Progression of Beat-to-Beat Blood Pressure Variability Despite Best Medical Management
Bibliographic record
Abstract
Beat-to-beat variability in blood pressure (BP) is associated with recurrent stroke despite good control of hypertension. However, no study has identified rates of progression of beat-to-beat BP variability (BPV), its determinants, or which patient groups are particularly affected, limiting understanding of its potential as a treatment target. In consecutive patients one month after a transient ischaemic attack or nondisabling stroke (Oxford Vascular Study), continuous noninvasive BP was measured beat-to-beat over 5 minutes (Finometer). Arterial stiffness was measured by carotid-femoral pulse wave velocity (Sphygmocor). Repeat assessments were performed at the 5-year follow-up visit and agreement determined by intraclass correlation coefficient. Rates of progression of systolic BPV (SBPV) and diastolic BPV (DBPV) and their determinants were estimated by mixed-effect linear models, adjusted for age, sex, and cardiovascular risk factors. One hundred eighty-eight of 310 surviving, eligible patients had repeat assessments after a median of 5.8 years. Pulse wave velocity was highly reproducible but SBPV and DBPV were not (intraclass correlation coefficient: 0.71, 0.10, and 0.16, respectively), however, all 3 progressed significantly (pulse wave velocity, 2.39%, P <0.0001; SBPV, 8.36%, P <0.0001; DBPV, 9.7, P <0.0001). Rate of progression of pulse wave velocity, SBPV, and DBPV all increased significantly with age ( P <0.0001), with an increasingly positive skew and were particularly associated with female sex (pulse wave velocity P =0.00035; SBPV P <0.0001; DBPV P <0.0001) and aortic mean SBP (SBPV P =0.037, DBPV P <0.0001). Beat-to-beat BP variability progresses significantly in high-risk patients, particularly in older individuals with elevated aortic systolic pressure. Beat-to-beat BPV and its progression represent potential new therapeutic targets to reduce cardiovascular risk.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".