Classification of Chronic Kidney Disease: A Step Forward
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Abstract
Editorials4 January 2011Classification of Chronic Kidney Disease: A Step ForwardAndrew S. Levey, MD, Navdeep Tangri, MD, and Lesley A. Stevens, MD, MSAndrew S. Levey, MDFrom Tufts Medical Center, Boston, MA 02111., Navdeep Tangri, MDFrom Tufts Medical Center, Boston, MA 02111., and Lesley A. Stevens, MD, MSFrom Tufts Medical Center, Boston, MA 02111.Author, Article, and Disclosure Informationhttps://doi.org/10.7326/0003-4819-154-1-201101040-00012 SectionsAboutFull TextPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinkedInRedditEmail Chronic kidney disease (CKD) is common and harmful but treatable, and it is recognized as a worldwide public health problem (1, 2). Approximately 23 million U.S. adults have CKD, for a prevalence of 11.5% (3). Kidney failure and cardiovascular disease (CVD) are generally considered to be the most important outcomes, but the risks for each outcome vary widely among patients, and clinicians need guidance to prioritize their clinical decisions."Chronic kidney disease" is a general term for heterogeneous disorders of kidney structure and function. The National Kidney Foundation Kidney Disease Outcomes Quality Initiative (KDOQI) guidelines (4) define CKD as follows, ...References1. Levey AS, Andreoli SP, DuBose T, Provenzano R, Collins AJ. CKD: common, harmful, and treatable—World Kidney Day 2007. Am J Kidney Dis. 2007;49:175-9. [PMID: 17261418] CrossrefMedlineGoogle Scholar2. Levey AS, Atkins R, Coresh J, Cohen EP, Collins AJ, Eckardt KU, et al. Chronic kidney disease as a global public health problem: approaches and initiatives - a position statement from Kidney Disease Improving Global Outcomes. Kidney Int. 2007;72:247-59. [PMID: 17568785] CrossrefMedlineGoogle Scholar3. Levey AS, Stevens LA, Schmid CH, Zhang YL, Castro AF, Feldman HI, et al; CKD-EPI (Chronic Kidney Disease Epidemiology Collaboration). A new equation to estimate glomerular filtration rate. Ann Intern Med. 2009;150:604-12. [PMID: 19414839] LinkGoogle Scholar4. Levey AS, Coresh J, Balk E, Kausz AT, Levin A, Steffes MW, et al; National Kidney Foundation. National Kidney Foundation practice guidelines for chronic kidney disease: evaluation, classification, and stratification. Ann Intern Med. 2003;139:137-47. [PMID: 12859163] LinkGoogle Scholar5. Iseki K, Kinjo K, Iseki C, Takishita S. Relationship between predicted creatinine clearance and proteinuria and the risk of developing ESRD in Okinawa, Japan. Am J Kidney Dis. 2004;44:806-14. [PMID: 15492946] CrossrefMedlineGoogle Scholar6. Ishani A, Grandits GA, Grimm RH, Svendsen KH, Collins AJ, Prineas RJ, et al. Association of single measurements of dipstick proteinuria, estimated glomerular filtration rate, and hematocrit with 25-year incidence of end-stage renal disease in the multiple risk factor intervention trial. J Am Soc Nephrol. 2006;17:1444-52. [PMID: 16611715] CrossrefMedlineGoogle Scholar7. Foster MC, Hwang SJ, Larson MG, Parikh NI, Meigs JB, Vasan RS, et al. Cross-classification of microalbuminuria and reduced glomerular filtration rate: associations between cardiovascular disease risk factors and clinical outcomes. Arch Intern Med. 2007;167:1386-92. [PMID: 17620532] CrossrefMedlineGoogle Scholar8. Hallan S, Astor B, Romundstad S, Aasarød K, Kvenild K, Coresh J. Association of kidney function and albuminuria with cardiovascular mortality in older vs younger individuals: The HUNT II Study. Arch Intern Med. 2007;167:2490-6. [PMID: 18071172] CrossrefMedlineGoogle Scholar9. Astor BC, Hallan SI, Miller ER, Yeung E, Coresh J. Glomerular filtration rate, albuminuria, and risk of cardiovascular and all-cause mortality in the US population. Am J Epidemiol. 2008;167:1226-34. [PMID: 18385206] CrossrefMedlineGoogle Scholar10. Brantsma AH, Bakker SJ, Hillege HL, de Zeeuw D, de Jong PE, Gansevoort RT; PREVEND Study Group. Cardiovascular and renal outcome in subjects with K/DOQI stage 1-3 chronic kidney disease: the importance of urinary albumin excretion. Nephrol Dial Transplant. 2008;23:3851-8. [PMID: 18641082] CrossrefMedlineGoogle Scholar11. Hemmelgarn BR, Manns BJ, Lloyd A, James MT, Klarenbach S, Quinn RR, et al; Alberta Kidney Disease Network. Relation between kidney function, proteinuria, and adverse outcomes. JAMA. 2010;303:423-9. [PMID: 20124537] CrossrefMedlineGoogle Scholar12. Matsushita K, van der Velde M, Astor BC, Woodward M, Levey AS, deJong PE, 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] CrossrefMedlineGoogle Scholar13. Levey AS, de Jong PE, Coresh J, El Nahas M, Astor BC, Matsushita K, et al. The definition, classification and prognosis of chronic kidney disease: a KDIGO Controversies Conference report. Kidney Int. 8 Dec 2010. [Epub ahead of print]. Google Scholar14. Tonelli M, Muntner P, Lloyd A, Manns BJ, James MT, Klarenbach S, et al; Alberta Kidney Disease Network. Using proteinuria and estimated glomerular filtration rate to classify risk in patients with chronic kidney disease. A cohort study. Ann Intern Med. 2011;154:12-21. LinkGoogle Scholar15. Stevens LA, Levey AS. Impact of reporting estimated glomerular filtration rate: it's not just about us. Kidney Int. 2009;76:245-7. [PMID: 19904255] CrossrefMedlineGoogle Scholar16. James MT, Quan H, Tonelli M, Manns BJ, Faris P, Laupland KB, et al; Alberta Kidney Disease Network. CKD and risk of hospitalization and death with pneumonia. Am J Kidney Dis. 2009;54:24-32. [PMID: 19447535] CrossrefMedlineGoogle Scholar17. Hailpern SM, Melamed ML, Cohen HW, Hostetter TH. Moderate chronic kidney disease and cognitive function in adults 20 to 59 years of age: Third National Health and Nutrition Examination Survey (NHANES III). J Am Soc Nephrol. 2007;18:2205-13. [PMID: 17554148] CrossrefMedlineGoogle Scholar18. Wilhelm-Leen ER, Hall YN, K Tamura M, Chertow GM. Frailty and chronic kidney disease: the Third National Health and Nutrition Evaluation Survey. Am J Med. 2009;122:664-71.e2. [PMID: 19559169] CrossrefMedlineGoogle Scholar19. Fink JC, Brown J, Hsu VD, Seliger SL, Walker L, Zhan M. CKD as an underrecognized threat to patient safety. Am J Kidney Dis. 2009;53:681-8. [PMID: 19246142] CrossrefMedlineGoogle Scholar20. National Cholesterol Education Program. ATP III Guidelines At-A-Glance Quick Desk Reference. NIH Publication No. 01-3305. Bethesda, MD: National Heart, Lung, and Blood Institute; 2001. Accessed at www.nhlbi.nih.gov/guidelines/cholesterol/atglance.pdf on 16 November 2010. Google Scholar Author, Article, and Disclosure InformationAffiliations: From Tufts Medical Center, Boston, MA 02111.Disclosures: Disclosures can be viewed at www.acponline.org/authors/icmje/ConflictOfInterestForms.do?msNum=M10-2555.Corresponding Author: Andrew S. Levey, MD, Tufts Medical Center, Box 391, 800 Washington Street, Boston, MA 02111.Current Author Addresses: Drs. Levey, Tangri, and Stevens: Tufts Medical Center, Box 391, 800 Washington Street, Boston, MA 02111. PreviousarticleNextarticle Advertisement FiguresReferencesRelatedDetailsSee AlsoUsing Proteinuria and Estimated Glomerular Filtration Rate to Classify Risk in Patients With Chronic Kidney Disease Marcello Tonelli , Paul Muntner , Anita Lloyd , Braden J. Manns , Matthew T. James , Scott Klarenbach , Robert R. Quinn , Natasha Wiebe , Brenda R. Hemmelgarn , and Metrics Cited ByWhat are the factors affecting the progression of kidney failure, mortality and morbidity after cardiac surgery in patients with chronic kidney disease?Burden of Chronic Kidney Disease by KDIGO Categories of Glomerular Filtration Rate and Albuminuria: A Systematic ReviewComparison and development of machine learning tools in the prediction of chronic kidney disease progressionPrecision Medicine Approaches to Diabetic Kidney Disease: Tissue as an IssuePrevalence of isolated non-albumin proteinuria in the US population tested for both, urine total protein and urine albumin: An unexpected discoveryValidation of the Kidney Failure Risk Equation in ManitobaEffect of Sitagliptin on Kidney Function and Respective Cardiovascular Outcomes in Type 2 Diabetes: Outcomes From TECOSChildhood Albuminuria and Chronic Kidney Disease is Associated with Mortality and End-Stage Renal DiseaseEndocan as a potential diagnostic or prognostic biomarker for chronic kidney diseaseMicroalbuminuria as a Risk Predictor in Diabetes: The Continuing SagaComparison of Associations of Outcomes After Stroke With Estimated GFR Using Chinese Modifications of the MDRD Study and CKD-EPI Creatinine Equations: Results From the China National Stroke RegistryDistribution of cardiovascular disease and retinopathy in patients with type 2 diabetes according to different classification systems for chronic kidney disease: a cross-sectional analysis of the renal insufficiency and cardiovascular events (RIACE) Italian multicenter studyAlbuminuria and renal function as predictors of cardiovascular events and mortality in a general population of patients with type 2 diabetes: A nationwide observational study from the Swedish National Diabetes RegisterThe Prevalence of Chronic Kidney Disease in a Primary Care Setting: A Swiss Cross-Sectional StudyThe kidney failure risk equation: on the road to being clinically useful?Risk Prediction Models for Patients With Chronic Kidney Disease A Systematic ReviewNavdeep Tangri, MD, PhD, Georgios D. Kitsios, MD, PhD, MS, Lesley Ann Inker, MD, MS, John Griffith, PhD, David M. Naimark, MD, MSc, Simon Walker, BSc(Hons), Claudio Rigatto, MD, MSc, Katrin Uhlig, MD, MS, David M. Kent, MD, MS, and Andrew S. Levey, MDExercise Training Improves HR Responses and V˙O2peak in Predialysis Kidney PatientsRisk prediction in chronic kidney diseaseModel-based physiological data stream evaluation for dialysis therapyDiverging Association of Reduced Glomerular Filtration Rate and Albuminuria With Coronary and Noncoronary Events in Patients With Type 2 Diabetes: The Renal Insufficiency And Cardiovascular Events (RIACE) Italian Multicenter StudyUrine Dipstick to Detect Trace Proteinuria: An Underused Tool for an Underappreciated Risk MarkerRenal disease in HIV-infected individuals 4 January 2011Volume 154, Issue 1Page: 65-67KeywordsCardiovascular diseasesChronic kidney diseaseMedical dialysisMedical risk factorsProteinuriaRenal diseasesRenal failureRenal transplantationTransplantation ePublished: 4 January 2011 Issue Published: 4 January 2011 CopyrightCopyright © 2011 by American College of Physicians. All Rights Reserved.PDF DownloadLoading ...
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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 | 0.019 | 0.068 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.007 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.009 | 0.026 |
| Insufficient payload (model declined to judge) | 0.012 | 0.010 |
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".