Association Between Visit-to-Visit Variability in Blood Pressure and Cardiovascular Events in Hypertensive Patients After Successful Percutaneous Coronary Intervention
Bibliographic record
Abstract
BACKGROUND: Visit-to-visit variability (VVV) in blood pressure (BP) in addition to high BP has been shown to be a strong predictor of coronary events and stroke. Therefore, we investigated the associations between VVV in BP or BP levels and cardiovascular events after successful percutaneous coronary intervention (PCI). METHODS: We enrolled 176 hypertensive patients who had undergone successful PCI and who had four clinic visits to measure BP until follow-up coronary angiography (CAG) at 6 - 9 months after PCI. The patients were divided into those with acute coronary syndrome (ACS group; n = 50) and those with stable angina pectoris (SAP group; n = 126). We determined VVV in BP expressed as the standard deviation (SD) of average BP, average, and the maximum and minimum BP during the follow-up period. Major adverse cardiovascular events (MACEs) (myocardial infarction (MI), target lesion revascularization (TLR) and all-cause death) were also analyzed. RESULTS: There were no significant differences in VVV in BP, average BP or maximum or minimum BP between the patients with and without MACE in all patients, the ACS and SAP groups. Interestingly, in the ACS group, VVV in SBP and maximum SBP in patients with MI were significantly higher than those in patients without MI. The cut-off levels for VVV in BP and maximum SBP that gave the greatest sensitivity and specificity for MI in the ACS group were 15.1 and 138 mm Hg, respectively. CONCLUSION: Higher VVV in SBP and maximum SBP in patients with ACS after successful PCI were associated with the onset of MI.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".