Clinical and experimental nephrology · 2018
Age- and height-adjusted total kidney volume growth rate in autosomal dominant polycystic kidney diseases.
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Higashihara Eiji, Yamamoto Kouji, Kaname Shinya, Okegawa Takatsugu, Tanbo Mitsuhiro, Yamaguchi Tsuyoshi, Shigemori Kaori, Miyazaki Isao, Yokoyama Kenichi, Nutahara Kikuo
Аннотация
BACKGROUND: The Mayo Clinic Image Classification (MIC) was proposed as a renal prognosis prediction model for autosomal dominant polycystic kidney disease (ADPKD). MIC is based on the assumption of exponential constant increase in height-adjusted total kidney volume (HtTKV). HtTKV growth rate is calculated by one-time measurement of HtTKV and age. We named it as an age-adjusted HtTKV growth rate (AHTKV-α). AHTKV-α was compared with HtTKV slope measured by at least two HtTKV values.
METHODS: Comparison of repeatability between AHTKV-α and HtTKV slope, correlation of subgroups divided according to baseline AHTKV-α and HtTKV slope with disease manifestations, estimated glomerular filtration rate (eGFR) slope, and renal survival were analyzed in 296 patients with ADPKD. PKD genotype influences were compared between AHTKV-α and HtTKV slope in 88 patients with characterized PKD mutations.
RESULTS: Absolute differences between baseline and follow-up measures were significantly larger for the HtTKV slope than for AHTKV-α (P < 0.0001). From baseline AHTKV-α-based subgroups A-E according to MIC, disease manifestations occurred earlier and future eGFR slopes became steeper (P < 0.0001). Multivariate hazard ratios of renal survival differed significantly among baseline AHTKV-α-based subgroups. Inter-subgroup differences in these predictors were less evident during baseline HtTKV slope-based classification. AHTKV-α values, but not HtTKV slopes, were significantly higher for PKD1 mutation carriers than for PKD2 mutation carriers (P < 0.0001).
CONCLUSION: MIC is a good renal prediction model applicable to Japanese patients also. AHTKV-α can be a more sensitive and reliable indicator in TKV growth rate than HtTKV slope.
METHODS: Comparison of repeatability between AHTKV-α and HtTKV slope, correlation of subgroups divided according to baseline AHTKV-α and HtTKV slope with disease manifestations, estimated glomerular filtration rate (eGFR) slope, and renal survival were analyzed in 296 patients with ADPKD. PKD genotype influences were compared between AHTKV-α and HtTKV slope in 88 patients with characterized PKD mutations.
RESULTS: Absolute differences between baseline and follow-up measures were significantly larger for the HtTKV slope than for AHTKV-α (P < 0.0001). From baseline AHTKV-α-based subgroups A-E according to MIC, disease manifestations occurred earlier and future eGFR slopes became steeper (P < 0.0001). Multivariate hazard ratios of renal survival differed significantly among baseline AHTKV-α-based subgroups. Inter-subgroup differences in these predictors were less evident during baseline HtTKV slope-based classification. AHTKV-α values, but not HtTKV slopes, were significantly higher for PKD1 mutation carriers than for PKD2 mutation carriers (P < 0.0001).
CONCLUSION: MIC is a good renal prediction model applicable to Japanese patients also. AHTKV-α can be a more sensitive and reliable indicator in TKV growth rate than HtTKV slope.