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Background <p>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&#xa0;applications.</p> Methods <p>We analyzed data from the Japanese Nationwide Retrospective Cohort Study in IgAN (<i>n</i> = 1174) gathered&#xa0;over 10&#xa0;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.</p> Results <p>In the derivation cohort, the primary care model included predictors such as estimated glomerular filtration rate &lt; 45&#xa0;mL/min/1.73 m<sup>2</sup>, proteinuria ≥ 0.5&#xa0;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).</p> Conclusions <p>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.</p> Graphical abstract <p></p>

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Development and validation of personalized risk prediction models for patients with IgA nephropathy: a nationwide multicenter cohort study

  • 期刊论文

  • 发表日期：

    2025年7月11日

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  • Keita Hirano,
  • Tatsuyoshi Ikenoue,
  • Tomohisa Seki,
  • Sho Komukai,
  • Hirosuke Nakata,
  • Takashi Yasuda,
  • Yoshinari Yasuda,
  • Keiichi Matsuzaki,
  • Tetsuya Kawamura,
  • Takashi Yokoo,
  • Shoichi Maruyama,
  • Hitoshi Suzuki,
  • Yusuke Suzuki,
  • Shingo Fukuma

摘要

Background

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.

Methods

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.

Results

In the derivation cohort, the primary care model included predictors such as estimated glomerular filtration rate < 45 mL/min/1.73 m2, 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).

Conclusions

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.

Graphical abstract