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Benefit and harm of intensive blood pressure treatment: Derivation and validation of risk models using data from the SPRINT and ACCORD trials

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  • Sanjay Basu
  • Jeremy B Sussman
  • Joseph Rigdon
  • Lauren Steimle
  • Brian T Denton
  • Rodney A Hayward

Abstract

Background: Intensive blood pressure (BP) treatment can avert cardiovascular disease (CVD) events but can cause some serious adverse events. We sought to develop and validate risk models for predicting absolute risk difference (increased risk or decreased risk) for CVD events and serious adverse events from intensive BP therapy. A secondary aim was to test if the statistical method of elastic net regularization would improve the estimation of risk models for predicting absolute risk difference, as compared to a traditional backwards variable selection approach. Methods and findings: Cox models were derived from SPRINT trial data and validated on ACCORD-BP trial data to estimate risk of CVD events and serious adverse events; the models included terms for intensive BP treatment and heterogeneous response to intensive treatment. The Cox models were then used to estimate the absolute reduction in probability of CVD events (benefit) and absolute increase in probability of serious adverse events (harm) for each individual from intensive treatment. We compared the method of elastic net regularization, which uses repeated internal cross-validation to select variables and estimate coefficients in the presence of collinearity, to a traditional backwards variable selection approach. Data from 9,069 SPRINT participants with complete data on covariates were utilized for model development, and data from 4,498 ACCORD-BP participants with complete data were utilized for model validation. Participants were exposed to intensive (goal systolic pressure

Suggested Citation

  • Sanjay Basu & Jeremy B Sussman & Joseph Rigdon & Lauren Steimle & Brian T Denton & Rodney A Hayward, 2017. "Benefit and harm of intensive blood pressure treatment: Derivation and validation of risk models using data from the SPRINT and ACCORD trials," PLOS Medicine, Public Library of Science, vol. 14(10), pages 1-26, October.
  • Handle: RePEc:plo:pmed00:1002410
    DOI: 10.1371/journal.pmed.1002410
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    Cited by:

    1. Mingyue Xue & Li Liu & Shuxia Wang & Yinxia Su & Kun Lv & Mingchen Zhang & Hua Yao, 2020. "A simple nomogram score for screening patients with type 2 diabetes to detect those with hypertension: A cross-sectional study based on a large community survey in China," PLOS ONE, Public Library of Science, vol. 15(8), pages 1-15, August.
    2. Yizhe Xu & Tom H. Greene & Adam P. Bress & Brian C. Sauer & Brandon K. Bellows & Yue Zhang & William S. Weintraub & Andrew E. Moran & Jincheng Shen, 2022. "Estimating the optimal individualized treatment rule from a cost‐effectiveness perspective," Biometrics, The International Biometric Society, vol. 78(1), pages 337-351, March.
    3. Gal Dinstag & David Amar & Erik Ingelsson & Euan Ashley & Ron Shamir, 2019. "Personalized prediction of adverse heart and kidney events using baseline and longitudinal data from SPRINT and ACCORD," PLOS ONE, Public Library of Science, vol. 14(8), pages 1-12, August.

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