Wolff Constantin A, Aiello Valeria, Elhassan Elhussein A E, Cristalli Carlotta, Lerario Sarah, Paccapelo Alexandro, Ciurli Francesca, Montanari Francesca, Conti Amalia, Benson Katherine, Seri Marco, Brigl Carolin B, Münster Julia S, Sciascia Nicola, Kursch Sebastian, de Fallois Jonathan, La Manna Gaetano, Eckardt Kai-Uwe, Rank Nina, Popp Bernt, Schönauer Ria, Conlon Peter J, Capelli Irene, Halbritter Jan
Department of Nephrology and Medical Intensive Care, Charité Universitätsmedizin Berlin, Berlin, Germany.
Nephrology, Dialysis and Kidney Transplant Unit, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy.
Clin J Am Soc Nephrol. 2025 Mar 1;20(3):397-409. doi: 10.2215/CJN.0000000625. Epub 2024 Dec 20.
The Mayo clinic imaging classification and the predicting renal outcome in polycystic kidney disease score are used to assess the risk of progression to kidney failure in autosomal dominant polycystic kidney disease. Mayo imaging classification and predicting renal outcome in polycystic kidney disease show little concordance; combined use increased the ability to identify rapid progression especially among intermediate risk patients. Accurate risk prediction is key for determining indication for specific treatment.
Autosomal dominant polycystic kidney disease is the most common genetic cause of kidney failure. Specific treatment is indicated on observed or predicted rapid progression. For the latter, risk stratification tools have been developed independently based on either total kidney volume or genotyping as well as clinical variables. This study aimed to improve risk prediction by combining both imaging and clinical-genetic scores.
We conducted a retrospective multicenter cohort study of 468 patients diagnosed with autosomal dominant polycystic kidney disease. Clinical, imaging, and genetic data were analyzed for risk prediction. We defined rapid disease progression as an eGFR slope ≥3 ml/min per 1.73 m per year over 2 years, Mayo imaging classification (MIC) 1D–1E, or a predicting renal outcome in polycystic kidney disease (PROPKD) score of ≥7 points. Using MIC, PROPKD, and rare exome variant ensemble learner scores, several combined models were designed to develop a new classification with improved risk stratification. Primary endpoints were the development of advanced CKD stages G4–G5, longitudinal changes in eGFR, and clinical variables such as hypertension or urological events. Statistically, logistic regression, survival, receiver operating characteristic analyses, linear mixed models, and Cox proportional hazards models were used.
-genotype ( < 0.001), MIC class 1E ( < 0.001), early-onset hypertension ( < 0.001), and early-onset urological events ( = 0.003) correlated best with rapid progression in multivariable analysis. While the MIC showed satisfactory specificity (77%), the PROPKD was more sensitive (59%). Among individuals with an intermediate risk in one of the scores, integration of the other score (combined scoring) allowed for more accurate stratification.
The combined use of both risk scores was associated with higher ability to identify rapid progressors and resulted in a better stratification, notably among intermediate risk patients.
梅奥诊所影像学分类和多囊肾病预测肾脏转归评分用于评估常染色体显性多囊肾病进展至肾衰竭的风险。梅奥影像学分类和多囊肾病预测肾脏转归之间的一致性较差；联合使用可提高识别快速进展的能力，尤其是在中度风险患者中。准确的风险预测是确定特定治疗指征的关键。
常染色体显性多囊肾病是肾衰竭最常见的遗传病因。根据观察到的或预测的快速进展情况给予特定治疗。对于后者，已基于总肾体积或基因分型以及临床变量独立开发了风险分层工具。本研究旨在通过结合影像学和临床-遗传评分来改善风险预测。
我们对468例诊断为常染色体显性多囊肾病的患者进行了一项回顾性多中心队列研究。分析临床、影像学和基因数据以进行风险预测。我们将疾病快速进展定义为2年内估算肾小球滤过率（eGFR）斜率≥3 ml/min/1.73m²/年、梅奥影像学分类（MIC）1D - 1E或多囊肾病预测肾脏转归（PROPKD）评分≥7分。使用MIC、PROPKD和罕见外显子变异集成学习器评分，设计了几种联合模型以开发具有改进风险分层的新分类。主要终点是晚期慢性肾脏病（CKD）G4 - G5期的发生、eGFR的纵向变化以及高血压或泌尿系统事件等临床变量。在统计学上，使用了逻辑回归、生存分析、受试者工作特征分析、线性混合模型和Cox比例风险模型。
在多变量分析中，基因型（<0.001）、MIC 1E级（<0.001）、早发性高血压（<0.001）和早发性泌尿系统事件（=0.003）与快速进展的相关性最强。虽然MIC显示出令人满意的特异性（77%），但PROPKD更敏感（59%）。在其中一项评分处于中度风险的个体中，整合另一项评分（联合评分）可实现更准确的分层。
两种风险评分的联合使用与识别快速进展者的更高能力相关，并导致更好的分层，特别是在中度风险患者中。
