Impact of model choice when studying the relationship between blood pressure variability and risk of stroke

Abstract

Long-term blood pressure variability (BPV), an increasingly recognized vascular risk factor, is challenging to define and to analyze. In order to take into account two major methodological issues - conditioning on the future and uncertainty around BPV value - we compared different models on the risk of stroke in a large dataset. We used data from the PROGRESS trial, a secondary stroke prevention trial which included 6105 subjects followed-up during four years. The median number of BP measurements was 12 and 727 strokes occurred. We compared a naive Cox model including BPV as a fixed covariate calculated on the entire follow-up, to other models aimed at avoiding conditioning on the future and taking into account uncertainty around BPV value. We found that BPV was associated with an increased risk of stroke when using a naive Cox Model (HR = 1.19, 95% CI: 1.10–1.30) but not when using other models dealing more appropriately with the issue of conditioning on the future. These results illustrate that the methods used to estimate the association between BPV and stroke may affect the estimates and that more appropriate models tend to reduce this association. Figure: http://extranet.insight-outside.fr/upload/compte1352/Base/inscriptions_projets/image/99861-image.pdf