Characterization of lipoprotein (a) testing in Alberta, Canada: a retrospective cohort study
Bibliographic record
Abstract
BACKGROUND AND AIMS: Patients with elevated lipoprotein (a) (Lp(a)) levels face increased risk of cardiovascular events. However, Lp(a) testing has only recently been recommended as routine clinical practice. This study examines real-world baseline characteristics, healthcare resource utilization (HCRU), costs, lipid-lowering therapy (LLT) treatment intensification, and major adverse cardiovascular events (MACE) among individuals with Lp(a) testing. METHODS: This retrospective, observational study analyzed population-level administrative health data from Alberta, Canada. Individuals with Lp(a) testing were indexed on the first Lp(a) test date occurring between January 1, 2015 and March 31, 2023 and stratified by prior atherosclerotic cardiovascular disease (ASCVD) status and Lp(a) levels (≤50, >50, >70, and >90 mg/dL). RESULTS: The study included 29,229 individuals with Lp(a) testing, of which 7787 (26.6 %) had prior ASCVD. HCRU/costs in the year prior to index, and LLT intensification and MACE rates during follow-up were generally highest in individuals who had both prior ASCVD and an elevated Lp(a) level. Median total costs (per 100 patient-years) and MACE rates (95 % confidence interval, per 1000 person-years) were numerically higher in patients with prior ASCVD who also had elevated Lp(a) levels [>50 mg/dL: $9,315, 32.7 (26.6-38.9); >70 mg/dL: $11,828, 34.2 (26.7-41.6); >90 mg/dL $14,835; 33.1 (24.1-42.1)] compared to those with lower Lp(a) levels ($5,976, 27.0 (23.8-30.4)). CONCLUSIONS: Individuals with elevated Lp(a) levels and prior ASCVD had numerically greater HCRU/costs and subsequent MACE rates. Understanding of the characteristics and outcomes in the context of ASCVD status is important to develop risk assessment and management strategies for those with elevated Lp(a).
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 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".