Trends of lipid-lowering drug utilization, treatment intensity and LDL-C target attainment in adults with diabetes and non-dialysis chronic kidney disease in Hong Kong
Bibliographic record
Abstract
Background: With evolving treatment targets, concerns over renal safety for some statins and new lipid-lowering drugs (LLDs), we aimed to evaluate the trends of statins and non-statin LLDs [ezetimibe, and proprotein convertase subtilisin/kexin type 9 inhibitors (PCSK9i)] utilization in individuals with diabetes and non-dialysis chronic kidney disease (CKD) in Hong Kong. Methods: We conducted a retrospective cohort study of 332,975 Chinese individuals with diabetes and non-dialysis CKD using data from Hong Kong Hospital Authority in 2002-2019. We analyzed the annual average dosage, treatment intensity (low-intensity: <30% Low density lipoprotein-cholesterol (LDL-C) reduction; moderate-intensity: 30%-49%; high-intensity: ≥50%) and attained LDL-C targets defined by annual average LDL-C value. We evaluated the age-sex standardized trends of statin and common non-statin LLD use for primary and secondary prevention across age, sex and CKD stages. Findings: Statin-users increased from 17.6% in 2002 to 71.3% in 2019 with similar trends across age, sex and CKD stages G3-5 except for the 18-49 age group having the highest proportion of non-users of LLD (39%). By 2019, the usage of ezetimibe (1.19%) and PCSK9i (0.01%) remained low. Amongst statin-users, 27.2% received moderate-intensity therapy for primary prevention and 11.2% received high-intensity therapy for secondary prevention. In 2019, 33.3% of LLD-users achieved LDL-C < 1.8 mmol/L for primary prevention and 21.3% achieved LDL-C < 1.4 mmol/L for secondary prevention. Interpretation: Despite the increasing use of statins, treatment gaps remain with respect to treatment intensity and LDL-C target attainment in diabetes and non-dialysis CKD calling for increased use of combination statin and ezetimibe or PCSK9i to close the treatment gaps. Funding: Dr. Aimin Yang was supported by a CUHK Impact Research Fellowship Scheme.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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".