Management of dyslipidaemia in individuals with severe mental illness: a population-based study in the Greater Copenhagen Area
Bibliographic record
Abstract
Background: Severe mental illness (SMI) is associated with increased cardiovascular risk. Dyslipidaemia is a potentially modifiable risk factor, which may be inadequately managed in patients with SMI. Objectives: To assess management of dyslipidaemia in patients with SMI versus healthy controls (HCs) in 2005 and 2015. Design and methods: Using Danish registers, we identified adult patients with SMI in the Greater Copenhagen Area (schizophrenia spectrum disorders or bipolar disorder) with ⩾1 general practitioner contact in the year before 2005 and 2015, respectively, and HCs without SMI matched on age and gender (1:5). Outcomes were lipid-profile measurements, presence of dyslipidaemia and redemption of lipid-lowering pharmacotherapy. Differences in outcomes between patients with SMI and controls were measured with multivariable logistic regression. Results: We identified 7217 patients with SMI in 2005 and 9939 in 2015. After 10 years, patients went from having lower odds of lipid measurements to having higher odds of lipid measurements compared with HCs [odds ratio (OR) 2005 0.70 (99% confidence interval (CI) 0.63–0.78) versus OR 2015 1.34 (99% CI 1.24–1.44); p 2005 versus2015 < 0.01]. Patients had higher odds of dyslipidaemia during both years [OR 2005 1.43 (99% CI 1.10–1.85) and OR 2015 1.23 (99% CI 1.08–1.41)]. Patients went from having lower odds of receiving lipid-lowering pharmacotherapy to having higher odds of receiving lipid-lowering pharmacotherapy [OR 2005 0.77 (99% CI 0.66–0.89) versus OR 2015 1.37 (99% CI 1.24–1.51); p 2005 versus2015 < 0.01]. However, among persons at high cardiovascular risk, patients had lower odds of receiving lipid-lowering pharmacotherapy during both years, including subsets with previous acute coronary syndrome [OR 2005 0.30 (99% CI 0.15–0.59) and OR 2015 0.44 (99% CI 0.24–0.83)] and ischaemic stroke or transient ischaemic attack (TIA) [OR 2005 0.43 (99% CI 0.26–0.69) and OR 2015 0.61 (99% CI 0.41–0.89)]. Conclusion: These results imply an increased general awareness of managing dyslipidaemia among patients with SMI in the primary prophylaxis of cardiovascular disease. However, secondary prevention with lipid-lowering drugs in patients with SMI at high cardiovascular risk may be lacking.
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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) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".