How Did the Updated 2019 European Society of Cardiology/European Atherosclerosis Society Risk Categorization for Patients with Diabetes Affect the Risk Perception and Lipid Goals? A Simulated Analysis of Real-life Data from EPHESUS Study
Bibliographic record
Abstract
BACKGROUND: The recent 2019 European Society of Cardiology/European Atherosclerosis Society practice guidelines introduced a new risk categorization for patients with diabetes. We aimed to compare the implications of the 2016 and 2019 European Society of Cardiology/European Atherosclerosis Society guidelines with regard to the lipid-lowering treatment use, low-density lipoprotein cholesterol goal attainment rates, and the estimated proportion of patients who would be at goal in an ideal setting. METHODS: Patients with diabetes were classified into 4 risk categories according to 2019 European Society of Cardiology/European Atherosclerosis Society dyslipidemia guidelines from the database of EPHESUS (cross-sectional, observational, countrywide registry of cardiology outpatient clinics) study. The use of lipid-lowering treatment and low-density lipoprotein cholesterol goal attainment rates were then compared according to previous and new guidelines. RESULTS: This analysis included a total of 873 diabetic adults. Half of the study population (53.8%) were on lipid-lowering treatment and almost one-fifth (19.1%) were on high-intensity statins. While low-density lipoprotein cholesterol goal was achieved in 19.5% and 7.5% of patients, 87.4% and 69.6% would be on target if their lipid-lowering treatment was intensified according to 2016 and 2019 European Society of Cardiology/European Atherosclerosis Society lipid guidelines, respectively. The new target <55 mg/dL could only be achieved in 2.2% and 8.1% of very high-risk primary prevention and secondary prevention patients, respectively. CONCLUSION: The control of dyslipidemia was extremely poor among patients with diabetes. The use of lipid-lowering treatment was not at the desired level, and high-intensity lipid-lowering treatment use was even lower. Our simulation model showed that the high-dose statin plus ezetimibe therapy would improve goal attainment; however, it would not be possible to get goals with this treatment in more than one-third of the patients.
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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 | 0.007 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".