Assessment of various parameters in the estimation of differential renal function using technetium-99m...

Differential renal function (DRF) is an important parameter that should be assessed from virtually every dynamic renogram. With the introduction of technetium-99m mercaptoacetyltriglycine (99mTc-MAG3), a tracer with a high renal extraction, the estimation of DRF might hopefully become accurate and reproducible both between observers in the same institution and also between institutions. The aim of this study was to assess the effect of different parameters on the estimation of DRF. To this end we investigated two groups of children: group A, comprising 35 children with a single kidney (27 of whom had poor renal function), and group B, comprising 20 children with two kidneys and normal global function who also had an associated 99mTc-dimercaptosuccinic acid scan (99mTc-DMSA). The variables assessed for their effect on the estimation of DRF were: different operators, the choice of renal regions of interest (ROIs), the applied background subtraction, and six different techniques for analysis of the renogram. The six techniques were based on: linear regression of the slopes in the Rutland-Patlak plot, matrix deconvolution, differential method, integral method, linear regression of the slope of the renograms, and the area under the curve of the renogram. The estimation of DRF was less dependent upon both observer and method in patients with two normally functioning kidneys than in patients with a single kidney. The inter-observer comparison among children in either group was not dependent on either ROI or background subtraction. However, in patients with poor renal function the method of choice for the estimation of DRF was dependent on background subtraction, though not ROI. In children with two kidneys and normal renal function, the estimation of DRF from the 24 techniques gave similar results. Methods that produced DRF values closest to expected results, from either group of children, were the Rutland-Patlak plot and matrix deconvolution methods.