Department of Medicine, Haukeland University Hospital, Bergen, Norway.
Renal Research Group, Department of Clinical Medicine, University of Bergen, Norway.
Nephrol Dial Transplant. 2023 May 4;38(5):1183-1191. doi: 10.1093/ndt/gfac225.
Recently, two immunoglobulin A (IgA) nephropathy-prediction tools were developed that combine clinical and histopathologic parameters. The International IgAN Prediction Tool predicts the risk for 50% declines in the estimated glomerular filtration rate or end-stage kidney disease up to 80 months after diagnosis. The IgA Nephropathy Clinical Decision Support System uses artificial neural networks to estimate the risk for end-stage kidney disease. We aimed to externally validate both prediction tools using a Norwegian cohort with a long-term follow-up.
We included 306 patients with biopsy-proven primary IgA nephropathy in this study. Histopathologic samples were retrieved from the Norwegian Kidney Biopsy Registry and reclassified according to the Oxford Classification. We used discrimination and calibration as principles for externally validating the prognostic models.
The median patient follow-up was 17.1 years. A cumulative, dynamic, time-dependent receiver operating characteristic analysis showed area under the curve values ranging from 0.90 at 5 years to 0.83 at 20 years for the International IgAN Prediction Tool, while time-naive analysis showed an area under the curve value at 0.83 for the IgA Nephropathy Clinical Decision Support System. The International IgAN Prediction Tool was well calibrated, while the IgA Nephropathy Clinical Decision Support System tends to underestimate risk for patients at higher risk and overestimates risk in the lower risk categories.
We have externally validated two prediction tools for IgA nephropathy. The International IgAN Prediction Tool performed well, while the IgA Nephropathy Clinical Decision Support System has some limitations.
最近，开发了两种结合临床和组织病理学参数的 IgA 肾病预测工具。国际 IgA 肾病预测工具预测诊断后 80 个月内肾小球滤过率下降 50%或终末期肾病的风险。IgA 肾病临床决策支持系统使用人工神经网络来估计终末期肾病的风险。我们旨在使用具有长期随访的挪威队列来外部验证这两种预测工具。
我们纳入了 306 例经活检证实的原发性 IgA 肾病患者。组织病理学样本取自挪威肾活检登记处，并根据牛津分类进行重新分类。我们使用判别和校准作为外部验证预后模型的原则。
中位患者随访时间为 17.1 年。累积、动态、时间依赖性接受者操作特征分析显示，国际 IgA 肾病预测工具的曲线下面积值在 5 年时为 0.90，在 20 年时为 0.83，而时间上的分析显示，IgA 肾病临床决策支持系统的曲线下面积值为 0.83。国际 IgA 肾病预测工具具有良好的校准能力，而 IgA 肾病临床决策支持系统倾向于低估高风险患者的风险，高估低风险类别的风险。
我们已经对两种 IgA 肾病预测工具进行了外部验证。国际 IgA 肾病预测工具表现良好，而 IgA 肾病临床决策支持系统存在一些局限性。
