Abstract

Background: Renal interstitial inflammation (RII) is a frequent pathological feature in IgA nephropathy (IgAN), but its prognostic value remains uncertain. This study investigated the effect of RII on renal outcomes and developed a machine learning-based model incorporating RII for individualized prognosis.
Materials and methods: We retrospectively analyzed 540 IgAN patients diagnosed by renal biopsy at Zhongda Hospital and the First People’s Hospital of Huai’an (2012 – 2023). The endpoint was a ≥ 50% decline in eGFR or end-stage renal disease, with follow-up to June 2024. Predictors included demographics, clinical/laboratory parameters (blood tests, serum biochemistry, 24-hour urine protein), and histopathology (Oxford MEST-C and RII scores). Variable selection used random forest, extreme gradient boosting, artificial neural networks, and LASSO regression. A logistic regression model and nomogram were developed and validated internally and externally.
Results: Of 540 patients (mean age 40.8 years; 50.6% male), 273 were in the derivation, 117 in the internal validation, and 150 in the external validation cohort. Patients with progression had lower baseline serum albumin (p = 0.023), lower estimated glomerular filtration rate (eGFR) (p < 0.001), and higher systolic blood pressure (SBP) and proteinuria (all p < 0.001). In multivariate analysis, RIIS1 (odds ratio (OR) 4.16, 95% CI 0.91 – 24.51, p = 0.048) and RIIS2 (OR 6.80, 95% CI 0.98 – 54.49, p = 0.039) independently predicted adverse outcomes. Use of renin-angiotensin-aldosterone system inhibitors was protective (OR 0.34, p = 0.026), while higher SBP increased risk (OR 1.04, p < 0.001). The nomogram achieved C-indices of 0.91, 0.90, and 0.92 in the derivation, internal, and external validation cohorts, respectively.
Conclusion: RII is an independent predictor of renal progression in IgAN. The developed model and nomogram may assist in individualized risk stratification.

*Qing Wei and Min Wu contributed equally to the article.

Author Details

Authors

• Qing Wei¹
• Min Wu¹*
• Jing Cao¹*
• Wenlong Ming²
• Yuxiang Gong³
• Min Chen⁴
• Minyu Yang³
• Dong Wei⁵
• Haifeng Ni³
• Pingsheng Chen⁶
• Bin Wang¹
• Bicheng Liu¹

Departments

• ¹ Department of Nephrology, Zhongda Hospital Southeast University,
• ² Nanjing University of Information Science and Technology,
• ³ Department of Nephrology Laboratory, Zhongda Hospital Southeast University,
• ⁴ Department of Nephrology, Huai’an First People’s Hospital, Huai’an,
• ⁵ Southeast University School of Medicine, and
• ⁶ Department of Pathology, Southeast University School of Medicine, Nanjing, Jiangsu, China

Address

Min Wu, PhD
and Bicheng Liu, PhD
Department of Nephrology
Zhongda Hospital Southeast University
No. 87 Dingjiaqiao, Nanjing,
Jiangsu 210009, China
Email: [email protected]; [email protected]

Citation