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

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

3. Data sources Swedish Intensive Care Registry Critical care outreach ICU-care aftercare Swedish population registry Microbiology lab data Many other ICUs Your ICU My ICU

4. Data sources Swedish Intensive Care Registry Critical care outreach ICU-care aftercare Swedish population registry Microbiology lab data Many other ICUs Your ICU My ICU Data coupling possible using Unique admission identifier Unique person identifier

5. Data sources Swedish Intensive Care Registry Critical care outreach ICU-care aftercare Swedish population registry Microbiology lab data Many other ICUs Your ICU My ICU 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. Consult Admit Treat Discharge Follow up Critical care outreach ICU outcome Withdrawal / Withholding Adverse events SOFA Nursing workload Diagnosis Key diagnosis Renal RT Ventilator therapy Procedures ICU-care aftercare SAPS 3 ICU-Higgins APACHE II PIM 2 Reason for admission Minimal dataset CardioThor ICU Pediatric ICU ICU Which data?

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

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

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

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

11. Registry metrics (DocDAT stuk) Criteria for assessing coverage and accuracy

12. Criteria …. (contd) Black et al, Qual Saf 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 Number physiologic variables missing Number of admissions Discrimination (Area under ROC curve) All admissions 31 6500.85 SIR data from 2009-2010

18. Case study I : Risk adjustment – SAPS3 Number physiologic variables missing Number of admissions Discrimination (Area under ROC curve) All admissions 31 6500.85 0 missing 16 9770.83 1 missing 4 8550.87 2 missing 3 7880.89 3 missing 1 8820.88 4 missing 1 4910.86 5 missing 7020.87 6 missing 1 1070.88 7 missing 4530.91 8 missing 960.86 9 missing 1790.87 10 missing 1200.73 SIR data from 2009-2010

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

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

21. Background 2002: First randomized controlled trials (RCT) supporting use of hypothermia after cardiac arrest are published 2003: International liaison committee on resuscitation (ILCOR) recommends hypothermia after cardiac arrest Rapid dissemination into clinical practice Case study II: Active cooling after cardiac arrest

22. Background 2002: First randomized controlled trials (RCT) supporting use of hypothermia after cardiac arrest are published 2003: International liaison committee on resuscitation (ILCOR) recommends hypothermia after cardiac arrest Rapid dissemination into clinical practice Case study II: Active cooling after cardiac arrest

23. 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. Variable Risk adjustment using APACHE II 2005-2010, N=1102 Active cooling 0.59 (0.51 – 0.68) Age (increase per 10 yrs) 1.17 (1.11 – 1.23) Female sex 1.11 (0.96 – 1.28) Trained ICU (>20 admissions) 0.82 (0.49 – 1.38) APACHE (increase per point) 1.05 (1.04 – 1.06) Out of hospital 2005-2010, hazard ratios (95% CI) Case study II: Active cooling after cardiac arrest

27. Variable Risk adjustment using APACHE II 2005-2010, N=1102 Risk adjustment using SAPS3 2008-2010, N=980 Active cooling 0.59 (0.51 – 0.68)0.71 (0.61 – 0.83) Age (increase per 10 yrs) 1.17 (1.11 – 1.23)1.01 (0.95 – 1.07) Female sex 1.11 (0.96 – 1.28)1.18 (1.01 – 1.38) Trained ICU (>20 admissions) 0.82 (0.49 – 1.38)0.91 (0.70 – 1.20) APACHE II / SAPS3 (increase per point) 1.05 (1.04 – 1.06)1.03 (1.02 – 1.04) Case study II: Active cooling after cardiac arrest Out of hospital 2005-2010, hazard ratios (95% CI)

28. SIR SSAI 2011 HACA NEJM 2002 Bernard et al NEJM 2002 Oksanen et al AAScand 2007 Arrich et al CCM 2007 Nielsen et al AAScand 2009 RegistryRCT Registry Survival Short term 30 days Normo: 28% Hypo: 42% Hospital Normo: 33% Hypo: 49% Hospital Hypo: 67% Hospital Normo: 32% Hypo: 57% Hospital Hypo: 56% Survival Long term 6 months Normo: 23% Hypo: 36% 6 months Normo: 45% Hypo: 59% 6 months Hypo: 55% 6-12 months Hypo: 50% Case study II: Active cooling after cardiac arrest Normo = No active cooling Hypo = Active cooling

29. SIR SSAI 2011 HACA NEJM 2002 Bernard et al NEJM 2002 Oksanen et al AAScand 2007 Arrich et al CCM 2007 Nielsen et al AAScand 2009 RegistryRCT Registry Survival Short term 30 days Normo: 28% Hypo: 42% Hospital Normo: 33% Hypo: 49% Hospital Hypo: 67% Hospital Normo: 32% Hypo: 57% Hospital Hypo: 56% Survival Long term 6 months Normo: 23% Hypo: 36% 6 months Normo: 45% Hypo: 59% 6 months Hypo: 55% 6-12 months Hypo: 50% 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. Assessing health related quality of life may give important insights You only manage what you measure Case study III: Health related quality of life after ICU

31. 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 Case study III: Health related quality of life after ICU

32. At 2 months (N=982): Age 61 (17 – 99) yrs ICU LOS 9 (2 – 48) days SF-36: All assessments (27 ICUs) 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 10 th Anniversary Saltsjöbaden 2011