Paige E, et al. – This research gauged the inclusion of data from repeated blood pressure and cholesterol measurements in order to speculate the cardiovascular disease (CVD) risk. It was determined that encompassing repeated measurements of blood pressure and cholesterol into CVD risk prediction models exhibited a minor improvement in the risk prediction.

Methods

• The recruitment involved 191,445 adults from the Emerging Risk Factors Collaboration (38 cohorts from 17 countries with data encompassing 1962–2014) with more than 1 million measurements of systolic blood pressure, total cholesterol, and high-density lipoprotein cholesterol.
• It was reported that over a median 12 years of follow-up, 21,170 CVD events occurred.
• A comparison was performed of the risk prediction models, using cumulative mean values of repeated measurements and summary measures from longitudinal modeling of the repeated measurements, with models using measurements from a single time point.
• An estimisk discrimination (C-index) and net reclassification were calculated, and changes in C-indices were meta-analyzed across studies.

Results

• It was observed that compared with the single-time-point model, the cumulative means and longitudinal models increased the C-index by 0.0040 (95% confidence interval (CI): 0.0023, 0.0057) and 0.0023 (95% CI: 0.0005, 0.0042), respectively.
• Reclassification improved in both models.
• Compared with the single-time-point model, overall net reclassification improvements were found to be 0.0369 (95% CI: 0.0303, 0.0436) for the cumulative-means model and 0.0177 (95% CI: 0.0110, 0.0243) for the longitudinal model.