Lipid Lowering in “Very High Risk” Patients Undergoing Coronary Artery Bypass Surgery and Its Projected Reduction in Risk for Recurrent Vascular Events: A Monte Carlo Stepwise Simulation Approach
Bibliographic record
Abstract
ABSTRACT: 2018 AHA guidelines provide criteria to identify patients at very high risk (VHR) for adverse vascular events and recommend an low density lipoprotein-C (LDL-C) level <1.8 mmol/L. Data regarding the 10-year risk for adverse vascular events in coronary artery bypass grafting (CABG) patients at VHR and the need for nonstatin therapies in the VHR cohort are limited. We queried a national cohort of CABG patients to answer these questions. The projected reduction of LDL-C from stepwise escalation of lipid-lowering therapy (LLT) was simulated; Monte Carlo methods were used to account for patient-level heterogeneity in treatment effects. Data on preoperative statin therapy and LDL-C levels were obtained. In the first scenario, all eligible patients not at target LDL-C received high-intensity statins, followed by ezetimibe and then alirocumab; alternatively, bempedoic acid was also used. The 10-year risk for an adverse vascular event was estimated using a validated risk score. Potential risk reduction was estimated after simulating maximal LLT. Before CABG, 8948 of 27,443 patients (median LDL-C 85 mg/dL) were at VHR. In the whole cohort, 31% were receiving high-intensity statins. With stepwise LLT escalation, the proportion of patients at target were 60%, 78%, 86%, and 97% after high-intensity statins, ezetimibe, bempedoic acid, and alirocumab, respectively. The projected 10-year risk to suffer a vascular event reduced by 4.6%. A large proportion of CABG patients who are at VHR for vascular events fail to meet 2018 AHA LDL-C targets. A stepwise approach, particularly with the use of bempedoic acid, can significantly reduce the need for more expensive proprotein convertase subtilisin kexin 9 inhibitors.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".