78Male4515NoAliveDepression Diabetes HTN Fall <2 MFlail Chest (AIS4) C cord transection (AIS5) Humeral # (AIS2) 99990004581

21 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

22 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) 1.Outcome at 30 days or discharge (whichever is sooner) 2.True 30 day outcome (linked to ONS data)

23 95% confidence intervals All hospitals Your hospital CATERPILLAR PLOT: Ascending Survival rate

24 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) 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)

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

26 Summary Inclusion criteria Identifying cases Data Entry Quality Assurance: Data Accreditation and Completeness Injury scoring & calculating the ISS (Injury Severity Score) Ps14 (Probability of Survival) & Hospital Survival rate (Ws) Slides on TARN website: www.tarn.ac.uk

27 Questions

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