From Proteinuria to Albuminuria: Great Expectations for Kidney Failure Risk Prediction
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Abstract
Editorials15 September 2020From Proteinuria to Albuminuria: Great Expectations for Kidney Failure Risk PredictionTyrone G. Harrison, MD and Brenda R. Hemmelgarn, MD, PhDTyrone G. Harrison, MDUniversity of Calgary, Calgary, Alberta, Canada (T.G.H.)Search for more papers by this author and Brenda R. Hemmelgarn, MD, PhDUniversity of Calgary and University of Alberta, Calgary and Edmonton, Alberta, Canada (B.R.H.)Search for more papers by this authorAuthor, Article, and Disclosure Informationhttps://doi.org/10.7326/M20-4211 SectionsAboutFull TextPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinkedInRedditEmail Prediction begets prediction. In their current report for Annals, Sumida and colleagues (1) present important work to understand the relationship between urine protein and albumin. Albuminuria, a marker of kidney disease, is associated with future risk for death, kidney failure, and cardiovascular events (2, 3). The strength of evidence has led to the urine albumin–creatinine ratio (ACR), used to diagnose and stage kidney disease and to predict kidney failure risk (4, 5). The kidney failure risk equation (KFRE) is a validated tool that incorporates ACR, sex, age, and estimated glomerular filtration rate to predict kidney failure (6). Yet, how can ...References1. Sumida K, Nadkarni GN, Grams ME, et al; Chronic Kidney Disease Prognosis Consortium. Conversion of urine protein–creatinine ratio or urine dipstick protein to urine albumin–creatinine ratio for use in chronic kidney disease screening and prognosis. An individual participant–based meta-analysis. Ann Intern Med. 2020;173:426-35. doi:10.7326/M20-0529 LinkGoogle Scholar2. Matsushita K, van der Velde M, Astor BC, et al; Chronic Kidney Disease Prognosis Consortium. Association of estimated glomerular filtration rate and albuminuria with all-cause and cardiovascular mortality in general population cohorts: a collaborative meta-analysis. Lancet. 2010;375:2073-81. [PMID: 20483451] doi:10.1016/S0140-6736(10)60674-5 CrossrefMedlineGoogle Scholar3. Hemmelgarn BR, Manns BJ, Lloyd A, et al; Alberta Kidney Disease Network. Relation between kidney function, proteinuria, and adverse outcomes. JAMA. 2010;303:423-9. [PMID: 20124537] doi:10.1001/jama.2010.39 CrossrefMedlineGoogle Scholar4. Levin A, Stevens PE, Bilous RW, et al; Kidney Disease: Improving Global Outcomes (KDIGO) CKD Work Group. KDIGO 2012 clinical practice guideline for the evaluation and management of chronic kidney disease. Kidney Int Suppl. 2013;3:1-150. doi:10.1038/kisup.2012.73 CrossrefGoogle Scholar5. Tangri N, Stevens LA, Griffith J, et al. A predictive model for progression of chronic kidney disease to kidney failure. JAMA. 2011;305:1553-9. [PMID: 21482743] doi:10.1001/jama.2011.451 CrossrefMedlineGoogle Scholar6. Tangri N, Grams ME, Levey AS, et al; CKD Prognosis Consortium. Multinational assessment of accuracy of equations for predicting risk of kidney failure: a meta-analysis. JAMA. 2016;315:164-74. [PMID: 26757465] doi:10.1001/jama.2015.18202 CrossrefMedlineGoogle Scholar7. Weaver RG, James MT, Ravani P, et al. Estimating urine albumin-to-creatinine ratio from protein-to-creatinine ratio: development of equations using same-day measurements. J Am Soc Nephrol. 2020;31:591-601. [PMID: 32024663] doi:10.1681/ASN.2019060605 CrossrefMedlineGoogle Scholar Author, Article, and Disclosure InformationAffiliations: University of Calgary, Calgary, Alberta, Canada (T.G.H.)University of Calgary and University of Alberta, Calgary and Edmonton, Alberta, Canada (B.R.H.)Disclosures: Authors have disclosed no conflicts of interest. Forms can be viewed at www.acponline.org/authors/icmje/ConflictOfInterestForms.do?msNum=M20-4211.Corresponding Author: Brenda R. Hemmelgarn, MD, PhD, 2J2.01 Walter C. Mackenzie Health Sciences Centre, Univer-sity of Alberta, Edmonton, AB T6G 2R7, Canada; e-mail, Brenda.[email protected]ca.Current Author Addresses: Dr. Harrison: University of Calgary, Health Sciences Centre, 3330 Hospital Drive, Calgary, AB T2N 4N1, Canada.Dr. Hemmelgam: 2J2.01 Walter C. Mackenzie Health Sciences Centre, University of Alberta, Edmonton, AB T6G 2R7, Canada.This article was published at Annals.org on 14 July 2020. PreviousarticleNextarticle Advertisement FiguresReferencesRelatedDetailsSee AlsoConversion of Urine Protein–Creatinine Ratio or Urine Dipstick Protein to Urine Albumin–Creatinine Ratio for Use in Chronic Kidney Disease Screening and Prognosis Keiichi Sumida , Girish N. Nadkarni , Morgan E. Grams , Yingying Sang , Shoshana H. Ballew , Josef Coresh , Kunihiro Matsushita , Aditya Surapaneni , Nigel Brunskill , Steve J. Chadban , Alex R. Chang , Massimo Cirillo , Kenn B. Daratha , Ron T. Gansevoort , Amit X. Garg , Licia Iacoviello , Takamasa Kayama , Tsuneo Konta , Csaba P. Kovesdy , James Lash , Brian J. Lee , Rupert W. Major , Marie Metzger , Katsuyuki Miura , David M.J. Naimark , Robert G. Nelson , Simon Sawhney , Nikita Stempniewicz , Mila Tang , Raymond R. Townsend , Jamie P. Traynor , José M. Valdivielso , Jack Wetzels , Kevan R. Polkinghorne , Hiddo J.L. Heerspink , and Metrics Cited byLycopene-Loaded Bilosomes Ameliorate High-Fat Diet-Induced Chronic Nephritis in Mice through the TLR4/MyD88 Inflammatory PathwayPrevalence, recognition and management of chronic kidney disease in Japan: population-based estimate using a healthcare database with routine health checkup data 15 September 2020Volume 173, Issue 6Page: 492-493KeywordsAlbuminsChronic kidney diseaseCohort studiesHealth careHypertensionProteinsProteinuriaRenal diseasesRenal failureUrine ePublished: 14 July 2020 Issue Published: 15 September 2020 Copyright & PermissionsCopyright © 2020 by American College of Physicians. 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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 | 0.009 | 0.046 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.009 | 0.013 |
| Insufficient payload (model declined to judge) | 0.036 | 0.020 |
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".