Ng Derek K, Matheson Matthew B, Hartung Erum A
Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland, USA; Division of Nephrology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.
Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland, USA.
Kidney Int. 2025 May;107(5):788-791. doi: 10.1016/j.kint.2025.02.018.
Autosomal recessive polycystic kidney disease is rare, with heterogeneous disease progression toward kidney failure. Risk stratification tools are needed to identify patients at higher risk of progression. Burgmaier et al. developed a relative risk score model in the international ARPKD registry for children older than 2 months of age without kidney failure. Their regression-based model included 5 predictors and yielded a simple prognostic score that classified "lower-risk" and "higher-risk" groups. Discrimination separating these 2 groups was good, but there are potential future opportunities for absolute risk prediction. We discuss considerations for the interpretation of relative risk scores and external validation of prediction models in rare diseases like autosomal recessive polycystic kidney disease.
常染色体隐性多囊肾病较为罕见，疾病进展至肾衰竭的情况具有异质性。需要风险分层工具来识别疾病进展风险较高的患者。布尔迈尔等人在国际常染色体隐性多囊肾病登记处为2个月以上无肾衰竭的儿童开发了一个相对风险评分模型。他们基于回归的模型纳入了5个预测因素，并得出了一个简单的预后评分，将患者分为“低风险”和“高风险”组。区分这两组的判别效果良好，但未来在绝对风险预测方面仍有潜在机会。我们讨论了在常染色体隐性多囊肾病等罕见疾病中解读相对风险评分及对预测模型进行外部验证的相关考量。
