Data-driven collaborative QUality improvement in Cardiac Rehabilitation (QUICR) to increase program completion:protocol for a cluster randomized controlled trial

Dion Candelaria*, Julie Redfern, Adrienne O'Neil, David Brieger, Robyn A Clark, Tom Briffa, Adrian Bauman, Karice Hyun, Michelle Cunich, Gemma A Figtree, Susie Cartledge, Robyn Gallagher

*Corresponding author for this work

Research output: Contribution to journalArticleResearchpeer-review

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Abstract

BACKGROUND: 

Coronary heart disease (CHD) is the leading cause of deaths and disability worldwide. Cardiac rehabilitation (CR) effectively reduces the risk of future cardiac events and is strongly recommended in international clinical guidelines. However, CR program quality is highly variable with divergent data systems, which, when combined, potentially contribute to persistently low completion rates. The QUality Improvement in Cardiac Rehabilitation (QUICR) trial aims to determine whether a data-driven collaborative quality improvement intervention delivered at the program level over 12 months: (1) increases CR program completion in eligible patients with CHD (primary outcome), (2) reduces hospital admissions, emergency department presentations and deaths, and costs, (3) improves the proportion of patients receiving guideline-indicated CR according to national and international benchmarks, and (4) is feasible and sustainable for CR staff to implement routinely.

METHODS:

QUICR is a multi-centre, type-2, hybrid effectiveness-implementation cluster-randomized controlled trial (cRCT) with 12-month follow-up. Eligible CR programs (n = 40) and the individual patient data within them (n ~ 2,000) recruited from two Australian states (New South Wales and Victoria) are randomized 1:1 to the intervention (collaborative quality improvement intervention that uses data to identify and manage gaps in care) or control (usual care with data collection only). This sample size is required to achieve 80% power to detect a difference in completion rate of 22%. Outcomes will be assessed using intention-to-treat principles. Mixed-effects linear and logistic regression models accounting for clusters within allocated groupings will be applied to analyse primary and secondary outcomes.

DISCUSSION: 

Addressing poor participation in CR by patients with CHD has been a longstanding challenge that needs innovative strategies to change the status-quo. This trial will harness the collaborative power of CR programs working simultaneously on common problem areas and using local data to drive performance. The use of data linkage for collection of outcomes offers an efficient way to evaluate this intervention and support the improvement of health service delivery.

ETHICS: 

Primary ethical approval was obtained from the Northern Sydney Local Health District Human Research Ethics Committee (2023/ETH01093), along with site-specific governance approvals.

TRIAL REGISTRATION:

Australian New Zealand Clinical Trials Registry (ANZCTR) ACTRN12623001239651 (30/11/2023) ( https://anzctr.org.au/Trial/Registration/TrialReview.aspx?id=386540&isReview=true ).

Original languageEnglish
Article number302
Pages (from-to)1-9
Number of pages9
JournalBMC Cardiovascular Disorders
Volume24
Issue number1
DOIs
Publication statusPublished - 14 Jun 2024
Externally publishedYes

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