Comparison of Various Equations for Estimating GFR in Malawi: How to Determine Renal Function in Resource Limited Settings?

<div><p>Background</p><p>Chronic kidney disease (CKD) is a probably underrated public health problem in Sub-Saharan-Africa, in particular in combination with HIV-infection. Knowledge about the CKD prevalence is scarce and in the available literature different methods to classify CKD are used impeding comparison and general prevalence estimates.</p><p>Methods</p><p>This study assessed different serum-creatinine based equations for glomerular filtration rates (eGFR) and compared them to a cystatin C based equation. The study was conducted in Lilongwe, Malawi enrolling a population of 363 adults of which 32% were HIV-positive.</p><p>Results</p><p>Comparison of formulae based on Bland-Altman-plots and accuracy revealed best performance for the CKD-EPI equation without the correction factor for black Americans. Analyzing the differences between HIV-positive and –negative individuals CKD-EPI systematically overestimated eGFR in comparison to cystatin C and therefore lead to underestimation of CKD in HIV-positives.</p><p>Conclusions</p><p>Our findings underline the importance for standardization of eGFR calculation in a Sub-Saharan African setting, to further investigate the differences with regard to HIV status and to develop potential correction factors as established for age and sex.</p></div

Cystatin C (van Deventer) vs. Cockcroft-Gault.

<p>The coloured lines represent the mean differences of the two equations to be compared at every point of the mean of the estimated GFRs, by HIV status; the coloured shaded areas mark the limits of agreement, which are mean- differences plus or minus two standard-deviations. Assuming a normal distribution, 95% of the dots are expected to appear within the limits of agreement. [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0130453#pone.0130453.ref040" target="_blank">40</a>] Closer margins reflect a higher agreement of the different methods.</p

Comparison of ART eligible patients who started and did not start ART, by clinic.

<p>Results from chi-square tests of associations within clinic.</p>Þ<p>Through June 30, 2011;</p>□<p>row percentages.</p>*<p>Included in WHO 3 or WHO 4.</p

Results from univariate and multivariate logistic regression models assessing risk factors associated with starting ART vs. not starting ART among eligible patients, stratified by clinic.

<p>AOR – adjusted odds ratio from multivariate models adjusted for all other included factors.</p>*<p>p<0.05;</p>**<p>p<0.01,</p>***<p>p<0.001. 95% CI presented in parenthesis.</p>Þ<p>Through June 30, 2011;</p

Cystatin C (van Deventer) vs. CKD-EPI (without factor for black Americans).

<p>Cystatin C (van Deventer) vs. CKD-EPI (without factor for black Americans).</p

CKD-EPI-Cystatin-C versus CKD-EPI without factor for black Americans.

<p>CKD-EPI-Cystatin-C versus CKD-EPI without factor for black Americans.</p

Discrepancies in staging results, cut off stage 3 and 2.

<p>* with factor for black Americans</p><p>Discrepancies in staging results, cut off stage 3 and 2.</p

Cystatin C (van Deventer) versus MDRD4 with factor for black Americans.

<p>Cystatin C (van Deventer) versus MDRD4 with factor for black Americans.</p

Descriptive statistics of all eligible patients, by clinic.

*<p>unless otherwise specified;</p>**<p>Through June 30, 2011;</p>***<p>Included in WHO 3 or WHO 4.</p

Flow chart of patient population characteristics retained in the study.

<p>Flow chart of patient population characteristics retained in the study.</p