Background Previous tools for stroke risk assessment have either been developed for specific populations or lack data on non-fatal events or uniform data collection. The purpose of this study was to develop a stepwise model for the estimation of 10 year risk of stroke in nine different countries across Europe.
Methods Using data from the MOnica Risk, Genetics, Archiving and Monograph (MORGAM) Project, sex-specific models estimating 10 year risk of stroke were developed using a Cox regression model stratified by country and including modelling of competing risks. Models were developed in a stepwise manner first using only data from questionnaires, and then adding data from physical examinations and finally data from blood samples.
Results During 1 176 296 years of observation, 2928 incident fatal and non-fatal events of stroke were registered. The developed model showed good calibration and accuracy of prediction. The discrimination of the model varied between sex and country but increased with increasing number of variables used (area under the receiver operating characteristic curve between 0.77 and 0.79 in men and between 0.75 and 0.80 in women).
Conclusion The present study shows that using a large multicountry cohort from nine European countries it is possible to develop a stepwise risk estimation model for 10 year risk of stroke tailored to different availability of risk factors and still obtain valid measures of risk even in the simplest form of the model, with increasing performance of the model following increasing complexity. The methods chosen which separate this model from previous models (competing risk and stepwise approach) should be considered for future risk estimation models.
- risk stratification
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MORGAM Project participants: see Appendix.
Funding This research was part funded through the European Community's Seventh Framework Programme (FP7/2007-2013), ENGAGE project, grant agreement HEALTH-F4-2007-201413.
Competing interests None.
Provenance and peer review Not commissioned; externally peer reviewed.
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