Establishing a national cardiac surgical database: Insights from the UK & Europe

1. Establishing a national cardiac surgical database:Insights from the UK & Europe Bruce E Keogh

2. What is healthcare data used for? • Policy planning • Performance assessment • Health Authorities • Hospitals • Specialties • Consultants • Governance • Research • Audit • Public and patient information

3. The value of national databases • Benchmarking of national standards • Institutional quality assurance • Trends in practice • Anticipate financial impact • Risk models for performance monitoring • International comparisons

5. Cost of CABG related to Mortality Ratio

6. ? 1975 1980 1985 1990 1995 2000 2005 Cardiac Surgical Data Collection in the UK UK Cardiac Surgical Register UK Heart Valve Registry Adult Cardiac Database Central Cardiac Audit Database

7. ? 1975 1980 1985 1990 1995 2000 2005 Cardiac Surgical Data Collection in the UK UK Cardiac Surgical Register UK Heart Valve Registry Adult Cardiac Database UK Cardiac Surgical Register Unit Activity & mortality 250 Adult categories 200 Congenital categories Aggregated results returned to units Central Cardiac Audit Database

8. Activity and mortality trends for isolated coronary surgery (n=386,745)

9. Activity and mortality trends for combined heart valve and coronary bypass surgery

10. ? 1975 1980 1985 1990 1995 2000 2005 Cardiac Surgical Data Collection in the UK UK Cardiac Surgical Register UK Heart Valve Registry Adult Cardiac Database UK Heart Valve Registry Valve replacements only Few patient & valve variables Mortality Tracking Central Cardiac Audit Database

11. Long term survival following valve replacement in the UK UK Heart Valve Registry, 2003 N= 87,343 patients

12. United States United Kingdom Health Care Financing Administration Raw Data Internal Market Purchaser / Provider Raw data STS Standards & Ethics Committee “Statement of Concern” SCTS Executive Committee “Database Project” Changing patient profile Malpractice / Clinical Governance JCAHCO / Accreditation Board Accreditation / CME Society Driven National database Society Driven National database The need for national clinical databases

13. Change in politics

14. ? 1975 1980 1985 1990 1995 2000 2005 Cardiac Surgical Data Collection in the UK UK Cardiac Surgical Register UK Heart Valve Registry Adult Cardiac Database National Adult Cardiac Surgical Database All adult cardiac surgery 150 data-points Voluntary Central Cardiac Audit Database

15. Developing the database: McNamara’s conundrum “Do you make what is measurable important or… Do you make what is important measurable?” President Ford Motor Company (1960) 8th US Secretary of Defence (1961-8) President of the World Bank (1968-81)

16. The Measurement of Outcome • Not easy to measure outcomes for all specialties • No perfect risk algorithm for any intervention or treatment Influenced by: • Differing thresholds of referral / acceptance • Age • Severity of illness • Standard of surgery or intervention • Overall standard of institutional care • Diagnosis, treatment and clinical management

17. Potential Clinical Outcomes Mortality Near miss Length of stay Reoperation Other morbidities Best practice Discharge drugs Communication Simple to measure Easily acquired Easily validated Understandable Relevant Robust The first step: Define an outcome

18. The second step: A meaningful dataset • Clinically relevant, defined dataset • Simple • Adequate contemporary risk stratification • Surveillance • Anticipates change

19. Patient Data Demographics Cardiac history Co-morbidities Preoperative investigations Preoperative support Operative Data Operative priority Procedure data Training data Outcome Data Complications Mortality The UK National Adult Cardiac Surgical Database: Minimum Dataset

20. Is more data better? Most prediction from the first 5-7 variables

21. Hypertension Smoking Peripheral vascular disease 1980's Cerebrovascular disease 2000 Redo surgery COAD Risk factor 3-vessel disease Female Gender Renal impairment Left main stem disease Ejection fraction Emegency Odds ratio Age>70 0 1 2 3 4 5 6 7 8 Risk factor influence changes with time:Evolution of STS Risk Factors

22. Risk Stratification Systems • Some basic variables • Type of operation • Age, Gender, Re-operation, Urgency • Cardiac function, recent MI • Comorbidities: diabetes, hypertension, etc • Score allocated to each variable • Simple additive systems • Parsonnet Score, EuroSCORE • Complex statistical systems • Logistic regression, Bayesian modelling

23. Different risk models use different variables 1 2 3 4 5 6 Age LV function Reoperation Renal function EJTCVS 2000;17:400-6

24. Lessons learned from the dataset • Keep the dataset simple • Define clear definitions immediately • Anticipate future changes • Post-operative complications are difficult to collect well

25. Accuracy of data depends on use • Professionally credible • Specialty input • Publicly credible • Independent Accuracy & reliability International Institutional Individual

26. Risk factor variation in New York: Before and after report cards COAD Unstable angina Year 1 1.8% 1.9% Year 2 52.9% 20.8% Range 1.4 - 61% 0.7 - 61.4% Why is Validation Necessary? New York DoH spends 3 years validating data before release

27. The importance of data quality & validation BMJ Jan 2003

28. Lessons on data quality • Decide a strategy for data validation • Define local data checking processes for all participating hospitals • External visits are best, but may be difficult with data protection laws

29. Fifth National Adult Cardiac Surgical Database Report September 2004 256 pages Evolving quality improvement initiatives The UK Cardiac Surgical Register The UK Heart Valve Registry The Cardiac Surgical Database Unit results for CABG & AVR Unit risk factor profiles Unit missing data Individual surgeon analyses

32. ? 1975 1980 1985 1990 1995 2000 2005 Cardiac Surgical Data Collection in the UK UK Cardiac Surgical Register UK Heart Valve Registry Adult Cardiac Database National Adult Cardiac Surgical Database All adult cardiac surgery 150 data-points Mandatory Long term mortality tracking Central Cardiac Audit Database

33. Healthcare Commission The Central Cardiac Audit Database National Clinical Audit Support Programme Harmonised datasets Data collection ONS Specialty Disease CCAD Specialty Disease MINAP Ambulance Angioplasty Congenital Adult cardiac

34. The Central Cardiac Audit Database Operating theatres Hospital clinical database Encryption & Internet transmission Office of National Statistics Secure CCAD server Dendrite & NICOR at UCL Reports & research

35. Mortality rates for surgery in the UK 30 days1 year5 years CABG 1.9% 3.9%11.1% Valve 3.4% 7.1%20.1% CABG + 5.8% 11.5%24.3%Valve Other 9.9% 16.3%28.1%

36. The National Adult Cardiac Surgical Database: Long-term follow up for a single hospital

37. Survival after Isolated Aortic Surgeryin patients aged 80+: Influence of pulmonary disease Central Cardiac Audit Database: Unpublished data

38. Emerging linkages Office of National Statistics Secure CCAD National server National NHS administrative data NICOR at University College London • Commercial databases • Mosaic • Social demographics • Financial demographics • Food & purchasing Reports

39. Define a data analysis and access policy • Where will data be stored? • Who can have access? • Who checks quality of analysis? • Who authorises publication? All who submit data should have access in some way

40. Department of Health DH Data oversight in the UK Professional Standards & reports Central Cardiac Audit Database Funding & Enforcement

41. National Institute for Clinical Outcomes Research (NICOR) Principles • To provide Independent analysis of clinical data • Answerable to: • Clinical Specialist Associations • Healthcare regulator • Department of Health • Provision of data to major partners

42. Health & Social Care Information Centre Board of Directors Analytical Support Team Prof Roger Boyle Prof Adam Timmis Prof Ken Taylor Dr John Birkhead Mr Ben Bridgewater Dr David Cunningham Dr John Gibbs Dr Peter Ludman Mr James Roxburgh Departmental Head CCAD Technical team Project manager UCL Senior Lecturer (Statistics) Ambulance MINAP BCIS SCTS Paediatrics HVR Support Team Data quality Users Data managers Workshops Newsletters Analysts Data management Analyses Report writing

43. Circulation 2005;112:224-31

44. What could we have done better? • Dataset • Clear definitions from the start • Agreement on how to change the dataset • Shared ownership of data • Early collaboration with others • Hospital administration • Health Authority • Regulators • Integrate with hospital data systems

45. In summary There will be increasing healthcare regulation with increasing transparency of data. A national clinical database will facilitate: • Analysis of trends in practice • Benchmarking of national standards • Institutional and personal • Risk models for performance monitoring • International comparisons Surgeons & institutions should take the initiative

46. Where to next? • Additional quality indicators • National Quality Forum • Canadian Consensus Panel • Annals of Thoracic Surgery April 2007 • Linkage to other national data sources • Documentation of training • Maintenance of certification

47. European Union Changing clinical practice Increasing national & EU regulation • Strength in numbers, politically & statistically • Strength through shared data • Strength through supra national support • EACTS Congenital Database • ESTS Thoracic Surgery Database • EACTS adult cardiac Surgery Database Why collect European data?

48. Gathering evidence: What sort of data? • National demographics • population, age, national SMRs etc. • How many institutions in each country? • How many operations in each country? • Patient data • Demographics • Outcomes

49. Patient Data Demographics Cardiac history Co-morbidities Preoperative investigations Preoperative support Operative Data Operative priority Procedure data Training data Outcome Data 3 Complications Survival Gathering evidence: A meaningful dataset EACTS Adult Cardiac Surgical Database Dataset Harmonised with STS and UK but smaller

50. Hospital direct Considerable communication Unclear authority Ownership unclear High cost, high risk Via national associations Less communication Clear lines of authority Nationally owned Data cleaner Reproducible model Low cost, low risk Process & Methodology:How to collect data across Europe