Optimizing Post-Acute Coronary Syndrome Dyslipidemia Management: Insights from the North American Acute Coronary Syndrome Reflective III
Bibliographic record
Abstract
INTRODUCTION: Despite contemporary practice guidelines, a substantial number of post-acute coronary syndrome (ACS) patients fail to achieve guideline-recommended LDL-C thresholds. Our study aimed to investigate this guideline recommendations-to-practice care gap. Specifically, we aimed to identify opportunities where additional lipid-lowering therapies are indicated and explore reasons for the non-prescription of guideline-recommended therapies. METHODS: ACS patients with LDL-C ≥1.81 mmol/L (70 mg/dL) despite maximally tolerated statin ± ezetimibe therapy (including those intolerant of ≥2 statins) were enrolled 1-12 months post-event from 27 Canadian and US sites from September 2018 to October 2020 and followed up for three visits during the 12 months post-event. We determined the proportion of patients who did not achieve Canadian/US guideline-recommended LDL-C thresholds, the number of patients who would have been eligible for additional lipid-lowering therapies, and reasons behind lack of escalation in lipid-lowering therapies when indicated. Individual patient and aggregate practice feedback, including guideline-recommended intensification suggestions, were provided to each physician. RESULTS: Of the 248 patients enrolled in the pilot study (median age 64 [57, 73] years, 31.5% female and STEMI 27.4%), 75.4% were on high-intensity statins on the first visit. A total of 18.5% of those who attended all 3 visits had an LDL-C measured only at the first visit which was above the threshold. After 1 year of follow-up, 51.9% of patients achieved LDL-C thresholds at either visit 2 or 3. In the context of feedback reminding physicians about guideline-directed LDL-C-modifying therapy in their individual participating patients, we observed an increase in the use of ezetimibe and PCSK9 inhibitor therapy at 3-12 months. This was associated with a significant lowering of the mean LDL-C (from 2.93 mmol/L [baseline] to 2.09 mmol/L [3-6 months] to 1.87 mmol/L [6-12 months]) and a significantly greater proportion of patients (from 0% [baseline] to 38.6% [3-6 months] to 53.4% [6-12 months]) achieving guideline-recommended LDL-C thresholds. The most prevalent reasons behind the non-intensification of LDL-C-lowering therapy with ezetimibe and/or PCSK9i were LDL-C levels being close to target, the pre-existing use of other lipid-lowering therapies, patient refusal, and cost. CONCLUSION: Although most patients post-ACS were on high-intensity statin therapy, almost 50% failed to achieve guideline-recommended LDL-C thresholds by 1-year follow-up. Furthermore, additional lipid-lowering therapies in this high-risk group were underprescribed, and this might be linked to several factors including potential gaps in physician knowledge, treatment inertia, patient refusal, and cost.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".