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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".