The Perfusion Downunder Collaboration:

Leveraging Our Data

Rob Baker* & Richard NewlandOn behalf of the Perfusion Downunder Collaboration

*Director Cardiac Surgery Research and PerfusionFlinders Medical Centre and Flinders University, Bedford Park, South Australia.

Perfusion Downunder Collaboration

COI’s / Disclosures

• Travel and Research support in the last 12 months– Medtronic– Cellplex Pty Ltd– Terumo Corporation

Perfusion Downunder Collaboration

A collaborative network of perfusion and interested researchers, who share

the commitment to cooperation and collaboration in the pursuit of

excellence in perfusion.

Who is the PDUC?

PDUC 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.

Perfusion Downunder Collaboration

Understand and

quantify our

practice Quality improveme

nt

Research

HLM software (DMS or JOCAP)

PDU Database

PDU TransferDatabase

De-identified Central PDU Database

PDU Collaborative Database

2007 - Current: Recruitment & Data

Dataset

(n=7769)• Total records imported (April 2011)

• Adult isolated CABG/ Valve/ Valve + CABG

(n=7364)

(n=5465)

• Jan 2007 - Feb 2011

294 after censor date

111 missing date of surgery

111 missing age

22 age <18

Dataset

• Demography – Age, Sex, Weight etc

• Clinical – Urgency, Clinical history etc

• Perfusion and quality indicators– Bypass time, management, monitoring etc– Electronic data variables

• (continuous and calculated)

• Procedure– Number of grafts, valve replacement etc

• Outcomes– Length of stay, complications etc

Risk factors and Demographics

PDUC 2007-

08

ASCTS 2007-08*

PDUC 2008-

09

ASCTS 2008-

09

PDUC 2009-

10

ASCTS 2009-10*

PDUC 2010-

11

PDUC Total

Number of patients 1191 2629 1286 2692 1530 2740 1458 5465

               

Risk Factors % % % % % % % %Current Smoker 16 14 11 15 14 14 15 14

Diabetes 28 29 29 30 27 30 28 28

Hypertension 68 71 64 72 68 73 68 67Cerebrovascular disease

9 13 10 13 10 14 10 10

Family history of heart disease

35 40 34 36 36

Hypercholesterolaemia

63 63 65 62 63

Previous cardiac intervention

17 19 17 21 19 21 18 18

Congestive heart failure

25 25 16 21 13 22 15 16

MI before surgery^ 34 20 27 20 25 20 26 28

Male 74 75** 74 70 74 72 73 74

Age > 60 68 72 71 72 71 72 72 71

Euroscore 5.9 6.4 6.1 6.4 6.2

* Based on the ASCTS Cardiac surgery in Victorian public hospitals 2009–10 public report (data reported from Victorian hospitals only). **approximate

^ MI – myocardial infarction, <21 days (ASCTS) or <90days (PDUC)

Risk Factors: Core Procedures

Postoperative outcomes

PDUC 2007-08

PDUC 2008-

09

PDUC 2009-

10

PDUC 2010-

11

PDUC Total

% % % % %

Stroke 1.6 1.1 1.8 1.7 1.6

New renal failure 2.6 2 2.1 2.5 2.3

Myocardial infarction 2.2 1.7 1.8 1 1.6

Reoperation 7.6 4.6 5.5 7.1 6.1

Ventilation > 24 hrs 11.3 13.8 15.7 15.7 14.2

30 day mortality 2.7 3.4 1.4 2.4 2.4

We are interested in what is not in other databases (ie Perfusion

variables) and relating practices to outcomes:

Components of the Circuit

Venous Reservoir Type

Pump Type

Biopassive circuit coating

Coated circuit use

Circuit coating:type

Oxygenator coating

Monitoring

Blood gas monitoring

Cerebral oximetry

BIS monitoring

Clinical incidents

Accidents reported to PIRS: 56.5%Near misses reported to PIRS: 37%

Incidents

Near misses

PIRS reports

23

(Cummulative %)

Exposure to RBC transfusion

Blood management utilisation

Overall By site

ICU blood loss

(n=2890, 384 cases missing data) (introduced nov 2007. n=2259, 393 cases missing data)

1st 4 hours Total

Continuous and Electronic data

• Quality indicators– haemoglobin <70 g/dl– blood glucose > 10 mmol– arterial temperature >37C for >2 min– arterial pressure < 40 mmHg > 5 minutes– cardiac index < 1.6 l/min/m2 > 5 minutes – venous saturation < 60% > 5 minutes– pCO2 < 35 or > 45 mmHg– pO2 <100 mmHg

• Multi-insitutional Level

Art P <40 mmHg >5 min

0

5

10

15

20

25

30

35

Pe

rce

nta

ge

of

Ca

se

s

1 2 3 4 5 6 7 8

Centre4th Harvest 5th Harvest

CI <1.6 l/min/m2 >5 min

05

101520253035404550

Pe

rce

nta

ge

of

Ca

se

s

1 2 3 4 5 6 7 8

Centre4th Harvest 5th Harvest

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”

Methods - Benchmarks

• Quality Indicators– Chosen

• Evidence / guidelines• Consensus

– arterial outlet temperature > 37oC– blood glucose < 4 or > 10 mmol/l

– pCO2 <35 or >45 mmHg

• Achievable Benchmarks of Care– Weissman et al 1999 J Eval Clin Pract 5;269-281

• Calculate adjusted performance fraction (APF)

APF = (x + 1)/(d + 2)

• Rank centres in order of performance for a specific quality indicator

• Create subset comprising top 10% best-performing centres, add centres until a subset represents at least 10% of the entire dataset is established

• Calculate benchmark based on subset as follows:

Total number of patients in subset receiving recommended interventionTotal number of patients in subset

Weissman et al 1999 J Eval Clin Pract 5;269-281

Calculating benchmarks with paired-mean method

20.3%

Arterial pCO2 < 35 or > 45 mmHg

Arterial pCO2 < 35 or > 45 mmHg

Arterial pCO2 < 35 or > 45 mmHg

Arterial outlet temperature > 37oC

6.2%

Pe

rce

nta

ge

of P

atie

nts

Factors

Arterial outlet temperature > 37oC

Cummulative site performance

Thankyou