Background: Although IgA nephropathy (IgAN), an immune-mediated disease with heterogeneous clinical and pathological phenotypes, is the most common glomerulonephritis worldwide, it remains unclear which IgAN patients benefit from immunosuppression (IS) therapy.
Methods: Clinical and pathological data from 4047 biopsy-proven IgAN patients from 24 renal centres in China were included. The derivation and validation cohorts were composed of 2058 and 1989 patients, respectively. Model-based recursive partitioning, a machine learning approach, was performed to partition patients in the derivation cohort into subgroups with different IS long-term benefits, associated with time to end-stage kidney disease, measured by adjusted Kaplan-Meier estimator and adjusted hazard ratio (HR) using Cox regression.
Findings: Three identified subgroups obtained a significant IS benefits with HRs ≤ 1. In patients with serum creatinine ≤ 1·437 mg/dl, the benefits of IS were observed in those with proteinuria > 1·525 g/24h (node 6; HR = 0·50; 95% CI, 0·29 to 0·89; P = 0·02), especially in those with proteinuria > 2·480 g/24h (node 8; HR = 0·23; 95% CI, 0·11 to 0·50; P <0·001). In patients with serum creatinine > 1·437 mg/dl, those with high proteinuria and crescents benefitted from IS (node 12; HR = 0·29; 95% CI, 0·09 to 0·94; P = 0·04). The treatment benefits were externally validated in the validation cohort.
Interpretation: Machine learning could be employed to identify subgroups with different IS benefits. These efforts promote decision-making, assist targeted clinical trial design, and shed light on individualised treatment in IgAN patients.
Funding: National Key Research and Development Program of China (2016YFC0904103), National Key Technology R&D Program (2015BAI12B02).
Keywords: Decision support; Glomerulonephritis; Heterogeneous treatment response; IgA nephropathy; Machine learning; Precision medicine.
Copyright © 2020 The Authors. Published by Elsevier B.V. All rights reserved.
Conflict of interest statement
Declaration of Competing Interest The authors declare that they have no competing interests.
Prediction and Risk Stratification of Kidney Outcomes in IgA Nephropathy.Am J Kidney Dis. 2019 Sep;74(3):300-309. doi: 10.1053/j.ajkd.2019.02.016. Epub 2019 Apr 25. Am J Kidney Dis. 2019. PMID: 31031086 Clinical Trial.
A validation study of crescents in predicting ESRD in patients with IgA nephropathy.J Transl Med. 2018 May 3;16(1):115. doi: 10.1186/s12967-018-1488-5. J Transl Med. 2018. PMID: 29724226 Free PMC article.
Patient classification and outcome prediction in IgA nephropathy.Comput Biol Med. 2015 Nov 1;66:278-86. doi: 10.1016/j.compbiomed.2015.09.003. Epub 2015 Sep 25. Comput Biol Med. 2015. PMID: 26453758
Non-immunosuppressive treatment for IgA nephropathy.Cochrane Database Syst Rev. 2011 Mar 16;(3):CD003962. doi: 10.1002/14651858.CD003962.pub2. Cochrane Database Syst Rev. 2011. PMID: 21412884 Review.
Has The Time Arrived to Refine The Indications of Immunosuppressive Therapy and Prognosis in IgA Nephropathy?J Clin Med. 2019 Oct 2;8(10):1584. doi: 10.3390/jcm8101584. J Clin Med. 2019. PMID: 31581654 Free PMC article. Review.
- Holdsworth S.R., Kitching A.R. Immune-mediated kidney disease in 2017: progress in mechanisms and therapy for immunological kidney disease. Nat Rev Nephrol. 2018;14(2):76–78. - PubMed
- Le W., Liang S., Hu Y. Long-term renal survival and related risk factors in patients with IgA nephropathy: results from a cohort of 1155 cases in a Chinese adult population. Nephrol Dial Transplant. 2012;27(4):1479–1485. - PubMed