Abstract 17252: High Low-density Lipoprotein Cholesterol Levels are Common Among High-risk Patients One Year After Initiating Statin Therapy: Results of a Large Canadian Cohort Study
Bibliographic record
Abstract
Introduction: Dyslipidemia is an important modifiable risk factor for CVD, yet studies show that dyslipidemia is undertreated in Canada. To evaluate the management of dyslipidemia in patients at high risk for CVD (including heterozygous familial hypercholesterolemia [HeFH] and statin intolerant [SI]), we assessed lipid lowering treatment patterns, LDL-C target achievement, prevalence of CV risk factors, and the incidence of CV events, in a large Canadian cohort. Methods: We retrospectively analysed data from an open cohort of patients initiating statin therapy between 2004 and 2012 using the Southwestern Ontario primary care practice database. Inclusion criteria were diagnosis of dyslipidemia, ≥ 1 statin prescription, and ≥ 2 years statin-free baseline data. Patients were stratified into 3 CV risk levels. Here, we report the results of the high-risk patient group. Results: In a cohort of 41 733 patients who initiated statin therapy, we identified 14 607 (35%) high-risk patients. At baseline, the mean (SD) age was 54 (7) years, 51% were male, and LDL-C was 3.07 (0.41) mmol/L. On average, patients had 3 CV risk factors. Age (66% male > 45 y; 90% female > 55 y), obesity (36%, BMI > 27 kg/m 2 ), high-risk hypertension (31%), and diabetes (17%) were identified as the most important risk factors. After 1-year follow-up, 25% (n=3652) of patients who initiated statin therapy failed to reach target LDL-C. Among high-risk patients with SI (n=1294), 84% (n=1089) discontinued therapy or had a dose decrease. Six months after SI diagnosis, 51% (n=656) did not meet the LDL-C target; 74% (n=484) of these patients had their statin dose decreased or stopped or were treated with a non-statin. In the prevalent cohort (new and follow-up patients in 2012), 52% (n=64) of HeFH patients were treated for secondary prevention (ie, had experienced a prior CV event) and had LDL-C > 2.59 mmol/L, and among high-risk SI patients with LDL-C > 2.59 mmol/L, 65% (n=2304) were diagnosed with coronary heart disease and 32% (n=1132) were treated for secondary prevention. Conclusions: Despite widespread statin use, a significant therapeutic need remains for CV high-risk patients who fail to reach the recommended LDL-C target.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 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".