Optimal Frequency of Urinary Albumin Screening in Type 1 Diabetes
Bibliographic record
Abstract
OBJECTIVE: Kidney disease screening recommendations include annual urine testing for albuminuria after 5 years' duration of type 1 diabetes. We aimed to determine a simple, risk factor-based screening schedule that optimizes early detection and testing frequency. RESEARCH DESIGN AND METHODS: Urinary albumin excretion measurements from 1,343 participants in the Diabetes Control and Complications Trial and its long-term follow-up were used to create piecewise-exponential incidence models assuming 6-month constant hazards. Likelihood of the onset of moderately or severely elevated albuminuria (confirmed albumin excretion rate AER ≥30 or ≥300 mg/24 h, respectively) and its risk factors were used to identify individualized screening schedules. Time with undetected albuminuria and number of tests were compared with annual screening. RESULTS: The 3-year cumulative incidence of elevated albuminuria following normoalbuminuria at any time during the study was 3.2%, which was strongly associated with higher glycated hemoglobin (HbA1c) and AER. Personalized screening in 2 years for those with current AER ≤10 mg/24 h and HbA1c ≤8% (low risk [0.6% three-year cumulative incidence]), in 6 months for those with AER 21-30 mg/24 h or HbA1c ≥9% (high risk [8.9% three-year cumulative incidence]), and in 1 year for all others (average risk [2.4% three-year cumulative incidence]) was associated with 34.9% reduction in time with undetected albuminuria and 20.4% reduction in testing frequency as compared with annual screening. Stratification by categories of HbA1c or AER alone was associated with reductions of lesser magnitude. CONCLUSIONS: A personalized alternative to annual screening in type 1 diabetes can substantially reduce both the time with undetected kidney disease and the frequency of urine testing. ARTICLE HIGHLIGHTS: Kidney disease screening recommendations include annual urine testing for albuminuria after 5 years' duration of type 1 diabetes. We investigated simple screening schedules that optimize early detection and testing frequency. Personalized screening in 2 years for those with current AER ≤10 mg/24 h and HbA1c ≤8%, in 6 months for those with AER 21-30 mg/24 h or HbA1c ≥9%, and in 1 year for all others yielded 34.9% reduction in time with undetected albuminuria and 20.4% fewer evaluations compared with annual screening. A personalized alternative to annual screening in type 1 diabetes can substantially reduce both the time with undetected kidney disease and the frequency of urine testing.
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 | 0.002 | 0.008 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".