1482-P: Low-Density Lipoprotein Cholesterol (LDL-C) Management in Patients with Atherosclerotic Cardiovascular Diseases (ASCVD) and Preexisting Diabetes in Alberta, Canada
Bibliographic record
Abstract
Background: Lipid-lowering therapy (LLT) reduces the risk of CV events, however, limited real-world data exist on the management of LDL-C in patients with ASCVD and pre-existing diabetes in Canada. Our study describes the clinical characteristics and LDL-C management of patients with ASCVD and diabetes using health system data in Alberta. Methods: A retrospective study was conducted linking multiple health system datasets to examine clinical characteristics and LDL-C levels in those receiving LLT (e.g., statins) after the first LDL-C test. Patients with ASCVD were identified using ICD diagnostic codes between 2011-2015. Similarly, diabetes status was assessed in the year prior to ASCVD diagnosis. LDL-C was assessed at the first (index) and second (follow-up) tests during the study period. LDL-C levels were evaluated based on a threshold of 2.0 mmol/L, as per the 2016 Canadian blood cholesterol management guidelines. Results: Among 144,607 patients with ASCVD and a prescription for LLT (mean age=66.3; 66% male), 27,540 (19.0%) patients were identified with diabetes. Patients with diabetes were more likely to have stroke, myocardial infarction, or peripheral arterial disease. Congestive heart failure and hypertension were found in 26.8% and 87.8% of patients with diabetes compared to 12.5% and 71.5% in those without, respectively. Of the patients with diabetes who had an index and follow-up LDL-C test (n=18,214), 35.0% (n=6,380) did not achieve the guideline specified LDL-C threshold at index. At follow-up (mean=238.9 days after index), 49.0% (n=3,129) of those who did not achieve threshold at index failed to meet the threshold. Conclusions: Nearly half of patients above the LDL-C threshold at the index test did not achieve threshold at the follow-up test, despite receiving LLT. Multifaceted interventions may be required to improve cholesterol management of patients with ASCVD and diabetes in Alberta. Disclosure G. Chen: Consultant; Self; Medlior Health Outcomes Research Ltd. M.S. Farris: Employee; Self; Medlior Health Outcomes Research Ltd. T. Cowling: Stock/Shareholder; Self; Medlior Health Outcomes Research Ltd. M. Tai: Employee; Self; Amgen Inc. L. Pinto: Employee; Self; Amgen Inc. S. Colgan: Employee; Self; Amgen Inc. R. Rogoza: Employee; Self; Amgen Inc. Stock/Shareholder; Self; Amgen Inc. T.J. Anderson: None.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".