Improved predictions of total kidney volume growth rate in ADPKD using two-parameter least squares fitting

Abstract

AbstractMayo Imaging Classification (MIC) for predicting future kidney growth in autosomal dominant polycystic kidney disease (ADPKD) patients is calculated from a single MRI/CT scan assuming exponential kidney volume growth and height-adjusted total kidney volume at birth to be 150 mL/m. However, when multiple scans are available, how this information should be combined to improve prediction accuracy is unclear. Herein, we studied ADPKD subjects ($$n = 36$$ n = 36 ) with 8+ years imaging follow-up (mean = 11 years) to establish ground truth kidney growth trajectory. MIC annual kidney growth rate predictions were compared to ground truth as well as 1- and 2-parameter least squares fitting. The annualized mean absolute error in MIC for predicting total kidney volume growth rate was $$2.1\% \pm 2\%$$ 2.1 % ± 2 % compared to $$1.1\% \pm 1\%$$ 1.1 % ± 1 % ($$p = 0.002$$ p = 0.002 ) for a 2-parameter fit to the same exponential growth curve used for MIC when 4 measurements were available or $$1.4\% \pm 1\%$$ 1.4 % ± 1 % ($$p = 0.01$$ p = 0.01 ) with 3 measurements averaging together with MIC. On univariate analysis, male sex ($$p = 0.05$$ p = 0.05 ) and PKD2 mutation ($$p = 0.04$$ p = 0.04 ) were associated with poorer MIC performance. In ADPKD patients with 3 or more CT/MRI scans, 2-parameter least squares fitting predicted kidney volume growth rate better than MIC, especially in males and with PKD2 mutations where MIC was less accurate.