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Impact of frailty on antihypertensive treatment in older adults

2025· article· en· W7127937918 on OpenAlexaff
K. Harris, L Chen, X. T. Chen, C S Anderson, J Chalmers, Ruth E. Hubbard, K Rockwood, R. Wang, David Ward, J Williamson, M Woodward, R Peters

Bibliographic record

VenueEuropean Heart Journal · 2025
Typearticle
Languageen
FieldMedicine
TopicFrailty in Older Adults
Canadian institutionsDalhousie University
Fundersnot available
KeywordsStroke (engine)Observational studyProportional hazards modelBlood pressureHazard ratioFrailty IndexRisk factor

Abstract

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Abstract Background High blood pressure (BP) is a risk factor for cardiovascular disease and premature mortality. Observational studies have shown differential effects of high BP in frail and non-frail people. Consequently, the former may need different approaches to manage their BP compared to non-frail individuals. Purpose We aimed to determine the impact of frailty on MI, stroke and mortality outcomes, and to examine whether the effect of antihypertensive treatment on those outcomes varies by frailty status. Methods Single-stage individual-participant-data meta-analysis. Data were merged from four landmark double-blind placebo-controlled trials of antihypertensive drugs with blinded adjudicated non-fatal MI, non-fatal stroke and all-cause mortality endpoints. Baseline frailty was assessed using a robust tool, the frailty index (FI), and modelled as a continuous (per SD increase) and binary variable ≤0.21 for none/mild and >0.21 for moderate-severe frailty. Cox regression (adjusted for age, sex, education) yielding hazard ratios (HRs) for mortality and Fine and Grey models yielding subdistribution HRs (sHRs) for MI and stroke accounting for the competing risk of mortality, were used to model the associations of frailty on outcomes, and of antihypertensive treatment on outcomes by frailty status. Results Data were available for 24,122 participants (mean age 68.5 (SD 9.31) years, 44% female) with a median follow up of 4.3 years. The mean (SD) FI was 0.175 (0.086) and median (interquartile interval) 0.16 (0.11-0.23), and 29% of participants had moderate-severe frailty. Frailty (per SD increase) was associated with a higher risk of MI sHR 1.50 (95%CI 1.40, 1.61), stroke 1.21 (1.15, 1.28) and all-cause mortality HR 1.49 (95%CI 1.43, 1.55). The impact of anti-hypertensive treatment on non-fatal stroke for those with none/mild frailty was sHR 0.72 (95%CI 0.63, 0.82) compared to 1.00 (0.82, 1.23) for those with moderate-severe frailty (p for interaction = 0.008). Corresponding sHRs for MI were 0.74 (0.59, 0.93) and 0.87 (0.68, 1.12) (p=0.335) and HRs for all-cause mortality were 0.89 (0.79, 0.99) and 0.89 (0.79, 1.02) (p=0.920). Conclusion Individuals with higher frailty are at higher risk of MI, stroke and premature mortality. Antihypertensive treatment was shown to be effective regardless of frailty status on MI and all-cause mortality, but there was evidence of a differential effect on stroke outcomes. These data have implications for treatment guidelines for cardiovascular prevention alongside approaches to the management of people at high-risk of adverse outcomes.

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)0.012
metaresearch head score (Gemma)0.018
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.064

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.018
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.006
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.039
GPT teacher head0.348
Teacher spread0.309 · 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 designObservational
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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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