Change in Urine Albumin-to-Creatinine Ratio (UACR) and Health Care Resource Utilization (HRU) and Costs in Patients with Type 2 Diabetes (T2D) and CKD
Bibliographic record
Abstract
Background: UACR is an important measure of kidney damage, but the impact of changes in UACR on HRU and costs in patients with T2D and CKD is unclear. Methods: We used the Optum electronic health records database (01/2007-09/2021) to identify adult patients with albuminuria, measured by UACR ≥30 mg/g (initial test) after diagnosis of T2D and CKD. UACR change was categorized as increased (>30% change), stable (-30% to 30%), or decreased (<-30%) based on the percent change between the initial test and the last test (between 183-730 days after the initial test). Allcause inpatient (IP) admissions, emergency room (ER) visits, outpatient (OP) visits, and total medical costs were evaluated during the 1 year after the last test. The association of UACR change with HRU was evaluated using Poisson regression, adjusting for key baseline characteristics. Medical costs (2022 USD) were estimated using a unit cost approach based on HRU frequencies. Results: Among 144,814 patients eligible for the study, 81,084 (56%) had decreased, 31,766 (22%) had stable, and 31,964 (22%) had increased UACR. Compared with patients with stable UACR (IP admissions: 0.18 per-person-per-year [PPPY]; ER visits: 0.31 PPPY; OP visits: 19.13 PPPY; costs: $12,521), those with decreased UACR had similar HRU (IP: 0.17 PPPY; ER: 0.31 PPPY; OP: 19.90 PPPY) and annual medical costs ($12,329), while those with increased UACR had higher HRU (IP: 0.24 PPPY; ER: 0.35 PPPY; OP: 21.20 PPPY) and costs ($15,013). Compared with patients with stable UACR, those with decreased UACR had adjusted incidence rate ratios of 0.97 (95% CI: 0.93-1.01) for IP, 0.97 (0.94-1.01) for ER, and 1.02 (1.01-1.03) for OP. Patients with increased UACR had adjusted incidence rate ratios of 1.22 (1.17-1.28) for IP, 1.10 (1.05-1.15) for ER, and 1.07 (1.05-1.08) for OP compared with patients with stable UACR. Conclusions: Among patients with T2D and CKD who had albuminuria, increases in UACR were associated with higher HRU and costs compared to patients with stable UACR, while decreases in UACR were associated with similar HRU and costs. Mitigating increases in UACR could yield economic benefits for this patient population. Funding: Commercial Support - Bayer U.S. LLC
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 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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".