STRATIFIED EFFECTS OF BLOOD PRESSURE-LOWERING TREATMENT ON LONG-TERM BLOOD PRESSURE: A META-ANALYSIS OF INDIVIDUAL-LEVEL DATA OF 334,219 PARTICIPANTS FROM 50 RANDOMIZED TRIALS
Bibliographic record
Abstract
Objective: Evidence on the magnitude of blood pressure (BP)-lowering treatment effects in large-scale randomised clinical trials is limited but is pertinent for explaining treatment effects on cardiovascular outcomes and for policy planning. We determined the effects of BP-lowering treatment on long-term BP overall and across important patient subgroups defined by their characteristics. Design and method: We conducted a one-stage individual patient-level data meta-analysis (N = 50 trials). We analysed data separately by trial design, and fitted mixed models (with fixed treatment effects, fixed time effect, random intercepts at trial and participant level, and a random slope for time at participant level). Models were adjusted for age, sex and baseline BP (except when used as stratification factor) and compared using likelihood ratio test and Akaike information criterion. Results: The analysis included 334,219 (42% women) participants with mean age = 65 years, and among whom 18% were current smokers, 47% had CVD, 29% had diabetes, and 73% were previously on BP-lowering medication. Over 4 years of follow-up, participants had an average of 8 BP measurements. For BP-lowering intensity trials (N = 8), mean differences (95% confidence interval) in systolic BP (SBP) and diastolic BP (DBP) between comparison arms were 8.2 (8.0 to 8.4) mmHg and 3.7 (3.6 to 3.9) mmHg, respectively, for placebo-controlled trials (N = 21) the SBP and DBP differences were 4.2 (4.0 to 4.3) mmHg and DBP 1.9 (1.9 to 2.0) mmHg, respectively, and for drug comparison trials (N = 28) the SBP and DBP differences were 1.3 (1.2 to 1.3) mmHg and 0.5 (0.5 to 0.5) mmHg, respectively. The reduction in BP differed by follow-up duration and patient characteristics, particularly for placebo-controlled and BP-lowering intensity trials, which showed substantial heterogeneity in BP reductions according to baseline BP, age, sex, prior CVD, diabetes and non-randomised anti-hypertensive use. Conclusions: Pharmacologic agents are effective in reducing long-term BP across individuals with different characteristics. In large-scale trials, the magnitude of BP reduction varied by several patient characteristics, which could have implications on interpretation of any differential effects of BP treatment on major clinical outcomes.
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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 | 0.046 | 0.060 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.063 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".