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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".