Keiichi Sumida1, Girish N Nadkarni2, Morgan E Grams3, Yingying Sang3, Shoshana H Ballew3, Josef Coresh3, Kunihiro Matsushita3, Aditya Surapaneni3, Nigel Brunskill4, Steve J Chadban5, Alex R Chang6, Massimo Cirillo7, Kenn B Daratha8, Ron T Gansevoort9, Amit X Garg10, Licia Iacoviello11, Takamasa Kayama12, Tsuneo Konta12, Csaba P Kovesdy13, James Lash14, Brian J Lee15, Rupert W Major4, Marie Metzger16, Katsuyuki Miura17, David M J Naimark18, Robert G Nelson19, Simon Sawhney20, Nikita Stempniewicz21, Mila Tang22, Raymond R Townsend23, Jamie P Traynor24, José M Valdivielso25, Jack Wetzels26, Kevan R Polkinghorne27, Hiddo J L Heerspink28. 1. University of Tennessee Health Science Center, Memphis, Tennessee (K.S.). 2. Icahn School of Medicine at Mount Sinai, New York, New York (G.N.N.). 3. Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland (M.E.G., Y.S., S.H.B., J.C., K.M., A.S.). 4. Leicester General Hospital, University Hospitals of Leicester NHS Trust, and University of Leicester, Leicester, United Kingdom (N.B., R.W.M.). 5. Royal Prince Alfred Hospital and Kidney Node, University of Sydney, Sydney, New South Wales, Australia (S.J.C.). 6. Geisinger Health, Danville, Pennsylvania (A.R.C.). 7. University of Naples "Federico II," Naples, Italy (M.C.). 8. Providence Sacred Heart Medical Center and Gonzaga University School of Anesthesia, Spokane, Washington (K.B.D.). 9. University Medical Center Groningen, University of Groningen, Groningen, the Netherlands (R.T.G.). 10. ICES and Western University, London, Ontario, Canada (A.X.G.). 11. IRCCS Neuromed, Pozzilli, Italy, and University of Insubria, Varese, Italy (L.I.). 12. Yamagata University, Yamagata, Japan (T.K., T.K.). 13. Memphis Veterans Affairs Medical Center and University of Tennessee Health Science Center, Memphis, Tennessee (C.P.K.). 14. University of Illinois at Chicago, Chicago, Illinois (J.L.). 15. Kaiser Permanente, Hawaii Region, and Moanalua Medical Center, Honolulu, Hawaii (B.J.L.). 16. Paris Saclay University, Paris-Sud University, UVSQ, CESP, INSERM U1018, Villejuif, France (M.M.). 17. Shiga University of Medical Science Seta-Tsukinowa-cho, Shiga, Japan (K.M.). 18. Sunnybrook Hospital, University of Toronto, Toronto, Ontario, Canada (D.M.N.). 19. National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Phoenix, Arizona (R.G.N.). 20. University of Aberdeen, Aberdeen, Scotland (S.S.). 21. American Medical Group Association, Alexandria, Virginia (N.S.). 22. University of British Columbia, Vancouver, British Columbia, Canada (M.T.). 23. Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania (R.R.T.). 24. Queen Elizabeth University Hospital, Glasgow, Scotland (J.P.T.). 25. Institute of Biomedical Research of Lleida and Spanish Research Network for Renal Diseases, Lleida, Spain (J.M.V.). 26. Radboud University Medical Center, Nijmegen, the Netherlands (J.W.). 27. Monash University, Clayton, Victoria, Australia (K.R.P.). 28. University of Groningen, University Medical Center, Groningen, the Netherlands, and The George Institute for Global Health, Sydney, New South Wales, Australia (H.J.H.).
Abstract
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.
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.
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Authors: Colleen M Shannon; Shoshana H Ballew; Natalie Daya; Linda Zhou; Alex R Chang; Yingying Sang; Josef Coresh; Elizabeth Selvin; Morgan E Grams Journal: J Am Geriatr Soc Date: 2021-07-23 Impact factor: 7.538