Division of Nephrology, Department of Internal Medicine, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.
Department of Radiology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.
Clin J Am Soc Nephrol. 2024 May 1;19(5):591-601. doi: 10.2215/CJN.0000000000000427. Epub 2024 Feb 26.
The Mayo Imaging Classification was developed to predict the rate of disease progression in patients with autosomal dominant polycystic kidney disease. This study aimed to validate its ability to predict kidney outcomes in a large multicenter autosomal dominant polycystic kidney disease cohort.
Included were patients with ≥1 height-adjusted total kidney volume (HtTKV) measurement and ≥3 eGFR values during ≥1-year follow-up. Mayo HtTKV class stability, kidney growth rates, and eGFR decline rates were calculated. The observed eGFR decline was compared with predictions from the Mayo Clinic future eGFR equation. The future eGFR prediction equation was also tested for nonlinear eGFR decline. Kaplan-Meier survival analysis and Cox regression models were used to assess time to kidney failure using Mayo HtTKV class as a predictor variable.
We analyzed 618 patients with a mean age of 47±11 years and mean eGFR of 64±25 ml/min per 1.73 m 2 at baseline. Most patients (82%) remained in their baseline Mayo HtTKV class. During a mean follow-up of 5.1±2.2 years, the mean total kidney volume growth rates and eGFR decline were 5.33%±3.90%/yr and -3.31±2.53 ml/min per 1.73 m 2 per year, respectively. Kidney growth and eGFR decline showed considerable overlap between the classes. The observed annual eGFR decline was not significantly different from the predicted values for classes 1A, 1B, 1C, and 1D but significantly slower for class 1E. This was also observed in patients aged younger than 40 years and older than 60 years and those with PKD2 mutations. A polynomial model allowing nonlinear eGFR decline provided more accurate slope predictions. Ninety-seven patients (16%) developed kidney failure during follow-up. The classification predicted the development of kidney failure, although the sensitivity and positive predictive values were limited.
The Mayo Imaging Classification demonstrated acceptable stability and generally predicted kidney failure and eGFR decline rate. However, there was marked interindividual variability in the rate of disease progression within each class.
Mayo 影像学分类法是为了预测常染色体显性多囊肾病患者的疾病进展率而开发的。本研究旨在验证其在一个大型多中心常染色体显性多囊肾病队列中预测肾脏结局的能力。
纳入标准为至少有 1 次身高校正后的总肾体积（HtTKV）测量值和至少 3 次 eGFR 值，且随访时间至少 1 年。计算 Mayo HtTKV 分类稳定性、肾生长率和 eGFR 下降率。将观察到的 eGFR 下降与 Mayo 诊所未来 eGFR 方程的预测值进行比较。还测试了未来 eGFR 预测方程是否存在非线性 eGFR 下降。使用 Kaplan-Meier 生存分析和 Cox 回归模型，以 Mayo HtTKV 分类为预测变量，评估肾衰竭的时间。
我们分析了 618 名年龄 47±11 岁、基线时 eGFR 为 64±25ml/min/1.73m2 的患者。大多数患者（82%）在基线时仍处于其 Mayo HtTKV 分类。在平均 5.1±2.2 年的随访期间，平均总肾体积增长率和 eGFR 下降率分别为 5.33%±3.90%/年和-3.31±2.53ml/min/1.73m2/年。类内的肾生长和 eGFR 下降有很大的重叠。观察到的年 eGFR 下降与 1A、1B、1C 和 1D 类的预测值没有显著差异，但与 1E 类的预测值显著较慢。这在年龄小于 40 岁和大于 60 岁的患者以及 PKD2 突变患者中也观察到。允许非线性 eGFR 下降的多项式模型提供了更准确的斜率预测。97 名患者（16%）在随访期间发生了肾衰竭。该分类预测了肾衰竭的发生，尽管敏感性和阳性预测值有限。
Mayo 影像学分类法显示出可接受的稳定性，通常可预测肾衰竭和 eGFR 下降率。然而，在每个类别内，疾病进展的个体间差异显著。
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