Consecutive First-Morning Urine Samples to Measure Change in the Albumin-to-Creatinine Ratio: A Pilot Study of a Home Urine Collection Protocol
Bibliographic record
Abstract
BACKGROUND: Multiple first-morning urine samples are recommended for measuring the urine albumin-to-creatinine ratio (ACR); however, this can be challenging in community-based research. METHODS: The objectives of the study are to pilot-test a home urine collection protocol and examine how the average and variance of ACR varied with the number of urine collections and time to laboratory analysis. This is a prospective observational pilot study. This study was conducted in London, Ontario, Canada at the London Health Sciences Centre (2012-2013). The patients were adults with chronic kidney disease (mean estimated glomerular filtration rate, 36 mL/min/1.73 m(2)). Participants collected a first-morning 20-mL urine sample on three consecutive days. This process was repeated after 3 months. Samples were picked up by hospital courier and analyzed for ACR on the same day; additional aliquots were analyzed after a delay of 24-48 h (stored at 4 °C) and 3-9 months (stored at -80 °C). The geometric mean of the percentage change in ACR between baseline and 3 months was calculated and compared between single samples and the average of two vs. three consecutive samples. RESULTS: Of 31 patients enrolled, 26 (83.9 %) submitted all six urine samples. The geometric mean of ACR for three consecutive samples at baseline was 87, 83, and 80 mg/mmol, and the corresponding percentage increase from baseline to 3 months was 15 % (95 % confidence interval (CI), -9 to 46 %), 33 % (95 % CI, 10 to 59 %), and 22 % (95 % CI, -6 to 57 %). Compared with single urine collections at baseline and follow-up, averaging ACR values from two consecutive first-morning urine samples improved the sample variance and reduced the required sample size to detect a given treatment effect by approximately 30 %. No further gain in statistical efficiency was achieved with three urine samples. Results were similar when the laboratory analysis was delayed by 24-48 h, but a delay of 3-9 months resulted in systematic overestimation of the ACR. Our study's generalizability is limited by its small sample size and reliance on a clinic-based population from a single urban center. CONCLUSIONS: We successfully used a home urine collection protocol to obtain multiple first-morning urine samples in patients with chronic kidney disease. Statistical efficiency was improved by averaging ACR values from two consecutive first-morning urine samples at baseline and follow-up.
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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 | 0.027 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".