Using Big Data to Conduct Innovative Cardiovascular Clinical Trials

Cardiovascular Diseases, Cardiovascular Risk Factor, Dyslipidemias

Primary Prevention, Cardiovascular disease, Lipids, Statins, Lipid management, Lipid screening

Traditional randomized clinical trials (RCTs) have provided extremely valuable information on medical therapies and procedures that have changed the way heart diseases are treated. However, despite these contributions, traditional RCTs are costly, the findings may not be applicable to patients unlike those in the study, and the use of trial findings may be infrequent. These limitations may be addressed by incorporating 'big data' in RCTs, which is the emerging field using electronic information that is routinely collected in various large administrative health databases. The Community Heart Outcomes Improvement and Cholesterol Education Study (CHOICES) will test the potential of using 'big data' in a 'real-world' clinical trial to measure outcomes using routinely collected health information. CHOICES aims to increase the use of cholesterol-lowering statin drugs to prevent heart attack and stroke in high-risk health regions across Ontario using a 'toolbox' of interventions. The 'toolbox' of interventions are informational strategies targeted for both patients and family physicians to help improve cholesterol management and contribute to shared decision making for heart healthy goals.

An estimated 19,500 cardiac events could be prevented each year in Canada by use of statin therapy as recommended in the Canadian Cardiovascular Society's Lipid Guidelines. Despite substantial evidence supporting statin use, several studies suggest dyslipidemia management in Canada remains suboptimal. In Ontario, prior work using the 2008 Cardiovascular Health in Ambulatory Care Research Team (CANHEART) 'big data' registry of almost the entire Ontario population of 9.8 million adults created through linkage of 17+ population health databases at ICES, has documented an approximate 2-fold variation across the province in cardiovascular events that is associated with performance of key cardiovascular preventive measures, particularly lipid screening and statin prescribing. This work noted that the variation did not have a clear association with traditional clinical risk factors or socioeconomic conditions. This observation suggests that heterogeneity in this care process may be modifiable with an intervention geared to improving adherence to national guidelines. In this pragmatic, cluster randomized registry trial, 'big data' is used to test the 'real world' effectiveness of a tailored, multicomponent intervention strategy aimed at improving lipid management (screening, risk assessment, statin initiation, statin adherence) amongst a primary prevention cohort of 40 to 75 year olds individuals living in 14 (of 28) communities in Ontario with higher than average rates of cardiovascular events. A multicomponent intervention strategy will include a 'toolbox' of lipid management resources for both patient and physicians in the intervention (high-risk) communities of the province. The intervention strategy will include tools to enable patients and physicians to make informed and shared decisions about statin therapy and will be implemented in the intervention communities using targeted local and social media strategies. Patient characteristics for those aged 40 to 75 and clinical outcomes in this study will be measured without primary data collection using the 2016 CANHEART 'big data' registry, with the exception of stain use and adherence data available only in adults 66 to 75 years old.

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