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