Dyslipidemias and HMG-CoA Reductase Inhibitor Prescription in Heart Transplant Recipients
Bibliographic record
Abstract
BACKGROUND: The treatment of dyslipidemias in orthotopic heart transplant (OHT) recipients is not highlighted in the National Cholesterol Education Program Adult Treatment Panel guidelines. Emerging data suggest that hydroxymethylglutaryl-coenzyme A reductase inhibitors (statins) safely reduce the risk of transplant rejection and coronary artery vasculopathy in OHT patients. OBJECTIVE: To assess the proportion of patients from our institution reaching the low-density lipoprotein cholesterol (LDL-C) target of <100 mg/dL, evaluate the impact of statins in reaching this goal, and evaluate the prescribing practice for statins in US OHT centers. METHODS: The management of dyslipidemia of OHT recipients followed at our institution was retrospectively evaluated. In addition, the use of statins in adult OHT centers in the US that performed >or=15 OHTs per year was assessed through a survey. RESULTS: Of the 328 patients from our institution, 58.5% achieved an LDL-C <100 mg/dL. Patients prescribed statins were more likely to reach this goal (p < 0.01). A total of 85.0% of centers responding to the survey use statins as a part of their post-OHT protocol, primarily to reduce coronary artery vasculopathy (70.6%). CONCLUSIONS: Due to the potential for improved outcomes, a large proportion of patients are prescribed a statin. Our results support previous findings that statins are safe and effective in reducing LDL-C in the management of dyslipidemias in OHT recipients. Nonetheless, dyslipidemias are suboptimally managed in many post-OHT 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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".