 Member  Non-member  Regional hospital  County hospital  Local hospital

1. The Swedish Intensive Care Registry: Source for research Member Non-member  Regional hospital  County hospital  Local hospital Sten Walther, MD Chairman, Swedish Intensive Care Registry Heart Centre, Linköping University Hospital http://www.icuregswe.org

2. The Swedish Intensive Care Registry: Source for research Outline: Basics Data sources Coverage and accuracy Case studies Data completeness and SAPS3 Active cooling after cardiac arrest Life after ICU-care Member Non-member  Regional hospital  County hospital  Local hospital

3. Data sources

4. Data sources • Data coupling possible using • Unique admission identifier • Unique person identifier

5. Data sources • Data coupling possible using • Unique admission identifier • Unique person identifier • National Quality Registry legislation • Person identifier permitted if purpose • is audit and benchmarking • Written information to the patient • must be provided • Consent presumed • Active withdrawal of consent possible

6. Which data? Consult Follow up Critical careoutreach ICU-careaftercare Treat Minimal dataset Withdrawal / Withholding ICU Admit Discharge Reason for admission Adverse events SOFA CardioThor ICU Pediatric ICU Diagnosis ICU outcome SAPS 3 Nursingworkload Key diagnosis APACHE II Procedures ICU-Higgins Renal RT PIM 2 Ventilator therapy

7. Data transfer: interaction over time My ICU No error Errors Swedish Intensive Care Registry Swedish Population Registry

8. Data transfer: interaction over time My ICU Old admissions Corrected errors New admissions Swedish Intensive Care Registry Swedish Population Registry

9. Data transfer: interaction over time My ICU Preferably weekly At least monthly Swedish Intensive Care Registry Swedish Population Registry

10. Data transfer: interaction over time My ICU Preferably weekly At least monthly Swedish Intensive Care Registry Vital status update Weekly Swedish Population Registry

11. Criteria for assessing coverage and accuracy Registry metrics (DocDATstuk)      

12. Criteria …. (cont’d)      Black et al, QualSaf Health Care 2003 12: 348-352

13. Case study I:Risk adjustment – SAPS3 Background • Transition to SAPS3 model from APACHE model

14. Case study I:Risk adjustment – SAPS3 Background • Transition to SAPS3 model from APACHE model • 2 vs. 24 hrs time window to capture physiologic data Admission to ICU Time (hrs)

15. Box III Physiologic variables www.saps3.org

16. Case study I:Risk adjustment – SAPS3 Background • Transition to SAPS3 model from APACHE model • 2 vs. 24 hrs time window to capture physiologic data • Will this leave us with more missing data and worse model performance? Admission to ICU Time (hrs)

17. Case study I: Risk adjustment – SAPS3 SIR data from 2009-2010

18. Case study I: Risk adjustment – SAPS3 SIR data from 2009-2010

19. Case study I: Risk adjustment – SAPS3 Calibration 1 physiologic variable missing No physiologic variable missing 5 physiologic variables missing 3 physiologic variables missing

20. Case study I:Risk adjustment – SAPS3 Conclusion • Good discrimination • Poor calibration • Limited influence of missing physiologic data • Customization necessary

21. Case study II:Active cooling after cardiac arrest Background • 2002: First randomized controlled trials (RCT) supporting use of hypothermia after cardiac arrest are published • 2003: International liaisoncommittee on resuscitation (ILCOR) recommendshypothermia after cardiac arrest • Rapid dissemination intoclinicalpractice

22. Case study II:Active cooling after cardiac arrest Background • 2002: First randomized controlled trials (RCT) supporting use of hypothermia after cardiac arrest are published • 2003: International liaisoncommittee on resuscitation (ILCOR) recommendshypothermia after cardiac arrest • Rapid dissemination intoclinicalpractice

23. Case study II:Active cooling after cardiac arrest N=1 301

24. Case study II:Active cooling after cardiac arrest All cases 2010 (N=1 301) Out-of-hospital 2010 (N=791)

25. Case study II:Active cooling after out-of-hospital cardiac arrest SIR data from 2005-2010

26. Case study II:Active cooling after cardiac arrest Out of hospital 2005-2010, hazard ratios (95% CI)

27. Case study II:Active cooling after cardiac arrest Out of hospital 2005-2010, hazard ratios (95% CI)

28. Case study II:Active cooling after cardiac arrest Normo = No active cooling Hypo = Active cooling

29. Case study II:Active cooling after cardiac arrest Conclusion • Active cooling improves survival in clinical practice • Effectiveness less than in RCT and prior registry studies

30. Case study III:Health related quality of life after ICU • Assessing health related quality of life may give important insights • You only manage what you measure

31. Case study III:Health related quality of life after ICU • Assessing health related quality of life may give important insights • You only manage what you measure • Differences related to illness severity? length of ICU-stay? treatment protocols?…… • Differences between diagnoses? gender? • Is there anything we can do about it? • Designing and exploring interventions

32. Case study III:Health related quality of life after ICU SF-36: All assessments (27 ICUs) At 2 months (N=982): Age 61 (17 – 99) yrs ICU LOS 9 (2 – 48) days SIR data from 2009-2010

33. Case study III:Health related quality of life after ICU SF-36: Complete follow-up What is the appropriate reference? For how long should we measure? Can we accelerate recovery? Designing and exploring interventions

34. The Swedish Intensive Care Registry • Not a database • Large group of people devoted • to audit and benchmarking to be • able to deliver the very best care SIR 10th Anniversary Saltsjöbaden 2011