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Record W3040786045 · doi:10.7326/m20-0529

Conversion of Urine Protein–Creatinine Ratio or Urine Dipstick Protein to Urine Albumin–Creatinine Ratio for Use in Chronic Kidney Disease Screening and Prognosis

2020· review· en· W3040786045 on OpenAlexafffund
Keiichi Sumida, Girish N. Nadkarni, Morgan E. Grams, Yingying Sang, Shoshana H. Ballew, Josef Coresh, Kunihiro Matsushita, Aditya Surapaneni, Nigel J. Brunskill, Steven J. Chadban, Alex R. Chang, Massimo Círillo, Kenn B. Daratha, Ron T. Gansevoort, Amit X. Garg, Licia Iacoviello, Takamasa Kayama, Tsuneo Konta, Csaba P. Kövesdy, James Lash, Brian J. Lee, Rupert Major, Marie Metzger, Katsuyuki Miura, David Naimark, Robert G. Nelson, Simon Sawhney, Nikita Stempniewicz, Mila Tang, Raymond R. Townsend, Jamie P. Traynor, José Manuel Valdivielso, Jack F.M. Wetzels, Kevan R. Polkinghorne, Hiddo J.L. Heerspink

Bibliographic record

VenueAnnals of Internal Medicine · 2020
Typereview
Languageen
FieldMedicine
TopicChronic Kidney Disease and Diabetes
Canadian institutionsUniversity of British ColumbiaSunnybrook HospitalUniversity of TorontoWestern University
FundersNational Institute of Diabetes and Digestive and Kidney DiseasesJohns Hopkins Bloomberg School of Public HealthPerelman School of Medicine, University of PennsylvaniaUniversité de Versailles Saint-Quentin-en-YvelinesRadboud Universitair Medisch CentrumUniversitair Medisch Centrum GroningenUniversity of Illinois at Urbana-ChampaignKidney Research UKMonash UniversityRadboud UniversiteitUniversity of PennsylvaniaUniversità degli Studi dell'InsubriaUniversity of AberdeenNational Institutes of HealthRijksuniversiteit GroningenUniversity of LeicesterUniversity of TorontoU.S. Department of Veterans AffairsUniversity Hospitals of Leicester NHS TrustJohns Hopkins UniversityInstitut National de la Santé et de la Recherche MédicaleUniversité Paris-SudKaiser PermanenteUniversità degli Studi di Napoli Federico IIGeorge Institute for Global HealthHealth Science Center, University of TennesseeNational Institute for Health and Care Research
KeywordsDipstickMedicineUrineCreatinineAlbuminKidney diseaseUrologyProteinuriaInternal medicineKidneyEndocrinology

Abstract

fetched live from OpenAlex

BACKGROUND: Although measuring albuminuria is the preferred method for defining and staging chronic kidney disease (CKD), total urine protein or dipstick protein is often measured instead. OBJECTIVE: To develop equations for converting urine protein-creatinine ratio (PCR) and dipstick protein to urine albumin-creatinine ratio (ACR) and to test their diagnostic accuracy in CKD screening and staging. DESIGN: Individual participant-based meta-analysis. SETTING: 12 research and 21 clinical cohorts. PARTICIPANTS: 919 383 adults with same-day measures of ACR and PCR or dipstick protein. MEASUREMENTS: Equations to convert urine PCR and dipstick protein to ACR were developed and tested for purposes of CKD screening (ACR ≥30 mg/g) and staging (stage A2: ACR of 30 to 299 mg/g; stage A3: ACR ≥300 mg/g). RESULTS: Median ACR was 14 mg/g (25th to 75th percentile of cohorts, 5 to 25 mg/g). The association between PCR and ACR was inconsistent for PCR values less than 50 mg/g. For higher PCR values, the PCR conversion equations demonstrated moderate sensitivity (91%, 75%, and 87%) and specificity (87%, 89%, and 98%) for screening (ACR >30 mg/g) and classification into stages A2 and A3, respectively. Urine dipstick categories of trace or greater, trace to +, and ++ for screening for ACR values greater than 30 mg/g and classification into stages A2 and A3, respectively, had moderate sensitivity (62%, 36%, and 78%) and high specificity (88%, 88%, and 98%). For individual risk prediction, the estimated 2-year 4-variable kidney failure risk equation using predicted ACR from PCR had discrimination similar to that of using observed ACR. LIMITATION: Diverse methods of ACR and PCR quantification were used; measurements were not always performed in the same urine sample. CONCLUSION: Urine ACR is the preferred measure of albuminuria; however, if ACR is not available, predicted ACR from PCR or urine dipstick protein may help in CKD screening, staging, and prognosis. PRIMARY FUNDING SOURCE: National Institute of Diabetes and Digestive and Kidney Diseases and National Kidney Foundation.

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)0.033
metaresearch head score (Gemma)0.069
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.033
Threshold uncertainty score0.176

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0330.069
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.006
Bibliometrics0.0050.006
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0020.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.001

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 head0.088
GPT teacher head0.372
Teacher spread0.284 · 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 designNot applicable
Domainnot available
GenreReview

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".

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Citations332
Published2020
Admission routes2
Has abstractyes

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