PDUC Registry – Audit and Quality Improvement

2. PDUC Registry – Audit and Quality Improvement Robert A. Baker PhD CCP(Aust)

3. Disclosures Received Research and Travel Support from the Terumo Corporation and Cellplex Pty Ltd. The Perfusion Downunder Meeting is sponsored by Cellplex Pty Ltd.

4. In Brief! • What is the PDUC? • Where we are at! • A look at benchmarks • Audit • Quality Improvement

5. What is the Perfusion Downunder Collaboration?

6. Mission Statement To foster and grow high quality research in the perfusion sciences by the establishment and maintenance of a prospective data set on cardiac surgical procedures performed in centres throughout Australia and New Zealand.

7. 5444 procedures from six Australian and New Zealand cardiac centers in the period March 2007 to March 2010

10. Risk Factors

11. Procedure types

12. Morbidity

13. 30 Day Mortality

14. Quality Indicators • maximum glucose >10 mmol/l during CPB • maximum arterial line temperature >37C during CPB • blood gas management outside of pCO2 range 35 to 45 mmHg • NADIR Haemoglobin < 70 g/L

16. Developing Benchmark in Perfusion?

17. Defining benchmarking? • “Concept of using a structured method of quality measurement and improvement” • “Process of measuring performance using one or more specific indicators to compare activity with others”

18. What is benchmarking? • Important tool for quality improvement • Identification of ‘leaders in the field’ • Involves measuring and analysing process-of-care indicators • Integral if we want to move from performance measurement to performance improvement

19. Data Collection • 5444 procedures from six Australian and New Zealand cardiac centers in the period March 2007 to March 2010 • Benchmarking • CABG / Valve / Valve/graft • 3973 cases Subset of variables selected to facilitate comparison with the most recently published ASCTS database data

20. Methods - Benchmarks • Chosen Four QI to investigate; • maximum blood glucose • pCO2 management • minimum haemoglobin • arterial outlet temperature > 37oC

22. Achievable Benchmark of Care • Premise is to base assessment of performance on actual performance cf subjective what should/could be done • Benchmarks • Measurable level of excellence • Attainable • Selection of high performers should be structured and reproducible • All high performers should contribute • Small numbers should not dominate • Change with continuous improvement Weissman et al 1999 J Eval Clin Pract 5;269-281

24. Maximum Blood Glucose During Bypass

25. Glucose benchmark calculation • ABC denominator = 3958 (15 cases with missing data) • 10% subset = 395 eligible procedures. • Since 1st rank site has >395 eligible procedures, • Benchmark = 20%

26. Preoperative Glucose Incidence of Diabetes Preop Glucose

28. Perfusion downunder collaboration Sigrid Tuble Flinders Medical Centre Adelaide, South Australia Data Quality Assurance Towards a High Quality Clinical Database

29. What we need from a Clinical Database Base clinical & hospital management decisions upon Public & user’s Reliable Information Preserve Confidence ...a High Quality Clinical Database

30. Features of quality data Conformity to the truth All necessary data that could have been registered have been registered Accuracy Completeness

31. X = Evaluate the data quality in the PDUC Database Plan the evaluation/audit Develop audit guidelines Implementation of audit process Evaluate the quality (i.e. accuracy and completeness) of data Identify discrepancies and their cause Recommend quality assurance procedures

32. Audit guidelines

33. Selection of variables for audit Number of variables: 57 (out of 260) Relevance of the variables: Cardiac risk models (e.g. AusScore, Euroscore) Outcomes Perfusion Downunder Data missing and not entered in > 20% (Data Quality Reports October 2009)

34. VARIABLES FOR AUDIT n = 57 variables

35. Audit process Conducted in May-July 2010

36. PDUC Database audit Perfusionists = 5 Surgeons = 5 Eligible cases = 635 Audit sample size = 63 Cases/centre = 13 FPH, 31 FMC, 19 AH Completed audit = 60 (3 unavailable for AH)

37. OVERALL DATA QUALITY n = 3,420 data (57 variables × 60 cases)

38. Rates of Accuracy & completeness

39. Inaccurate data by centre Postop creatinine & dialysis Postop creatinine Length of stays Preop creatinine,ventilationtime, CABG type Ventilation time ICU time

40. causes of Errors Systematic errors Data transfer and calculation (e.g. postop dialysis and ventilation time) Unclear or ambiguous data definitions (e.g. mitral valve type) Random errors Transcription & incompleteness of data source(e.g. ICU time, mortality 30-day) Lack of adherence to data definitions (e.g. CABG type, creatinine levels)

41. Quality assurance procedure Recommendations Ensure good data definitions (clear & unambiguous) Periodic training of database personnel Reduce frequency ofIncompleteness, Non-adherence to data definitions, Inter-observer variability Comprehensive documentation of data collection guidelines High quality data collection forms Routine data monitoring programme (independent auditor or data managers)

43. PDU QUALITY CONTROL INITIATIVE Carmel Fenton; YiYi Huang; Nick Carr Royal Hobart Hospital Hobart, Tasmania, Australia Statistics: Michael Long

45. RHH FOCUS ON TRANSFUSION RATES

46. RHH QUALITY INDICATOR PROBLEM: PDU indicator for Hb at 7 g/dl RHH indicator for blood transfusion trigger is Hb of 6 g/dl Change in PDU Quality Indicator criteria to include Hb <6 g/dl

47. GOALS Decrease the number of patients with min Hb on CPB <6 g/dl Increase the minimum Hb on CPB Increase the arrival Hb into ICU Decrease the overall Blood transfusion rate in the unit particularly the earlier transfusions in ICU prior to auto diuresis

48. PDU QUALITY CONTROL INITIATIVE

49. P < 0.01

50. Retrograde Autologous Priming of the Cardiopulmonary Bypass Circuit: A Quality Improvement Initiative and Prospective Clinical Audit. Richard Newland On behalf of the Flinders Cardiac Surgery Team Flinders Medical Centre Adelaide, South Australia