International Practice Patterns of Dyslipidemia Management in Patients with Chronic Kidney Disease under Nephrology Care: Is it time to review guideline recommendations?
Bibliographic record
Abstract
Abstract Background In contrast to guidelines related to lipid therapy in other areas, 2012 Kidney Disease Improving Global Outcomes (KDIGO) guidelines recommend conducting a lipid profile upon diagnosis of chronic kidney disease (CKD) and treating all patients older than 50 years without defining a target for lipid levels. We evaluated multinational practice patterns for lipid management in patients with advanced CKD under nephrology care. Methods We analyzed lipid-lowering therapy (LLT), LDL- cholesterol (LDL-C) levels, and nephrologist-specified LDL-C goal upper limits in adult patients with eGFR < 60ml/min from nephrology clinics in Brazil, France, Germany, and the United States (2014–2019). Models were adjusted for CKD stage, country, cardiovascular risk indicators, sex, and age. Results LLT treatment differed significantly by country, from 51% in Germany to 61% in the US and France (p = 0.002) for statin monotherapy. For ezetimibe with or without statins, the prevalence was 0.3% in Brazil to 9% in France (< 0.001). Compared with patients not taking lipid-lowering therapy, LDL-C was lower among treated patients (p < 0.0001) and differed significantly by country (p < 0.0001). At the patient level, the LDL-C levels and statin prescription did not vary significantly by CKD stage (p = 0.09 LDL-C and p = 0.24 statin use). Between 7–23% of untreated patients in each country had LDL-C ≥ 160 mg/dL. Only 7–17% of nephrologists believed that LDL-C should be < 70 mg/dL. Conclusion There is substantial variation in practice patterns regarding LLT across countries but not across CKD stages. Treated patients appear to benefit from LDL-C lowering, yet a significant proportion of hyperlipidemia patients under nephrologist care are not receiving treatment.
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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 | 0.027 | 0.124 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".