Abstract
The added value of incorporating information from repeated blood pressure and cholesterol measurements to predict cardiovascular disease (CVD) risk has not been rigorously assessed. We used data on 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. Over a median 12 years of follow-up, 21,170 CVD events occurred. Risk prediction models using cumulative mean values of repeated measurements and summary measures from longitudinal modeling of the repeated measurements were compared with models using measurements from a single time point. Risk discrimination (C-index) and net reclassification were calculated, and changes in C-indices were meta-analyzed across studies. 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 was also improved in both models; compared with the single-time-point model, overall net reclassification improvements were 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. In conclusion, incorporating repeated measurements of blood pressure and cholesterol into CVD risk prediction models slightly improves risk prediction.
Highlights
net reclassification improvement (NRI) was improved in model 2 compared with model 3 (P < 0.001), primarily due to greater specificity in model 2 compared with model 3 (P < 0.001)
Stratification of patients according to risk is used to guide clinical treatment decisions, and interpretation of the clinical benefit of adding a predictor to a risk model typically takes into account both improvements in discrimination and risk reclassification, as well as the cost of obtaining the risk factor data
Both methods for modeling repeated measurements improved the sensitivity of the model, and the cumulativemeans model produced slight gains in specificity compared with the use of single measurements of risk predictors
Summary
NRI was improved in model 2 compared with model 3 (P < 0.001), primarily due to greater specificity in model 2 compared with model 3 (P < 0.001). Incorporating repeated measurements of SBP, total cholesterol, and HDL cholesterol into the risk models improved their sensitivity (for model 2 vs model 1, event NRI = 1.54% (95% CI: 0.84, 2.24), and for model 3 vs model 1, event NRI = 2.14% (95% CI: 1.48, 2.79); Table 3).
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
