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Record W3107338180 · doi:10.1093/ehjci/ehaa946.2762

Stratified effects of blood pressure-lowering treatment on long-term blood pressure: an individual patient-level meta-analysis involving 50 randomised trials and 334,219 participants

2020· article· en· W3107338180 on OpenAlexaff
Dexter Canoy, Emma Copland, Rema Ramakrishnan, Ana‐Catarina Pinho‐Gomes, Milad Nazarzadeh, Zeinab Bidel, Gholamreza Salimi-Khorshidi, Mark Woodward, Barry R. Davis, Carl J. Pepine, John Chalmers, Koon Teo, Kazem Rahimi

Bibliographic record

VenueEuropean Heart Journal · 2020
Typearticle
Languageen
FieldMedicine
TopicBlood Pressure and Hypertension Studies
Canadian institutionsMcMaster UniversityPopulation Health Research Institute
Fundersnot available
KeywordsMedicineMeta-analysisBlood pressurePlaceboRandom effects modelClinical trialRandomized controlled trialInternal medicinePhysical therapy

Abstract

fetched live from OpenAlex

Abstract Background Meta-analyses of randomised controlled trials (RCT) have shown the efficacy of pharmacologic lowering of blood pressure (BP) in reducing cardiovascular disease (CVD) risk. While efficacy has been shown across important patient characteristics, meta-analysis based on aggregate data could not fully account for potential sources of variation due to individual-level characteristics. Moreover, it is unclear if any variation in treatment effects due to patient characteristics are reflected in differential effects of BP-lowering treatment on long-term BP according to these characteristics. Purpose We determined the effects of BP-lowering treatment on repeated measures of blood pressure, identified trial- and participant-level sources of heterogeneity, and examined consistency of these BP-lowering effects across different patient characteristics. Methods We conducted an individual patient-level data meta-analysis (N=50 trials) using one-stage approach. We classified trials according to trial design: drug comparison (N=28), placebo-controlled (N=21) and BP-lowering intensity (N=8) trials. We fitted mixed models with fixed treatment effects and fixed time effect, random intercepts at trial and participant level, and a random slope for time at participant level. We adjusted for age, sex and baseline BP (except when used as stratification factor). We used likelihood ratio test and Akaike information criterion to compare models. Results This meta-analysis included 334,219 (42% women) participants. At baseline, mean age=65 (SD=9) years, among whom 18% were current smokers, 47% had cardiovascular disease, 29% had diabetes, and 73% were previously on BP-lowering medication. Participants had an average of 8 BP measurements over 4 years of mean follow-up. For drug comparison trials, mean differences (95% confidence interval) in systolic BP (SBP) and diastolic BP (DBP) between comparison arms were 1.3 (1.2 to 1.3) mmHg and 0.5 (0.5 to 0.5) mmHg, respectively; for placebo-controlled trials, the SBP and DBP differences were 4.2 (4.0 to 4.3) mmHg and 1.9 (1.9 to 2.0) mmHg, respectively; and for BP-lowering intensity trials, the SBP and DBP differences were 8.2 (8.0 to 8.4) mmHg and 3.7 (3.6 to 3.9) mmHg, respectively. However, BP reduction differed by duration of follow-up, type of trial. In particular, for placebo-controlled and BP-intensity trials, heterogeneity in BP reductions according to patient characteristics such as baseline BP, age, sex, prior CVD, diabetes and non-randomised anti-hypertensive use were observed. Conclusion This study shows the role of pharmacologic agents in effectively reducing long-term BP across individuals with a wide range of characteristics. The magnitude of BP reduction varied by several patient characteristics. This might have implications for investigation and explanation of any differential effects of BP treatment on major clinical outcomes. Funding Acknowledgement Type of funding source: Public Institution(s). Main funding source(s): British Heart Foundation; NIHR Oxford Biomedial Research Centre

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.043
metaresearch head score (Gemma)0.059
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.043
Threshold uncertainty score0.230

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0430.059
Meta-epidemiology (narrow)0.0040.002
Meta-epidemiology (broad)0.0230.078
Bibliometrics0.0050.006
Science and technology studies0.0010.001
Scholarly communication0.0050.002
Open science0.0030.002
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0030.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.315
GPT teacher head0.358
Teacher spread0.043 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designMeta-analysis
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2020
Admission routes1
Has abstractyes

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