Changes in urine albumin-to-creatinine ratio and health care resource utilization and costs in patients with type 2 diabetes and chronic kidney disease
Bibliographic record
Abstract
BACKGROUND: Albuminuria, indicated by an elevated urine albumin-to-creatinine ratio (UACR) at baseline, is consistently associated with poor clinical outcomes and increased economic burden. The effect of a change in albuminuria over time on health care resource utilization is not well understood. OBJECTIVE: To assess the association between changes in UACR and economic outcomes in patients with chronic kidney disease (CKD) associated with type 2 diabetes (T2D). METHODS: The Optum electronic health records database (January 2007 to September 2021) was used to identify adult patients with albuminuria, measured by UACR of 30 mg/g or more (initial test) after diagnosis of T2D and CKD. UACR change was categorized as increased (>30% change), stable (30% increase to 30% decrease), or decreased (>30% change) based on the percentage of change between the initial test and the follow-up test (the last test within 0.5 to 2 years after the initial test). All-cause inpatient (IP) admissions, emergency department (ED) visits, outpatient (OP) visits, and total medical costs were evaluated during the year after the follow-up test. The association of UACR change with health care resource utilization (HRU) was evaluated using Poisson regression, adjusting for key baseline characteristics. Medical costs (2022 US dollars) were estimated using a unit costing approach based on HRU frequencies. RESULTS: values of <0.001). CONCLUSIONS: Among patients with CKD and T2D who had albuminuria, an increase in UACR over time was associated with significantly higher HRU and costs compared with patients with stable or decreased UACR. Managed care organizations and other health care decision-makers should consider strategies that enhance monitoring and management of UACR in patients with CKD and T2D to potentially reduce HRU and associated costs.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 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".