Trends in lipoprotein(a) testing and impact on clinical care: A contemporary systemwide analysis
Bibliographic record
Abstract
: Elevated lipoprotein(a) [Lp(a)] is an independent, causal risk factor for atherosclerotic cardiovascular disease (ASCVD), yet testing remains low. As our health system has expanded its efforts to increase Lp(a) awareness, we evaluated testing rates and their impact on care. : Lp(a) testing rates were collected through electronic health record queries between 1/1/2022 to 12/31/2024. Baseline demographics, ASCVD status, Lp(a) testing rates by specialty, lipid lowering therapy (LLT) prescriptions and number of cardiology referrals were collected. : 450,412 outpatients had ≥1 lipid panel order and 3.7% (N=16,476) had Lp(a) tested. Of those who had Lp(a) measured, 50.5% were female and 61.8% identified as White. Most Lp(a) orders were for patients without established ASCVD (68.9%). Between 2022-2024, Lp(a) orders increased from 3,052 to 8,425. Most orders were placed by cardiologists although their proportion decreased (75.5% in 2022 vs. 62.9% in 2024) as orders from other specialties increased. We found 67.0% of patients with normal Lp(a) (<75 nmol/L), 12.2% were intermediate risk (75 ≥ Lp(a) < 125 nmol/L), 11.3% were high risk (125 ≥ Lp(a) < 200 nmol/L) and 9.4% had very high-risk values (≥200 nmol/L). Across the same Lp(a) categories, LLT initiation/escalation rates were 12.8%, 17.5%, 20.2% and 22.1%. There was a positive association between LLT initiation/escalation and Lp(a) range (p<0.0001). : While Lp(a) testing was low, it increased substantially over time. High risk Lp(a) levels were found irrespective of ASCVD status and were associated with more aggressive treatment. Systematic strategies to increase Lp(a) awareness and testing are warranted to mitigate cardiovascular risk.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".