Cardiac Networks United: an integrated paediatric and congenital cardiovascular research and improvement network

Abstract

Optimising short-and long-term outcomes for children and patients with CHD depends on continued scientific discovery and translation to clinical improvements in a coordinated effort by multiple stakeholders. Several challenges remain for clinicians, researchers, administrators, patients, and families seeking continuous scientific and clinical advancements in the field. We describe a new integrated research and improvement network -Cardiac Networks Unitedthat seeks to build upon the experience and success achieved to-date to create a new infrastructure for research and quality improvement that will serve the needs of the paediatric and congenital heart community in the future. Existing gaps in data integration and barriers to improvement are described, along with the mission and vision, organisational structure, and early objectives of Cardiac Networks United. Finally, representatives of key stakeholder groupsheart centre executives, research leaders, learning health system experts, and parent advocatesoffer their perspectives on the need for this new collaborative effort.Clinical outcomes for children and patients with CHD improved considerably over the past several decades. However, several domains still pose a challenge to clinicians and researchers seeking to optimise care and outcomes for these children and families. Early morbidity and mortality remain high for patients undergoing high-complexity surgery. Data on outcomes from across the lifespan are scant, but those that exist suggest burdensome physical and functional morbidities across multiple domains. Finally, there remains substantial variability in care, outcomes, and costs across hospitals in providing care for these patients.To find a mechanism to address these challenges, the field of paediatric and CHD has invested considerably in recent years to improve data capture and analysis from multiple sources in order to learn and improve. Although these efforts have led to several notable advances, 1-6 important limitations remain. These include lack of integration across data sources that limits our capabilities to drive discovery across the lifespan, limited mechanisms to translate knowledge gained from these data into tangible improvements in patient care and outcomes, and the overall cost and sustainability of such efforts. Many children's hospitals that participate in clinical data registries and other networks report spending in excess of $500,000-1,000,000 per year to support the necessary personnel and infrastructure, and the key stakeholders have voiced concerns regarding inadequate return on that investment in relation to scientific discovery and improvements in clinical outcomes. A new paradigm for this work is necessary to maximise the value of these data and accelerate discovery and Cardiology in the Young

The Fontan outcomes network: first steps towards building a lifespan registry for individuals with Fontan circulation in the United States

“…The collaboration among data scientists, cardiologists, and information system specialists will be key to the success of the Fontan Outcomes Network registry by improving the data collection process. 15 Furthermore, the advancements in artificial intelligence and natural language processing are promising for solving medical record data collection challenges. 30 While improvements in facilitating the ease of data collection are made, there will also be efforts to learn from the network centres.…”

“…21 Further, both National Pediatric Cardiology Quality Improvement Collaborative and Advanced Cardiac Therapies Improving Outcomes Network are members of Cardiac Networks United, a consortium of paediatric and congenital networks that aims to accelerate learning and discovery through collaborative sharing of data to maximise return on investment and sustainability for organisations funding and participating in research and quality improvement. 15 These successes of the learning health networks proves the effectiveness of this model and led our group to embrace this model in order to improve outcomes in Fontan patients. The Australian and New Zealand Fontan Registry, also started in 2009, has successfully generated foundational knowledge at the population level.…”

“…In order to accelerate discovery that was impossible through single-centre research, collaboration among congenital heart centres has been enhanced, with an emphasis on patient and family engagement, quality improvement, and research. [14][15][16][17][18] The learning health network is a dynamic platform of multi-institutional collaboration for quality improvement and research, which facilitates identification of best clinical practices, rapid sharing of data to improve outcomes, generation of new knowledge, and translation of research into practice. 19,20 For single ventricle infants, specifically those with hypoplastic left heart syndrome, the National Pediatric Cardiology Quality Improvement Collaborative has improved interstage survival by 40% since 2009 using the learning health network model.…”

Updates in Congenital Heart Disease: Can Outcomes Improve?

“…24 Translating scientific discovery and advances in clinical care to optimize care for both long-and short-term outcomes requires a coordinated effort by multiple stakeholders. 25 A recent expansion of databases and registries has created a necessity to streamline data collection, analytics, and outcomes reporting to simplify research, quality improvement, and patient advocacy. Cardiac Networks United 25 has been launched to integrate pediatric and congenital cardiovascular research and quality improvement networks and databases.…”

Enhancing efficiency and scientific impact of a clinical trials network: the Pediatric Heart Network Integrated CARdiac Data and Outcomes (iCARD) Collaborative

“…These have included an exponential increase in the variety of healthcare data captured across numerous sources such as clinical registries, the electronic health record, billing data, various monitors and wearable devices, and genetic and biomarker data, among others. [2][3][4][5][6][7] At the same time, there has been a concurrent trend characterised by a relative decline federal funding to support medical research and other financial pressures faced by hospitals and health systems. 8 These two trends have prompted greater interest in understanding whether the increasing number and variety of data sets hold the potential to support novel investigation that would otherwise not be possible, particularly through the linkage of disparate sources of information, and allow us to conduct research more efficiently.…”