Does LDL-C determination method affect statin prescribing for primary prevention? A register-based study in Southern Denmark
Bibliographic record
Abstract
AIMS: Examine whether the low-density lipoprotein cholesterol (LDL -C) determination method influences the rate of statin initiation for primary prevention of cardiovascular disease. METHODS AND RESULTS: We conducted a register-based retrospective study in the Region of Southern Denmark. Two hospital-based laboratories in the region directly measure LDL -C whereas four laboratories calculate LDL -C using Friedewald's formula. Physicians do not choose which method is used. We included all statin-naïve patients ≥40 years with no history of cardiovascular disease, diabetes, or chronic kidney disease, who had their LDL -C determined during 2018-2019. There were 202 807 people who had LDL -C determined during the study period (median age 59 years, 44% women) of which 37% had a direct LDL -C measurement. The median reported LDL -C was 3.40 mmol/L [interquartile range (IQR) 2.90-4.00] for those with a direct measurement vs. 3.00 mmol/L (IQR 2.40-3.50) for those with calculated LDL -C. For those with direct measurement, re-calculated LDL -C (using Friedewald's formula) was 0.35 mmol/L lower than the reported direct LDL -C measurement. Among those with directly measured LDL -C, 3.6% initiated statins compared with 2.7% of those with a calculated LDL -C. Direct LDL -C measurement led to higher odds of having a statin initiated compared with calculated LDL -C (adjusted odds ratio 1.23, 95% CI 1.17-1.30); for those with triglycerides >1.7 mmol/L the adjusted odds ratio was 1.41 (95% CI 1.30-1.52). CONCLUSION: Differences in the reporting of LDL -C from laboratories using different methods have a substantial influence on physician's decisions to prescribe statins.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 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".