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Machine learning-based 2-year risk prediction tool in immunoglobulin A nephropathy

Authors
 Yujeong Kim  ;  Jong Hyun Jhee  ;  Chan Min Park  ;  Donghwan Oh  ;  Beom Jin Lim  ;  Hoon Young Choi  ;  Dukyong Yoon  ;  Hyeong Cheon Park 
Citation
 KIDNEY RESEARCH AND CLINICAL PRACTICE, Vol.43(6) : 739-752, 2024-11 
Journal Title
KIDNEY RESEARCH AND CLINICAL PRACTICE
ISSN
 2211-9132 
Issue Date
2024-11
Keywords
Chronic kidney failure ; Immunoglobulin A nephropathy ; Machine learning ; Proteinuria
Abstract
Background: This study aimed to develop a machine learning-based 2-year risk prediction model for early identification of patients with rapid progressive immunoglobulin A nephropathy (IgAN). We also assessed the model's performance to predict the long-term kidney-related outcome of patients.

Methods: A retrospective cohort of 1,301 patients with biopsy-proven IgAN from two tertiary hospitals was used to derive and externally validate a random forest-based prediction model predicting primary outcome (30% decline in estimated glomerular filtration rate from baseline or end-stage kidney disease requiring renal replacement therapy) and secondary outcome (improvement of proteinuria) within 2 years after kidney biopsy.

Results: For the 2-year prediction of primary outcomes, precision, recall, area-under-the-curve, precision-recall-curve, F1, and Brier score were 0.259, 0.875, 0.771, 0.242, 0.400, and 0.309, respectively. The values for the secondary outcome were 0.904, 0.971, 0.694, 0.903, 0.955, and 0.113, respectively. From Shapley Additive exPlanations analysis, the most informative feature identifying both outcomes was baseline proteinuria. When Kaplan-Meier analysis for 10-year kidney outcome risk was performed with three groups by predicting probabilities derived from the 2-year primary outcome prediction model (low, moderate, and high), high (hazard ratio [HR], 13.00; 95% confidence interval [CI], 9.52-17.77) and moderate (HR, 12.90; 95% CI, 9.92-16.76) groups showed higher risks compared with the low group. From the 2-year secondary outcome prediction model, low (HR, 1.66; 95% CI, 1.42-1.95) and moderate (HR, 1.42; 95% CI, 0.99-2.03) groups were at greater risk for 10-year prognosis than the high group.

Conclusion: Our machine learning-based 2-year risk prediction models for the progression of IgAN showed reliable performance and effectively predicted long-term kidney outcome.
Files in This Item:
T202407333.pdf Download
DOI
10.23876/j.krcp.23.076
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Biomedical Systems Informatics (의생명시스템정보학교실) > 1. Journal Papers
1. College of Medicine (의과대학) > Dept. of Internal Medicine (내과학교실) > 1. Journal Papers
Yonsei Authors
Park, Hyeong Cheon(박형천) ORCID logo https://orcid.org/0000-0002-1550-0812
Oh, Donghwan(오동환)
Yoon, Dukyong(윤덕용)
Lim, Beom Jin(임범진) ORCID logo https://orcid.org/0000-0003-2856-0133
Jhee, Jong Hyun(지종현)
Choi, Hoon Young(최훈영) ORCID logo https://orcid.org/0000-0002-4245-0339
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/201452
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