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Cardiovascular medicine
Protocol
Cardiovascular Disease Population Risk Tool (CVDPoRT): predictive algorithm for assessing CVD risk in the community setting. A study protocol
  1. Monica Taljaard1,2,
  2. Meltem Tuna1,3,
  3. Carol Bennett1,3,
  4. Richard Perez1,3,
  5. Laura Rosella3,4,5,
  6. Jack V Tu3,6,7,
  7. Claudia Sanmartin8,
  8. Deirdre Hennessy8,
  9. Peter Tanuseputro1,3,10,
  10. Michael Lebenbaum3,
  11. Douglas G Manuel1,2,3,8,9,10
  1. 1Ottawa Hospital Research Institute, Ottawa, Ontario, Canada
  2. 2Department of Epidemiology and Community Medicine, University of Ottawa, Ottawa, Ontario, Canada
  3. 3Institute for Clinical Evaluative Sciences, Ottawa and Toronto, Ontario, Canada
  4. 4Public Health Ontario, Toronto, Ontario, Canada
  5. 5Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
  6. 6Sunnybrook Schulich Heart Centre, University of Toronto, Toronto, Ontario, Canada
  7. 7Institute of Health Policy, Management, and Evaluation, University of Toronto, Toronto, Ontario, Canada
  8. 8Health Analysis Division, Statistics Canada, Ottawa, Ontario, Canada
  9. 9Department of Family Medicine, University of Ottawa, Ottawa, Ontario, Canada
  10. 10Bruyère Research Institute, Ottawa, Ontario, Canada
  1. Correspondence to Dr Monica Taljaard; mtaljaard{at}ohri.ca

Abstract

Introduction Recent publications have called for substantial improvements in the design, conduct, analysis and reporting of prediction models. Publication of study protocols, with prespecification of key aspects of the analysis plan, can help to improve transparency, increase quality and protect against increased type I error. Valid population-based risk algorithms are essential for population health planning and policy decision-making. The purpose of this study is to develop, evaluate and apply cardiovascular disease (CVD) risk algorithms for the population setting.

Methods and analysis The Ontario sample of the Canadian Community Health Survey (2001, 2003, 2005; 77 251 respondents) will be used to assess risk factors focusing on health behaviours (physical activity, diet, smoking and alcohol use). Incident CVD outcomes will be assessed through linkage to administrative healthcare databases (619 886 person-years of follow-up until 31 December 2011). Sociodemographic factors (age, sex, immigrant status, education) and mediating factors such as presence of diabetes and hypertension will be included as predictors. Algorithms will be developed using competing risks survival analysis. The analysis plan adheres to published recommendations for the development of valid prediction models to limit the risk of overfitting and improve the quality of predictions. Key considerations are fully prespecifying the predictor variables; appropriate handling of missing data; use of flexible functions for continuous predictors; and avoiding data-driven variable selection procedures. The 2007 and 2009 surveys (approximately 50 000 respondents) will be used for validation. Calibration will be assessed overall and in predefined subgroups of importance to clinicians and policymakers.

Ethics and dissemination This study has been approved by the Ottawa Health Science Network Research Ethics Board. The findings will be disseminated through professional and scientific conferences, and in peer-reviewed journals. The algorithm will be accessible electronically for population and individual uses.

Trial registration number ClinicalTrials.gov NCT02267447.

  • PUBLIC HEALTH
  • STATISTICS & RESEARCH METHODS
  • EPIDEMIOLOGY

This is an Open Access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/

https://doi.org/10.1136/bmjopen-2014-006701

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