1. Dr. Carolyn Astley RN,DrPH, Snr project lead, cardiac rehabilitation
A Cardiac Rehabilitation Coalition: measuring and improving practice, ACRA 2018
Dr. Carolyn Astley RN,DrPH, Snr project lead, cardiac rehabilitation

2. What is the problem or gap?
Cardiac rehab (CR): effective in reducing risk factors and preventing death and readmission.1
Referral and completion are poor.2
Audit can measure service effectiveness.3
2012 the SA Cardiac Clinical Network audit (N=13) showed a 12% completion rate.
Not all services were collecting data, data variables were not the same.
Our aim was to increase referral and
completion.
1.Anderson et al. Exercise-based CR for coronary heart disease.
JACC 2016;67:1-12.
2.Clark AM et al. Factors influencing referral to CR and secondary prevention
programs; a systematic review. Eur J Prev Cardiol (4);
3.Ivers et al. Audit and feedback: effects on professionals practice and healthcare
outcomes. Cochrane Database Systematic Reviews. 2012;6:Cd
Cardiac rehab in coronary heart disease has efficacy in reducing risk factors, death and readmission
Referral and completion rates are poor
Audit systems can measure the effectiveness of services
In 2012 the SA Health cardiac clinical network showed a 12% completion rate from an audit of 13 public CR services
What we found was that some services collected no data at all and not all data was the same
Our aim was to improve referral and completion rates

3. Improving referral and completion for CR eligible patients
What did we do?
Improving referral and completion for CR eligible patients
2012:
Developed evidence –based minimum dataset
Coalition
Clinicians
Researchers
iCCnet
Heart Foundation
Agreed on standard data entry :
iCCnet/PHN
a CR telephone service for country
2015:
Built the CATCH database
Web-based data entry
centralised referral for country patients and external patients
To do this we:
Developed a minimum dataset of variables
Developed a coalition of clinicians, the integrated cardiovascular clinical network( iCCnet) and others
We agreed that services would all collect standard data
Between : iCCnet partnered with the country PHN to develop a telephone CR service for country patients
In 2015: iCCnet built the country access to cardiac health(CATCH) database with a web-based data entry function
In 2015: iCCnet introduced a centralised referral system for country and external referral patients

4. What are the outcomes?
Standardised, uniform data entry across 24 country and metropolitan CR services contributing to 3 audits.
Audit years
2013
2014
2015
Referrals (n)
5,031
4,965
5,138
Telephone
N/A
327 (16.4%)
291 (54.9%)
Face to face n(%)
100%
1,628 (81.6%)
2,063 (86.4%)
Started n (%)
1,134 (22.5%)
1,995 (40.2%)
2,387 (45.1%)
Completed n (%)
945 (83%)
1,067(68.8%)
1,466 (77.4%)
Completion rate
(% of referrals)
19%
21.4%
28.4%
We now have a standardised, uniform data entry system for country and metropolitan CR public services who have contributed to 3 audits.
Referrals have remained static at around 5,000 patients per year,
Most receive a face to face program
Those starting CR have increased to 45%
Those completing after starting range between 69-83%
Completion rate has increased to 28.4%

5. Outcomes Adjust clinical and social factors:
HR: 0.68, 95% CI: , P < 0.001 %
Readmission outcome
This is diagnosis on the x axis and percentage on the y
With attended in yellow and referred/declined in blue, readmission for a cardiovascular cause was lower in those attending as a total and by individual diagnosis
When we adjusted for clinical and social factors this effect was sustained and remained statistically significant.
ICD-10AM diagnosis

6. Outcomes % Time For the composite outcome
This is time on the x axis and percentage on the y axis
With attended in yellow, composite outcomes were lower in those attending cardiac rehab
When we adjusted for clinical and social factors this effect was sustained but was no longer statistically significant.
Time

7. Lessons learned
Our Coalition provides a mechanism for clinician engagement
Audit is an effective tool to identify practice gaps
We need a structure to facilitate service improvements in the 70% who are referred but don’t attend CR
Ongoing data management
and analysis costs
The coalition provides a mechanism for clinical engagement
Auditing is a tool to measure service effectiveness and identify practice gaps
What is missing is a structure to introduce new improvement interventions
Need efficient access and funding for ongoing data management and analysis

8. Acknowledgements
The South Australian public metro and country cardiac rehabilitation service providers:
SALHN: Sanchia Shute, Rhonda Naffin, Kerry Pasco
CALHN: Amy Wilson, Renee Henthorn
NALHN: Susan Sierp, Michelle Iadanza
CATCH phone program: Teena Wilson, Claudine Clark
CHSA leads: Ann Felder, Nicole Daws, Caroline Wilksch, Frank Circelli, Ceri Johns, Alyssia Smith, Trudy Baker, Cindy Whittlesea, Nikki Bock, Linda Marshall, Paul Whittaker, William Davies.
SA Academic and Health Science CR
Translation group
I would like to acknowledge the following cardiac rehab service providers across the SA local health networks
and the SA Academic and Health Science Translation Centre cardiac rehab group

10. Clinical and social factors adjustment
What are the outcomes?
In 2018 we linked service level data with administrative data and found;
N= 49,909 ELIGIBLE for CR
ATTENDED (n=4,286) vs NOT REFERRED ( n=34,820)
ATTENDED (n= 4,286) vs REFERRED/DECLINE
(n= 10,803)
Clinical and social factors adjustment
(HR, 95% CI)
P Value
Cardiovascular readmission
n(%)
1174/4286 (27.3%) v
12034/34820 (43.5%) vs
4478/10,803 (41.4%)
HR: 0.68
(95% CI: )
< 0.001
Death, new/re-MI, HF, AF, stroke
267/4286 (6.2%)
10,400/34,820 (29.9%)
1,583/10,803 (14.6%)
HR: 0.80 )
0.156
We conducted an analysis linking service provider data with SA Health and BDM data:
Of 49,909 eligible patients
Readmission was lower in those attending than those not referred or referred but declined to attend and after social and clinical factors adjustment
The composite outcome of death, new/re-MI, stroke, AF and HF were also lower but not significant after adjustment.
Referral rate was 30.2%.
Referral rate across the 3 years was 15,089/49,909 or 30.2%