Diagnostic Accuracy of Smartphone-Enabled Urinary Albumin-to-Creatinine Ratio Testing
Bibliographic record
Abstract
BACKGROUND: Albuminuria is an important biomarker for detecting chronic kidney disease (CKD), particularly in at-risk individuals. Advances in diagnostic technology have introduced home-use, semiquantitative testing methods such as the FDA-cleared Minuteful Kidney Test (MKT). This study compares the diagnostic accuracy of the MKT to the Beckman Coulter AU 480 analyzer, a standard laboratory quantitative method, for detecting albuminuria in individuals with or at risk for CKD. METHODS: Urine samples were obtained from individuals with risk factors for kidney damage. Each sample was analyzed for the urinary albumin-to-creatinine ratio (uACR) using both the MKT and the laboratory standard method. Albuminuria was classified according to KDIGO guidelines: A1 (uACR <30 mg/g, "normal to mildly increased"), A2 (uACR 30-300 mg/g, "moderately increased"), and A3 (uACR >300 mg/g, "severely increased"). Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of MKT were calculated against the laboratory standard. RESULTS: Out of 615 collected urine samples, 555 met inclusion criteria. Albuminuria (uACR >30 mg/g) was detected in 24.9% of samples using the laboratory method (95% CI: 21.3%-28.5%). The MKT demonstrated a sensitivity of 96.4%, specificity of 84.2%, NPV of 98.6%, and PPV of 66.8%. MKT accurately categorized 85.6% of samples into the KDIGO albuminuria categories A1, A2, and A3, identifying 100% of laboratory-confirmed A3 samples as abnormal. CONCLUSIONS: The MKT shows high sensitivity, exceeding the guideline-required threshold (>85%, P < 0.001), and acceptable specificity for detecting albuminuria in high-risk populations. This device offers the potential to improve CKD detection and management and facilitate early intervention in primary care and community settings, where accurate rule-out capabilities are essential.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".