The importance of blood pressure thresholds versus predicted cardiovascular risk on subsequent rates of cardiovascular disease: a cohort study in English primary care.


Journal

The lancet. Healthy longevity
ISSN: 2666-7568
Titre abrégé: Lancet Healthy Longev
Pays: England
ID NLM: 101773309

Informations de publication

Date de publication:
01 2022
Historique:
entrez: 14 1 2022
pubmed: 15 1 2022
medline: 15 1 2022
Statut: ppublish

Résumé

For five decades, blood pressure lowering treatment has been recommended for patients with hypertension (currently defined as blood pressure of ≥140/90 mm Hg). In the past 20 years, guidelines for treatment began incorporating predicted absolute cardiovascular disease risk (predicted risk) and reducing blood pressure thresholds. The blood pressure threshold at which to start treatment has become a secondary consideration in some countries. We aimed to provide descriptive data to assess the relative importance of blood pressure thresholds versus predicted risk on the subsequent rate of cardiovascular disease to inform treatment decisions. In this English population-based cohort study, we used linked data from the Clinical Practice Research Datalink (CPRD) GOLD, Hospital Episode Statistics Admitted Patient Care, and the Office for National Statistics mortality data, and area-based deprivation indices (Townsend scores). Eligible patients were aged 30-79 years on Jan 1, 2011 (cohort entry date) and could be linked to hospital, mortality, and deprivation data. Patients were followed up until death, end of CPRD follow-up, or Nov 31, 2018. We examined three outcomes: cardiovascular disease, markers of potential target organ damage, and incident dementia without a known cause. The rate of each outcome was estimated and stratified by systolic blood pressure and predicted 10-year risk of cardiovascular disease (QRISK2 algorithm). Between Jan 1, 2011, and Nov 31, 2018, 1 098 991 patients were included in the cohort and followed up for a median of 4·3 years (IQR 2·6-6·0; total follow-up of 4·6 million person-years). Median age at entry was 52 years (IQR 42-62) and 629 711 (57·3%) patients were female. There were 51 996 cardiovascular disease events and the overall rate of cardiovascular disease was 11·2 per 1000 person-years (95% CI 11·1-11·3). Median QRISK2 10-year predicted risk was 4·6% (IQR 1·4-12·0) and mean systolic blood pressure before cohort entry was 129·1 mm Hg (SD 15·7). Within strata of predicted risk, the effect of increasing systolic blood pressure on outcomes was small. For example, in the group with 10·0-19·9% predicted risk, rates of all cardiovascular disease rose from 20·1 to 23·6 per 1000 person-years between systolic blood pressures less than 110 mm Hg and 180 and higher mm Hg. But among patients with systolic blood pressure 140·0-149·9 mm Hg, rates rose from 6·9 to 52·3 per 1000 person-years between those with less than 10·0% risk and those with 30·0% or higher predicted risk. For a wide range of blood pressures, the rate of cardiovascular disease and effectiveness of blood pressure drug treatment was mainly determined by predicted risk, with blood pressure thresholds 140/90 mm Hg or 160/100 mm Hg-ubiquitous in most countries-adding little useful information. When medium-term predicted risk is low, there is no urgency to initiate drug treatment, allowing time to attempt non-pharmacological blood pressure reduction. National Institute for Health Research.

Sections du résumé

BACKGROUND
For five decades, blood pressure lowering treatment has been recommended for patients with hypertension (currently defined as blood pressure of ≥140/90 mm Hg). In the past 20 years, guidelines for treatment began incorporating predicted absolute cardiovascular disease risk (predicted risk) and reducing blood pressure thresholds. The blood pressure threshold at which to start treatment has become a secondary consideration in some countries. We aimed to provide descriptive data to assess the relative importance of blood pressure thresholds versus predicted risk on the subsequent rate of cardiovascular disease to inform treatment decisions.
METHODS
In this English population-based cohort study, we used linked data from the Clinical Practice Research Datalink (CPRD) GOLD, Hospital Episode Statistics Admitted Patient Care, and the Office for National Statistics mortality data, and area-based deprivation indices (Townsend scores). Eligible patients were aged 30-79 years on Jan 1, 2011 (cohort entry date) and could be linked to hospital, mortality, and deprivation data. Patients were followed up until death, end of CPRD follow-up, or Nov 31, 2018. We examined three outcomes: cardiovascular disease, markers of potential target organ damage, and incident dementia without a known cause. The rate of each outcome was estimated and stratified by systolic blood pressure and predicted 10-year risk of cardiovascular disease (QRISK2 algorithm).
FINDINGS
Between Jan 1, 2011, and Nov 31, 2018, 1 098 991 patients were included in the cohort and followed up for a median of 4·3 years (IQR 2·6-6·0; total follow-up of 4·6 million person-years). Median age at entry was 52 years (IQR 42-62) and 629 711 (57·3%) patients were female. There were 51 996 cardiovascular disease events and the overall rate of cardiovascular disease was 11·2 per 1000 person-years (95% CI 11·1-11·3). Median QRISK2 10-year predicted risk was 4·6% (IQR 1·4-12·0) and mean systolic blood pressure before cohort entry was 129·1 mm Hg (SD 15·7). Within strata of predicted risk, the effect of increasing systolic blood pressure on outcomes was small. For example, in the group with 10·0-19·9% predicted risk, rates of all cardiovascular disease rose from 20·1 to 23·6 per 1000 person-years between systolic blood pressures less than 110 mm Hg and 180 and higher mm Hg. But among patients with systolic blood pressure 140·0-149·9 mm Hg, rates rose from 6·9 to 52·3 per 1000 person-years between those with less than 10·0% risk and those with 30·0% or higher predicted risk.
INTERPRETATION
For a wide range of blood pressures, the rate of cardiovascular disease and effectiveness of blood pressure drug treatment was mainly determined by predicted risk, with blood pressure thresholds 140/90 mm Hg or 160/100 mm Hg-ubiquitous in most countries-adding little useful information. When medium-term predicted risk is low, there is no urgency to initiate drug treatment, allowing time to attempt non-pharmacological blood pressure reduction.
FUNDING
National Institute for Health Research.

Identifiants

pubmed: 35028631
doi: 10.1016/S2666-7568(21)00281-6
pii: S2666-7568(21)00281-6
pmc: PMC8732286
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Pagination

e22-e30

Subventions

Organisme : Wellcome Trust
ID : 220283/Z/20/Z
Pays : United Kingdom
Organisme : Wellcome Trust
ID : 107731/Z/15/Z
Pays : United Kingdom
Organisme : Medical Research Council
Pays : United Kingdom

Informations de copyright

© 2022 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license.

Déclaration de conflit d'intérêts

DN was on the steering group for two GlaxoSmithKline funded studies investigating aspects of kidney function in children and adults in sub-Saharan Africa; holds a grant from the UK Health Foundation on improving acute cardiac care for kidney patients; and is the Renal Association's director of informatics research. LT is a member of three Medicines and Healthcare products Regulatory Agency's expert advisory groups and an unpaid member of two non-industry funded trial advisory committees. EW received personal payment for providing training on statistical methods, outside of the submitted work. All other authors declare no competing interests.

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Auteurs

Emily Herrett (E)

Department of Non-Communicable Diseases Epidemiology, London School of Hygiene & Tropical Medicine, London, UK.

Helen Strongman (H)

Department of Non-Communicable Diseases Epidemiology, London School of Hygiene & Tropical Medicine, London, UK.

Sarah Gadd (S)

Department of Non-Communicable Diseases Epidemiology, London School of Hygiene & Tropical Medicine, London, UK.

Laurie Tomlinson (L)

Department of Non-Communicable Diseases Epidemiology, London School of Hygiene & Tropical Medicine, London, UK.

Dorothea Nitsch (D)

Department of Non-Communicable Diseases Epidemiology, London School of Hygiene & Tropical Medicine, London, UK.

Krishnan Bhaskaran (K)

Department of Non-Communicable Diseases Epidemiology, London School of Hygiene & Tropical Medicine, London, UK.

Elizabeth Williamson (E)

Department of Medical Statistics, London School of Hygiene & Tropical Medicine, London, UK.
Health Data Research UK London, London, UK.

Tjeerd van Staa (T)

Centre for Health Informatics, Division of Informatics, Imaging, and Data Science, School of Health Sciences, University of Manchester, Manchester Academic Health Science Centre, Manchester, UK.
Division of Pharmacoepidemiology and Clinical Pharmacology, Utrecht Institute of Pharmaceutical Sciences, Utrecht, Netherlands.

Reecha Sofat (R)

Institute of Health Informatics, University College London, London, UK.

Adam Timmis (A)

Queen Mary University London, London, UK.

Susan Wells (S)

Section of Epidemiology and Biostatistics, School of Population Health, University of Auckland, Auckland, New Zealand.

Liam Smeeth (L)

Department of Non-Communicable Diseases Epidemiology, London School of Hygiene & Tropical Medicine, London, UK.

Rod Jackson (R)

Section of Epidemiology and Biostatistics, School of Population Health, University of Auckland, Auckland, New Zealand.

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