Blood pressure lowering in isolated diastolic hypertension and cardiovascular risk: an individual patient data meta-analysis
Bibliographic record
Abstract
BACKGROUND AND AIMS: Blood pressure (BP) lowering reduces cardiovascular disease (CVD) risk; however, the benefits of treating patients with normal systolic BP but elevated diastolic BP remain uncertain. METHODS: Data from 51 randomized controlled trials were pooled to compare BP-lowering effects in participants with and without isolated diastolic hypertension (IDH), defined as systolic BP < 130 mmHg and diastolic BP ≥ 80 mmHg. Treatment effects were stratified across baseline diastolic BP categories (range < 60 to ≥90 mmHg) among individuals with baseline systolic BP < 130 mmHg. Fixed-effect one-stage individual participant data meta-analyses were used, and Cox proportional hazard models, stratified by trial, were applied to analyse the data. RESULTS: Among 358 325 participants, 15 845 (4.4%) had IDH. At a median follow-up of 4.2 years, a 5 mmHg reduction in systolic BP reduced the risk of major cardiovascular events similarly in individuals with IDH [hazard ratio 0.91; 95% confidence interval (CI) 0.82-1.01] and those without IDH (hazard ratio 0.90; 95% CI 0.89-0.92; P for interaction = 1.00). Analyses by baseline diastolic BP showed no evidence of heterogeneity in treatment effects among individuals with baseline systolic BP < 130 mmHg (P for interaction = .26). Relative treatment effects were not statistically different by CVD history, age, prior medication use, and BP measurement methods. CONCLUSIONS: The study found no evidence to suggest that pharmacological BP-lowering therapy in individuals with IDH is less or more effective than in those without IDH. Relative risk reductions also did not diminish in those with lower diastolic BP, down to <60 mmHg at baseline. No meaningful differences across various clinical phenotypes were detected.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.028 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.053 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".