Christina M Parrinello1, Morgan E Grams2, David Couper3, Christie M Ballantyne4, Ron C Hoogeveen4, John H Eckfeldt5, Elizabeth Selvin6, Josef Coresh7. 1. Department of Epidemiology and the Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD; 2. Department of Epidemiology and the Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD; Division of Nephrology and. 3. Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC; 4. Division of Atherosclerosis and Vascular Medicine, Department of Medicine, Baylor College of Medicine, Houston, TX; 5. Department of Laboratory Medicine and Pathology, University of Minnesota, Minneapolis, MN. 6. Department of Epidemiology and the Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD; Division of General Internal Medicine, Department of Medicine, Johns Hopkins University, Baltimore, MD; 7. Department of Epidemiology and the Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD; Division of General Internal Medicine, Department of Medicine, Johns Hopkins University, Baltimore, MD; coresh@jhu.edu.
Abstract
BACKGROUND: Equivalence of laboratory tests over time is important for longitudinal studies. Even a small systematic difference (bias) can result in substantial misclassification. METHODS: We selected 200 Atherosclerosis Risk in Communities Study participants attending all 5 study visits over 25 years. Eight analytes were remeasured in 2011-2013 from stored blood samples from multiple visits: creatinine, uric acid, glucose, total cholesterol, HDL cholesterol, LDL cholesterol, triglycerides, and high-sensitivity C-reactive protein. Original values were recalibrated to remeasured values with Deming regression. Differences >10% were considered to reflect substantial bias, and correction equations were applied to affected analytes in the total study population. We examined trends in chronic kidney disease (CKD) pre- and postrecalibration. RESULTS: Repeat measures were highly correlated with original values [Pearson r > 0.85 after removing outliers (median 4.5% of paired measurements)], but 2 of 8 analytes (creatinine and uric acid) had differences >10%. Original values of creatinine and uric acid were recalibrated to current values with correction equations. CKD prevalence differed substantially after recalibration of creatinine (visits 1, 2, 4, and 5 prerecalibration: 21.7%, 36.1%, 3.5%, and 29.4%, respectively; postrecalibration: 1.3%, 2.2%, 6.4%, and 29.4%). For HDL cholesterol, the current direct enzymatic method differed substantially from magnesium dextran precipitation used during visits 1-4. CONCLUSIONS: Analytes remeasured in samples stored for approximately 25 years were highly correlated with original values, but 2 of the 8 analytes showed substantial bias at multiple visits. Laboratory recalibration improved reproducibility of test results across visits and resulted in substantial differences in CKD prevalence. We demonstrate the importance of consistent recalibration of laboratory assays in a cohort study.
BACKGROUND: Equivalence of laboratory tests over time is important for longitudinal studies. Even a small systematic difference (bias) can result in substantial misclassification. METHODS: We selected 200 Atherosclerosis Risk in Communities Study participants attending all 5 study visits over 25 years. Eight analytes were remeasured in 2011-2013 from stored blood samples from multiple visits: creatinine, uric acid, glucose, total cholesterol, HDL cholesterol, LDL cholesterol, triglycerides, and high-sensitivity C-reactive protein. Original values were recalibrated to remeasured values with Deming regression. Differences >10% were considered to reflect substantial bias, and correction equations were applied to affected analytes in the total study population. We examined trends in chronic kidney disease (CKD) pre- and postrecalibration. RESULTS: Repeat measures were highly correlated with original values [Pearson r > 0.85 after removing outliers (median 4.5% of paired measurements)], but 2 of 8 analytes (creatinine and uric acid) had differences >10%. Original values of creatinine and uric acid were recalibrated to current values with correction equations. CKD prevalence differed substantially after recalibration of creatinine (visits 1, 2, 4, and 5 prerecalibration: 21.7%, 36.1%, 3.5%, and 29.4%, respectively; postrecalibration: 1.3%, 2.2%, 6.4%, and 29.4%). For HDL cholesterol, the current direct enzymatic method differed substantially from magnesium dextran precipitation used during visits 1-4. CONCLUSIONS: Analytes remeasured in samples stored for approximately 25 years were highly correlated with original values, but 2 of the 8 analytes showed substantial bias at multiple visits. Laboratory recalibration improved reproducibility of test results across visits and resulted in substantial differences in CKD prevalence. We demonstrate the importance of consistent recalibration of laboratory assays in a cohort study.
Authors: Andrew S Levey; Josef Coresh; Tom Greene; Jane Marsh; Lesley A Stevens; John W Kusek; Frederick Van Lente Journal: Clin Chem Date: 2007-03-01 Impact factor: 8.327
Authors: Gary L Myers; W Greg Miller; Josef Coresh; James Fleming; Neil Greenberg; Tom Greene; Thomas Hostetter; Andrew S Levey; Mauro Panteghini; Michael Welch; John H Eckfeldt Journal: Clin Chem Date: 2005-12-06 Impact factor: 8.327
Authors: Josef Coresh; Brad C Astor; Geraldine McQuillan; John Kusek; Tom Greene; Frederick Van Lente; Andrew S Levey Journal: Am J Kidney Dis Date: 2002-05 Impact factor: 8.860
Authors: Lesley A Stevens; Jane Manzi; Andrew S Levey; Jing Chen; Amy E Deysher; Tom Greene; Emilio D Poggio; Christopher H Schmid; Michael W Steffes; Yaping Lucy Zhang; Frederick Van Lente; Josef Coresh Journal: Am J Kidney Dis Date: 2007-07 Impact factor: 8.860
Authors: Andrew S Levey; Lesley A Stevens; Christopher H Schmid; Yaping Lucy Zhang; Alejandro F Castro; Harold I Feldman; John W Kusek; Paul Eggers; Frederick Van Lente; Tom Greene; Josef Coresh Journal: Ann Intern Med Date: 2009-05-05 Impact factor: 25.391
Authors: John W McEvoy; Yuan Chen; Chiadi E Ndumele; Scott D Solomon; Vijay Nambi; Christie M Ballantyne; Roger S Blumenthal; Josef Coresh; Elizabeth Selvin Journal: JAMA Cardiol Date: 2016-08-01 Impact factor: 14.676
Authors: Anna Fretz; Andrea L C Schneider; John W McEvoy; Ron Hoogeveen; Christie M Ballantyne; Josef Coresh; Elizabeth Selvin Journal: Am J Epidemiol Date: 2016-02-08 Impact factor: 4.897
Authors: Keenan A Walker; B Gwen Windham; Melinda C Power; Ron C Hoogeveen; Aaron R Folsom; Christie M Ballantyne; David S Knopman; Elizabeth Selvin; Clifford R Jack; Rebecca F Gottesman Journal: Neurobiol Aging Date: 2018-04-04 Impact factor: 4.673
Authors: Keenan A Walker; B Gwen Windham; Charles H Brown; David S Knopman; Clifford R Jack; Thomas H Mosley; Elizabeth Selvin; Dean F Wong; Timothy M Hughes; Yun Zhou; Alden L Gross; Rebecca F Gottesman Journal: J Alzheimers Dis Date: 2018 Impact factor: 4.472
Authors: Bernhard Haring; Elizabeth Selvin; Menglu Liang; Josef Coresh; Morgan E Grams; Natalia Petruski-Ivleva; Lyn M Steffen; Casey M Rebholz Journal: J Ren Nutr Date: 2017-01-05 Impact factor: 3.655
Authors: Morgan E Grams; Casey M Rebholz; Yuan Chen; Andreea M Rawlings; Michelle M Estrella; Elizabeth Selvin; Lawrence J Appel; Adrienne Tin; Josef Coresh Journal: J Am Soc Nephrol Date: 2016-03-10 Impact factor: 10.121
Authors: Johannes B Scheppach; Josef Coresh; Aozhou Wu; Rebecca F Gottesman; Thomas H Mosley; David S Knopman; Morgan E Grams; A Richey Sharrett; Silvia Koton Journal: Am J Kidney Dis Date: 2020-05-16 Impact factor: 8.860
Authors: Menglu Liang; John William McEvoy; Yuan Chen; A Richey Sharrett; Elizabeth Selvin Journal: Diabetes Care Date: 2016-08-01 Impact factor: 19.112
Authors: Casey M Rebholz; Elizabeth Selvin; Menglu Liang; Christie M Ballantyne; Ron C Hoogeveen; David Aguilar; John W McEvoy; Morgan E Grams; Josef Coresh Journal: Kidney Int Date: 2017-08-31 Impact factor: 10.612
Authors: Christina M Parrinello; Morgan E Grams; Yingying Sang; David Couper; Lisa M Wruck; Danni Li; John H Eckfeldt; Elizabeth Selvin; Josef Coresh Journal: Clin Chem Date: 2016-05-19 Impact factor: 8.327