Relationship between LDL-C Reduction after Coronary Revascularization and Prevention of Recurrence of Cardiovascular Events
Bibliographic record
Abstract
PURPOSE: The purpose of our study was to optimize lipid-lowering therapy in patients undergoing coronary revascularization and to determine whether the percentage change in low-density lipoprotein-cholesterol (LDL-C) level in the 3 months after coronary revascularization could be used as a predictor of the time to recurrence of coronary artery disease (CAD). METHODS: Biochemical values of patients undergoing lipid-lowering therapy after receiving coronary revascularization at the Nippon Medical School Chiba Hokusoh Hospital, Japan, were retrospectively investigated. Recurrence of a cardiovascular event (CVE) was defined by death, myocardial infarction, or angina caused by coronary revascularization more than 3 months after the first event. RESULTS: Of 171 patients under secondary preventive care who had at least one recurrence of a CVE, 75 showed evidence of objective stenotic lesions on coronary angiography. Among these 75 patients, exclusion of those in whom coronary revascularization had not been performed at disease onset, balloon dilatation had been used, serum lipid levels had not been measured, or coronary revascularization had been applied to restenosis left 44 patients suitable for inclusion in the study group. Although the mean value of high density lipoprotein-cholesterol did not change in the 3 months after coronary revascularization, that of (LDL-C) significantly decreased. A significant positive correlation was identified between % decrease in LDL-C and number of days to CVE recurrence. The average LDL-C value (102.8+/-21.7 mg/dL) in the group of patients with no recurrence within 5 years was significantly lower than that (135.3+/-46.1 mg/dL) in the recurrence group (P = 0.0088). The % of patients achieving the LDL-C target level (non-recurrence group vs. recurrence group: 50.0% vs. 16.7%; P = 0.032) and the % decrease in LDL-C (31.0%+/-12.6% vs. 9.6+/-21.0%, P = 0.0012) were significantly greater in the non-recurrence group than in the recurrence group. CONCLUSIONS: From our present study, a decrease in LDL-C 3 months after revascularization surgery reduces the rate of CVE relapse. The % LDL-C decrease could serve as a useful predictor of CVE recurrence, in addition to LDL-C values and achievement of the LDL-C target level.
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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 | 0.000 |
| 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".