For a study, one of the progressively acknowledged vascular risk factors was the long-term blood pressure variability (BPV). However, it was difficult to examine. The purpose of the study was to evaluate the effect of BPV modeling on the expected effect on the risk of stroke. The researchers used the data of 6,105 participants, who were part of PROGRESS (Perindopril Protection Against Stroke Study). It was a secondary stroke prevention trial. The blood pressure (BP) measurements’ median value was 12 per patient. 727 out of the 6,105 patients had a first stroke recurrence over a mean follow-up period of 4.3 years. The researchers used six proportional hazards models to predict the hazard ratios (HRs) of BPV. They used the different models for comparisons. Acquiring the SD of BP measures, noted over a given period of follow-up, was the first step in the three most regularly used models. This data was utilized in a Cox model as a fixed covariate. In a single-stage analysis, the three more sophisticated models accounted for the variances in BP or BPV over a period of time. In cases of adjusted for BP at randomization or mean BP over the follow-up, the three most regularly used models showed contrasting outcomes (for a 5 mmHg increase in BPV, HR=0.75 [95% CI, 0.68–0.82], HR=0.99 [0.91–1.08], HR=1.19 [1.10–1.30]). But, the three more sophisticated models showed comparable moderate positive association (HR=1.08 [95% CI, 0.99–1.17]).

The estimated effect on the risk of stroke is strongly influenced by the technique used to measure BPV. Hence, caution was required while choosing a method. There was a requirement for additional methodological developments to explain the dynamics of BP and BPV over time and elucidate BPV’s particular role.