Use of Repeated Blood Pressure and Cholesterol Measurements to Improve Cardiovascular Disease Risk Prediction: An Individual-Participant-Data Meta-Analysis

Ellie Paige, Jessica Barrett, Lisa Pennells, Michael Sweeting, Peter Willeit, Emanuele Di Angelantonio, Vilmundur Gudnason, Børge G. Nordestgaard, Bruce M. Psaty, Uri Goldbourt, Lyle G. Best, Gerd Assmann, Jukka T. Salonen, Paul J. Nietert, W.M.M. Verschuren, Eric J. Brunner, Richard A. Kronmal, Veikko Salomaa, Stephan L.J. Bakker, Gilles R. DagenaisShinichi Sato, Jan Håkan Jansson, Johann Willeit, Altan Onat, Agustin Gómez De La Cámara, Ronan Roussel, Henry Völzke, Rachel Dankner, Robert W. Tipping, Tom W. Meade, Chiara Donfrancesco, Lewis H. Kuller, Annette Peters, John Gallacher, Daan Kromhout, Hiroyasu Iso, Matthew W. Knuiman, Edoardo Casiglia, Maryam Kavousi, Luigi Palmieri, Johan Sundström, Barry R. Davis, Inger Njølstad, David Couper, John Danesh, Simon G. Thompson, Angela M. Wood*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

18 Citations (Scopus)

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 (Cindex) 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.
Original languageEnglish
Pages (from-to)899-907
JournalAmerican Journal of Epidemiology
Volume186
Issue number8
DOIs
Publication statusPublished - 15 Oct 2017

Keywords

  • Cardiovascular disease
  • Longitudinal measurements
  • Repeated measurements
  • Risk factors
  • Risk prediction

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