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Record W4403832544 · doi:10.1681/asn.2024fppch7dp

Accuracy of Smartphone-Enabled Urinary Albumin-to-Creatinine Ratio (uACR) Testing

2024· article· en· W4403832544 on OpenAlexaff
Danielle Jeddah, Nicholas Bevins, M Ronen, Ron Zohar, Navdeep Tangri

Bibliographic record

VenueJournal of the American Society of Nephrology · 2024
Typearticle
Languageen
FieldMedicine
TopicConsumer Attitudes and Food Labeling
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsCreatinineUrologyMedicineUrinary systemAlbuminInternal medicineComputer science

Abstract

fetched live from OpenAlex

Background: Urinary albumin to creatinine ratio (uACR) testing is crucial for diagnosing, staging, and managing chronic kidney disease (CKD), yet testing rates remain short of guideline recommendations. Guidelines recommend semi-quantitative point-of-care testing (POCT) with ≥85% sensitivity when lab access is limited or when POCT offers advantages like convenience, immediate results, and elimination of sample transportation. We evaluated the accuracy of the Minuteful-Kidney Test (MKT), an FDA-cleared, smartphone-enabled, semi-quantitative test of uACR for home use, improving accessibility to kidney health assessment Methods: We conducted a diagnostic study comparing the Minuteful-Kidney Test to the Beckman Coulter AU 480 Analyzer, a gold standard laboratory method, in a population at risk for kidney disease. From January to March 2024, we analyzed 615 urine samples in an accredited U.S. laboratory. Inclusion criteria were medical conditions or risk factors for kidney damage based on EMR data. Exclusion criteria were extreme urine pH, diluted samples, and samples with preservatives. Sensitivity, specificity, PPV, and NPV were calculated Results: Among 615 samples, 60 met exclusion criteria, leaving 555 for analysis. The Minuteful-Kidney Test showed a sensitivity of 96.2% (95% CI 94.2%-98.2%) and specificity of 84.2% (95% CI 80.7%-87.7%), with an NPV of 98.6% (95% CI 97.4%-99.8%) and PPV of 66.8% (95% CI 60.3%-73.3%). Albuminuria was identified in 24.9% (95% CI 21.3-28.5) of the samples based on the quantitative device. The test accurately classified 85.6% of samples into KDIGO albuminuria categories, identifying all 25 (100%) laboratory-confirmed A3 samples as abnormal Conclusion: The Minuteful-Kidney Test demonstrated high sensitivity (96.2%), exceeding the guideline-required 85%, and robust specificity for detecting albuminuria in high-risk CKD populations. The device can streamline CKD screening and enable early intervention, for patients with or at risk for CKD. Funding: Private Foundation Support Comparative Analysis of Minuteful - Kidney Test and Quantitative Analyzer for Albuminuria Detection - Beckman Coulter AU 480 Analyzer Normoalbuminuria Albuminuria Total Minuteful - Kidney Test (A1) uACR ≤30 mg/g (A2) uACR 30-300 mg/g (A3) uACR ≥300 mg/g (A1) uACR ≤30 mg/g 351 5 356 NPV 98.6% (A2) uACR 30-300 mg/g 65 102 3 170 PPV 66.8% (A3) uACR ≥300 mg/g 1 6 22 29 Grand Total (n) 417 113 25 555 Positivity Rate 24.9% Specificity 84.2% Sensitivity 96.2%

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 imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.029
GPT teacher head0.318
Teacher spread0.289 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2024
Admission routes1
Has abstractyes

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