Effect of Lowering Diastolic Pressure in Patients With and Without Cardiovascular Disease
Bibliographic record
Abstract
Systolic and diastolic blood pressure thresholds, below which cardiovascular events increase, are widely debated. Using data from the SPRINT (Systolic Blood Pressure Intervention Trial), we evaluated the relation between systolic and diastolic pressure and cardiovascular events among 1519 participants with or 7574 without prior cardiovascular disease. Using Cox regression, we examined the composite risk of myocardial infarction, other acute coronary syndrome, stroke, heart failure, or cardiovascular death, and follow-up systolic and diastolic pressure were analyzed as time-dependent covariates for a median of 3.1 years. Models were adjusted for age, sex, baseline systolic pressure, body mass index, 10-year Framingham risk score, and estimated glomerular filtration rate. A J-shaped relationship with diastolic pressure was observed in both treatment arms in patients with or without cardiovascular disease ( P nonlinearity≤0.002). When diastolic pressure fell <55 mm Hg, the hazards were at least 25% higher relative to 70 mm Hg ( P =0.29). The hazard ratios (95% CI) of diastolic pressure <55 mm Hg versus 55 to 90 mm Hg were 1.68 (1.16–2.43), P value 0.006 and 1.52 (0.99–2.34), P value 0.06 in patients without and with prior cardiovascular disease, respectively. After adjusting for follow-up diastolic pressure, follow-up systolic pressure was not associated with the outcome in those without prior cardiovascular disease ( P =0.64). In those with cardiovascular disease, adjusting for diastolic pressure, follow-up systolic pressure was associated with the risk in the intensive arm (hazard ratio per 10 mm Hg decrease, 0.86; 95% CI, 0.75–0.99; P interaction=0.02). Although the observed J-shaped relationship may be because of reverse causality in the SPRINT population, we advise caution in aggressively lowering diastolic pressure.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".