Evidence map›Paper›PMID 32631245›Full record

ArticleBMC cardiovascular disorders2020

Multi-state analysis of hypertension and mortality: application of semi-Markov model in a longitudinal cohort study.

Azra Ramezankhani, Michael J Blaha, Mohammad Hassan Mirbolouk, Fereidoun Azizi, Farzad Hadaegh

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Article in BMC cardiovascular disorders, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

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2citing papers in PubMed
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3 · Its place in the literature

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2 citing papers in PubMed.

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5 · Who and what money

Authors and funding

5 authors.

Azra RamezankhaniPrevention of Metabolic Disorders Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Michael J BlahaCiccarone Center for the Prevention of Heart Disease, Johns Hopkins University School of Medicine, Baltimore, USA.
Mohammad Hassan MirboloukCiccarone Center for the Prevention of Heart Disease, Johns Hopkins University School of Medicine, Baltimore, USA.
Fereidoun AziziEndocrine Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Farzad HadaeghPrevention of Metabolic Disorders Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran. fzhadaegh@endocrine.ac.ir.ORCID 0000-0002-8935-2744

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundMost previous research has studied the association of hypertension with cardiovascular disease (CVD) and all-cause mortality by focusing on the transition from the initial state to a single outcome. We investigated the impact of hypertension, defined according to the 2017 American College of Cardiology/American Heart Association (ACC/AHA) (new) and the Seventh Report of the Joint National Committee (JNC7) (old), on CVD death and all-cause mortality considering non-fatal CVD as an intermediate event between two CVD-free and mortality states.

methodsA total of 3002 Iranian population (47.4% men), aged ≥50 years were followed from 1999 to 2014. Two multi-state semi-Markov models with three transitions were defined for CVD death and all-cause mortality as two outcomes. The multivariable Cox model was used to estimate the effect of hypertension on transition hazards. The mean of 15-year life expectancy of participants in each transition was estimated using the restricted mean survival time.

resultsThe ACC/AHA guideline increased the prevalence of hypertension from 43.3 to 68.6%. Among CVD-free individuals, hypertension was significantly associated with increased risk of non-fatal CVD [Hazard Ratio, 1.52 (1.28-1.81) and 1.48 (1.21-1.80)], CVD death [2.96 (2.06-4.25) and 1.98 (1.30-3.04)] and all-cause mortality [1.64 (1.32-2.05) and 1.31 (1.01-1.69)] according the old and new guidelines, respectively. However, after incident non-fatal CVD, the association between hypertension and mortality events was not significant according to both definitions. Hypertensive participants experienced a first non-fatal CVD about 0.9 and 0.6 years earlier than normotensive population according to JNC7 and the 2017 ACC/AHA guidelines, respectively.

conclusionHypertension, according to JNC7 and the ACC/AHA guidelines, significantly increased the risk of mortality events among CVD-free population although the risk was attenuated using ACC/AHA guideline. Hypertension also decreased the number of years lived without CVD and early onset of CVD, and consequently, an increase in the time spent with these diseases. After non-fatal CVD, hypertension had no significant impact on mortality risk according to both guidelines.

Indexed as

Blood PressureLife ExpectancyAgedCardiovascular DiseasesFemaleHumansHypertensionIranLongitudinal StudiesMaleMarkov ChainsMiddle AgedPrevalencePrognosisRisk AssessmentRisk FactorsCardiovascularGuidelineHypertensionMortalityMulti-state

Identifiers

PMID32631245
PMCPMC7336626

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.