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

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. 2013. 20(4);692-700. 3.Ivers et al. Audit and feedback: effects on professionals practice and healthcare outcomes. Cochrane Database Systematic Reviews. 2012;6:Cd000259. 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

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 2013-2016: 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 2013-2016: 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

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%

Outcomes Adjust clinical and social factors: HR: 0.68, 95% CI: 0.57-0.80, 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

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

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

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

Clinical and social factors adjustment What are the outcomes? In 2018 we linked 2013-2015 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: 0.57-0.80) < 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 0.60-1.08) 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%