The association and predictive value analysis of metabolic syndrome on diastolic heart failure in patients at high risk for coronary artery disease
Bibliographic record
Abstract
BACKGROUND: The purpose of the present study was to evaluate the effect and predictive value of metabolic syndrome (MetS) and its components on diastolic heart failure (DHF) in patients at high risk for coronary artery disease (CAD). MATERIALS AND METHODS: We enrolled 261 patients with normal left ventricular ejection fraction (≥50%) who were scheduled to undergo coronary angiography for suspected myocardial ischemia. They were categorized into three groups (non-MetS, pre-MetS and MetS) based on the number of MetS criteria. Echocardiography was used to assess left ventricular (LV) diastolic function. The association between MetS and DHF was assessed by multivariate logistic regression (MLR) analysis (non-DHF patients as reference group) after controlling for confounders. The predictive performance of the MetS severity score (MSS) was evaluated using the area under the receiver-operating characteristic curve (AUC). RESULTS: A tendency toward increased DHF prevalence with increasing MSS was found (p < 0.001). MLR analysis showed that in patients with an MSS of 1, the odds ratio (OR) of DHF was 1.60 (95% confidence interval-CI, 1.19-2.16; p = 0.02) compared to non-DHF patients; in patients with MSS ≥4, the OR was 6.61 (95% CI, 4.90-8.90; p < 0.001) compared to non-DHF patients. MSSs strongly predicted DHF (AUC = 0.73, 95% CI, 0.66-0.78, p < 0.001). MLR with MetS components as binary variables showed that blood pressure (BP) and triglycerides (TGs) were significantly associated with DHF (P = 0.001 and 0.043, respectively). CONCLUSION: Our findings signify that MetS and its components of BP or TG were associated with DHF in high-risk CAD patients. DHF prevalence tends to increase with increasing MSS that has a high value in predicting DHF in high-risk CAD patients.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".