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Abstract
Estimates of glomerular filtration rate (GFR) that are based on serum creatinine are
routinely used; however, they are imprecise, potentially leading to the overdiagnosis
of chronic kidney disease. Cystatin C is an alternative filtration marker for estimating
GFR.
Using cross-sectional analyses, we developed estimating equations based on cystatin
C alone and in combination with creatinine in diverse populations totaling 5352 participants
from 13 studies. These equations were then validated in 1119 participants from 5 different
studies in which GFR had been measured. Cystatin and creatinine assays were traceable
to primary reference materials.
Mean measured GFRs were 68 and 70 ml per minute per 1.73 m(2) of body-surface area
in the development and validation data sets, respectively. In the validation data
set, the creatinine-cystatin C equation performed better than equations that used
creatinine or cystatin C alone. Bias was similar among the three equations, with a
median difference between measured and estimated GFR of 3.9 ml per minute per 1.73
m(2) with the combined equation, as compared with 3.7 and 3.4 ml per minute per 1.73
m(2) with the creatinine equation and the cystatin C equation (P=0.07 and P=0.05),
respectively. Precision was improved with the combined equation (interquartile range
of the difference, 13.4 vs. 15.4 and 16.4 ml per minute per 1.73 m(2), respectively
[P=0.001 and P<0.001]), and the results were more accurate (percentage of estimates
that were >30% of measured GFR, 8.5 vs. 12.8 and 14.1, respectively [P<0.001 for both
comparisons]). In participants whose estimated GFR based on creatinine was 45 to 74
ml per minute per 1.73 m(2), the combined equation improved the classification of
measured GFR as either less than 60 ml per minute per 1.73 m(2) or greater than or
equal to 60 ml per minute per 1.73 m(2) (net reclassification index, 19.4% [P<0.001])
and correctly reclassified 16.9% of those with an estimated GFR of 45 to 59 ml per
minute per 1.73 m(2) as having a GFR of 60 ml or higher per minute per 1.73 m(2).
The combined creatinine-cystatin C equation performed better than equations based
on either of these markers alone and may be useful as a confirmatory test for chronic
kidney disease. (Funded by the National Institute of Diabetes and Digestive and Kidney
Diseases.).
Equations to estimate glomerular filtration rate (GFR) are routinely used to assess kidney function. Current equations have limited precision and systematically underestimate measured GFR at higher values. To develop a new estimating equation for GFR: the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation. Cross-sectional analysis with separate pooled data sets for equation development and validation and a representative sample of the U.S. population for prevalence estimates. Research studies and clinical populations ("studies") with measured GFR and NHANES (National Health and Nutrition Examination Survey), 1999 to 2006. 8254 participants in 10 studies (equation development data set) and 3896 participants in 16 studies (validation data set). Prevalence estimates were based on 16,032 participants in NHANES. GFR, measured as the clearance of exogenous filtration markers (iothalamate in the development data set; iothalamate and other markers in the validation data set), and linear regression to estimate the logarithm of measured GFR from standardized creatinine levels, sex, race, and age. In the validation data set, the CKD-EPI equation performed better than the Modification of Diet in Renal Disease Study equation, especially at higher GFR (P < 0.001 for all subsequent comparisons), with less bias (median difference between measured and estimated GFR, 2.5 vs. 5.5 mL/min per 1.73 m(2)), improved precision (interquartile range [IQR] of the differences, 16.6 vs. 18.3 mL/min per 1.73 m(2)), and greater accuracy (percentage of estimated GFR within 30% of measured GFR, 84.1% vs. 80.6%). In NHANES, the median estimated GFR was 94.5 mL/min per 1.73 m(2) (IQR, 79.7 to 108.1) vs. 85.0 (IQR, 72.9 to 98.5) mL/min per 1.73 m(2), and the prevalence of chronic kidney disease was 11.5% (95% CI, 10.6% to 12.4%) versus 13.1% (CI, 12.1% to 14.0%). The sample contained a limited number of elderly people and racial and ethnic minorities with measured GFR. The CKD-EPI creatinine equation is more accurate than the Modification of Diet in Renal Disease Study equation and could replace it for routine clinical use. National Institute of Diabetes and Digestive and Kidney Diseases.
Statistics is a subject of many uses and surprisingly few effective practitioners. The traditional road to statistical knowledge is blocked, for most, by a formidable wall of mathematics. The approach in An Introduction to the Bootstrap avoids that wall. It arms scientists and engineers, as well as statisticians, with the computational techniques they need to analyze and understand complicated data sets.
Identification of key factors associated with the risk of developing cardiovascular disease and quantification of this risk using multivariable prediction algorithms are among the major advances made in preventive cardiology and cardiovascular epidemiology in the 20th century. The ongoing discovery of new risk markers by scientists presents opportunities and challenges for statisticians and clinicians to evaluate these biomarkers and to develop new risk formulations that incorporate them. One of the key questions is how best to assess and quantify the improvement in risk prediction offered by these new models. Demonstration of a statistically significant association of a new biomarker with cardiovascular risk is not enough. Some researchers have advanced that the improvement in the area under the receiver-operating-characteristic curve (AUC) should be the main criterion, whereas others argue that better measures of performance of prediction models are needed. In this paper, we address this question by introducing two new measures, one based on integrated sensitivity and specificity and the other on reclassification tables. These new measures offer incremental information over the AUC. We discuss the properties of these new measures and contrast them with the AUC. We also develop simple asymptotic tests of significance. We illustrate the use of these measures with an example from the Framingham Heart Study. We propose that scientists consider these types of measures in addition to the AUC when assessing the performance of newer biomarkers.
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