The CANHEART ‘big data’ research initiative is aimed at measuring and improving cardiovascular health and the quality of ambulatory cardiovascular care provided in Ontario, Canada using the population-based CANHEART cohorts. The CANHEART cohorts were created through the linkage of 19+ routinely collected health administrative, vital statistics, survey and laboratory databases housed at ICES. The strength of these cohorts lies in the large sample size, containing information on 9.8 million Ontarians age 20-105 years, and diversity of the linked databases.
Building upon the insights gained to date through the CANHEART work, we have recently launched a new CANHEART SPOR project aimed at leveraging big data to conduct innovative cardiovascular clinical trials. We aim to: 1) undertake a pragmatic cluster randomized registry-based clinical trial to improve lipid-management amongst intermediate-and high-risk patients residing in high-risk health regions in Ontario (called CHOICES), and 2) develop novel algorithms for measuring clinical outcomes in clinical trials using health-related databases and compare whether they are as accurate as traditional event ascertainment methods in a prospective clinical trial (called INVESTED). These projects will be undertaken by an interdisciplinary team consisting of experts in administrative health databases, implementation science, clinical trials, knowledge translation and patient engagement.
G. Liew, P. Mitchell, T. Wong, E. Rochtchina, and J. Wang. JAMA internal medicine, 173 (4):
258-64(February 2013)JID: 101589534; ppublish;<m:linebreak></m:linebreak>
<m:linebreak></m:linebreak>Causalitat; Exemple.
C. Roumie, E. Mitchel, L. Kaltenbach, P. Arbogast, P. Gideon, and M. Griffin. Stroke; a journal of cerebral circulation, 39 (7):
2037-45(July 2008)4450<m:linebreak></m:linebreak>PUBM: Print-Electronic; JID: 0235266; 0 (Anti-Inflammatory Agents, Non-Steroidal); 0 (Cyclooxygenase 2 Inhibitors); 2008/04/24 aheadofprint; ppublish;<m:linebreak></m:linebreak>New-user designs.
L. Thabane, G. Wells, R. Cook, R. Platt, J. Pogue, E. Pullenayegum, D. Matthews, T. McCready, P. Devereaux, J. Cairns and 1 other author(s). Trials, (January 2011)6243<m:linebreak></m:linebreak>GR: Canadian Institutes of Health Research/Canada; JID: 101263253; OID: NLM: PMC3049182; 2010/11/14 received; 2011/02/18 accepted; 2011/02/18 aheadofprint; epublish;<m:linebreak></m:linebreak>Recursos/Organització; Consulting.
M. Pencina, R. D'Agostino, and O. Demler. Statistics in medicine, 31 (2):
101-13(January 2012)6488<br/>CI: Copyright (c) 2011; JID: 8215016; aheadofprint;<br/>Models predictius; Proves diagnòstiques.
K. McGeechan, P. Macaskill, L. Irwig, G. Liew, and T. Wong. Archives of internal medicine, 168 (21):
2304-10(November 2008)4871<br/>JID: 0372440; 0 (Biological Markers); ppublish;<br/>Models predictius; Avaluació de riscs.
H. Mannan, and J. McNeil. Journal of evaluation in clinical practice, 19 (2):
358-62(April 2013)6705<br/>CI: (c) 2012; JID: 9609066; aheadofprint;<br/>Models predictius; Avaluació de riscs; CV; SAS; Software estadístic.
D. Brotman, E. Walker, M. Lauer, and R. O'Brien. Archives of internal medicine, 165 (2):
138-45(January 2005)3841<m:linebreak></m:linebreak>Mesures d'associació.
G. Smith. Journal of epidemiology and community health, 60 (5):
382-8(May 2006)4828<m:linebreak></m:linebreak>JID: 7909766; 57-88-5 (Cholesterol); 9001-32-5 (Fibrinogen); ppublish;<m:linebreak></m:linebreak>Epidemiologia genètica.