Change in urine albumin-to-creatinine ratio and clinical outcomes in patients with chronic kidney disease and type 2 diabetes
Bibliographic record
Abstract
INTRODUCTION: This study aims to investigate the association between change in urine albumin-to-creatinine ratio (UACR) and clinical outcomes in patients with chronic kidney disease (CKD) and type 2 diabetes. RESEARCH DESIGN AND METHODS: Adult patients with elevated UACR (≥30 mg/g in initial testing) after the diagnosis of type 2 diabetes and CKD were identified from the Optum electronic health records database (01/2007-09/2021). UACR change from initial to last test (6-24 months) was categorized as >30% decrease, stable (-30% to 30%), or >30% increase. Risk of all-cause mortality, composite cardiovascular (CV) outcome (CV death, myocardial infarction, stroke, and hospitalization for heart failure), and CKD progression (≥40% decline in estimated glomerular filtration rate or kidney failure) were estimated with Cox proportional hazard models adjusted for baseline characteristics. RESULTS: Compared with patients with a stable UACR (n=35 117), those with a >30% UACR decrease (n=89 562) had lower risk of all-cause mortality (adjusted HR (aHR)=0.93, 95% CI 0.90 to 0.96), composite CV outcomes (aHR=0.93, 95% CI 0.90 to 0.95), and CKD progression (aHR=0.84, 95% CI 0.81 to 0.86) (all p<0.001), and patients with a >30% UACR increase (n=35 703) had higher risk of each endpoint (aHR=1.24, 95% CI 1.19 to 1.28; aHR=1.24, 95% CI 1.20 to 1.28; and aHR=1.41, 95% CI 1.36 to 1.46, respectively; all p<0.001). CONCLUSIONS: In patients with CKD and type 2 diabetes, a >30% UACR decrease was associated with lower risk of mortality, CV events, and CKD progression, whereas a >30% UACR increase was associated with higher risk of these clinical outcomes. These findings highlight the importance of albuminuria monitoring and potential clinical benefits of targeted UACR reductions in this population.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".