Hirano Keita, Ikenoue Tatsuyoshi, Seki Tomohisa, Komukai Sho, Nakata Hirosuke, Yasuda Takashi, Yasuda Yoshinari, Matsuzaki Keiichi, Kawamura Tetsuya, Yokoo Takashi, Maruyama Shoichi, Suzuki Hitoshi, Suzuki Yusuke, Fukuma Shingo
Human Health Sciences, Kyoto University Graduate School of Medicine, Kyoto, Japan.
Department of Nephrology, St. Luke's International Hospital, Tokyo, Japan.
J Nephrol. 2025 Jul 11. doi: 10.1007/s40620-025-02338-x.
Effective prediction of immunoglobulin A nephropathy (IgAN) progression is crucial for early intervention and management. We aimed to develop and validate distinct IgAN prediction models for clinical and research applications.
We analyzed data from the Japanese Nationwide Retrospective Cohort Study in IgAN (n = 1174) gathered over 10 years. The models were developed and tested using data from general physicians in primary care, specialists in tertiary care hospitals, and researchers at academic research institutes. Three tailored prediction models (Primary Care, Tertiary Care, and Research Institute Models) were created to address the unique needs of different clinical environments. The primary outcome was a composite renal event defined as a 1.5-fold increase in serum creatinine level or progression to kidney failure. The predictive performance was assessed using C-statistics.
In the derivation cohort, the primary care model included predictors such as estimated glomerular filtration rate < 45 mL/min/1.73 m, proteinuria ≥ 0.5 g/day, and non-use of corticosteroids, achieving a C-statistic of 0.796 (95% confidence interval [CI] 0.686-0.895). The tertiary care model showed a C-statistic of 0.807 (95% CI 0.713-0.886), using predictors such as glomerular number and histological severity. The research institute model, incorporating 38 variables, demonstrated a C-statistic of 0.802 (95% CI 0.686-0.906).
The prediction models for primary and tertiary care settings provided effective tools for forecasting renal outcomes in IgAN patients and are competitive with more complex machine learning-based models used in research. These models can help guide clinical decisions in various healthcare settings.
有效预测免疫球蛋白A肾病（IgAN）的进展对于早期干预和管理至关重要。我们旨在开发并验证用于临床和研究应用的不同IgAN预测模型。
我们分析了来自日本全国IgAN回顾性队列研究的数据（n = 1174），这些数据收集了超过10年。使用来自基层医疗的全科医生、三级医院的专科医生以及学术研究机构的研究人员的数据来开发和测试模型。创建了三个定制的预测模型（基层医疗模型、三级医疗模型和研究所模型）以满足不同临床环境的独特需求。主要结局是复合肾脏事件，定义为血清肌酐水平增加1.5倍或进展为肾衰竭。使用C统计量评估预测性能。
在推导队列中，基层医疗模型纳入了诸如估计肾小球滤过率<45 mL/min/1.73 m²、蛋白尿≥0.5 g/天以及未使用皮质类固醇等预测因素，C统计量为0.796（95%置信区间[CI] 0.686 - 0.895）。三级医疗模型使用诸如肾小球数量和组织学严重程度等预测因素，C统计量为0.807（95% CI 0.713 - 0.886）。研究所模型纳入了38个变量，C统计量为0.802（95% CI 0.686 - 0.906）。
基层医疗和三级医疗环境的预测模型为预测IgAN患者的肾脏结局提供了有效的工具，并且与研究中使用的更复杂的基于机器学习的模型具有竞争力。这些模型可以帮助指导各种医疗环境中的临床决策。
