Applying meta-analysis to trauma registry

1. Applying meta-analysis to trauma registry Ammarin Thakkinstian, Ph.D. Clinical Epidemiology Unit Faculty of Medicine, Ramathibodi Hospital Tel: 2011269,2011762 Fax: 02-2011284 e-mail: raatk@mahidol.ac.th

2. Meta-analysis • A tool for pooling results/data of the same topics from different sources/centres in order to • estimates treatment/intervention effects • leading to reduces probability of false negative results • potentially to a more timely introduction of effective treatments/intervention/program • Objective evidence & quantitative conclusion

3. Type of meta-analysis • Summary data • Unit of analysis is study • Mean (SD) • Count/frequency data by intervention & outcome • Person-time data

4. Summary-data • Continuous data Studyi N Mean SD Rx/Exp+ N1 Mean1 SD1 Cont/Exp- N2 Mean2 SD2

5. Summary-data • Categorical data

6. Type of meta-data • Individual patient data (IPD) • Raw databases • Unit of analysis is patient • Analogous to multi-centre trials • More retrospective than prospective • Data registry

7. IPD • Carry out data checking (data validation) • Better standardization of information • Categorization of eligible participants • Definition of Outcomes • Variables’ Classification • ICD-10 • Type of trauma • AIS

8. IPD • Flexible to apply statistic modeling • Better adjust for confounders & adjust for the same confounders simultaneously • More flexible to assess interaction effects • More flexible and capable in assessing cause of heterogeneity • Allow to assess which subgroup of patients (centre) that intervention/program may/may not work • Establishment of international networks of collaborating investigators

9. IPD • Disadvantage • Data quality • Missing data • Data validation • More cost & time consuming • Substantial effort and infrastructure require to • Develop & administer a standardized protocol • Collect, manage, & data management • Communicate with collaborators

10. Hospitals Data collection & management • Data Registry Databases Data coding Data manager QC Data entry Cleaning Checking Validate data Validated Data

11. Retrieve databases Combine data Statistician Re-check data Analyse data Report results Writing report (manuscript) Publish (annual, twice/year)

12. Data analysis • Heterogeneity test • Different source data are homogeneous? • Homogeneity

13. Analysis • Heterogeneity

14. Outcomes • Death/alive • Disability/Non-disability • Complications • Infection • Fracture • Hospitalization • Hospital days • QoL • Cost

15. Count (discrete) outcome • Poisson regression • Number of death • Number of infection • Number of disability • Number of fracture

17. Hospital standardised mortality ratio

18. HSMR • Definition • The ratio of actual number of deaths to expected number of deaths in the hospital

19. Expected number of deaths

20. Original HSMR • X • Age in year • Sex • Admission category • Emergency versus elective • Length of stay • Diagnosis group • Account for 80% of death • Co-morbidity • Chalson’s index • Might be able to use AIS scores • Transfer • Patient was transferred from acute care

21. Step of analysis • Fit logistic regression with death as the outcome • Estimate probability of death from the logit model • E = sum(p)

22. Modified HSMR • age in year • sex • Length of stay • Admission category • Emergency vs elective • Transfers • Acute care • Diagnosis group • Account for 80% of death • Co-morbidity • Chalson’s index • age in year • sex • Length of stay • Patient transferring • Ambulance • Non-ambulance AIS scores Add • Risk behavior • Alcohol • Transquilizer/sedation • Type of trauma

24. Problem • Missing • Diagnosis • Co-morbid • Length o stay • Data validation??