Use of Long-term Cumulative Blood Pressure in Cardiovascular Risk Prediction Models

Lindsay R Pool1, Hongyan Ning1, John Wilkins1

  • 1Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago, Illinois.

JAMA Cardiology
|September 8, 2018
PubMed

Insights

Long-term cumulative systolic blood pressure (SBP) improves atherosclerotic cardiovascular disease (ASCVD) risk prediction. Including cumulative SBP modestly enhances models for better individual risk classification.

Area of Science:

  • Cardiovascular Medicine
  • Epidemiology
  • Biostatistics

Background:

  • Long-term cumulative systolic blood pressure (SBP) is linked to atherosclerotic cardiovascular disease (ASCVD) risk.
  • Current ASCVD risk prediction algorithms rely solely on single SBP measurements.

Purpose of the Study:

  • To evaluate if incorporating 5- and 10-year cumulative SBP enhances ASCVD risk prediction compared to using current SBP alone.
  • To determine the impact of cumulative SBP on the accuracy of cardiovascular risk assessment.

Main Methods:

  • Utilized data from the Lifetime Risk Pooling Project, including Coronary Artery Risk Development in Young Adults, Atherosclerosis Risk in Communities, and Framingham Heart Study cohorts.
  • Calculated 10-year ASCVD risk using 2013 ACC/AHA equations, comparing models with current SBP versus 5- and 10-year cumulative SBP.
  • Assessed predictive ability using C statistic, net reclassification index (NRI), and integrated discrimination index (IDI).

Main Results:

  • Included 11,767 participants (mean age 59.1 years; 58% women; 13% African American).
  • No significant improvement in C statistic was observed with cumulative SBP.
  • Significant improvements in NRI (men: 0.04, women: 0.03) and relative IDI (men: 0.12, women: 0.10) were found when cumulative SBP was added.

Conclusions:

  • Long-term cumulative SBP measurements offer modest improvements in cardiovascular disease risk prediction models.
  • Incorporating cumulative SBP enhances the classification accuracy of individuals' ASCVD risk.
  • This approach refines risk stratification beyond single SBP measurements.
Abstract

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