Kidney diseases (Basel, Switzerland) · 2024
Development and Validation of a Machine Learning-Based Prognostic Model for IgA Nephropathy with Chronic Kidney Disease Stage 3 or 4.
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Yu Zixian, Ning Xiaoxuan, Qin Yunlong, Xing Yan, Jia Qing, Yuan Jinguo, Zhang Yumeng, Zhao Jin, Sun Shiren
Аннотация
INTRODUCTION: Immunoglobulin A nephropathy (IgAN) patients with lower estimated glomerular filtration rate (eGFR) and higher proteinuria are at a higher risk for end-stage kidney disease (ESKD) and their prognosis is still unclear. We aim to develop and validate prognostic models in IgAN patients with chronic kidney disease (CKD) stage 3 or 4 and proteinuria ≥1.0 g/d.
METHODS: Patients who came from Xijing Hospital, spanning December 2008 to January 2020 were divided into training and test cohorts randomly, with a ratio of 7:3, achieving ESKD and death as study endpoints. Created prediction models for IgAN patients based on 66 clinical and pathological characteristics using the random survival forests (RSF), survival support vector machine (SSVM), eXtreme Gradient Boosting (XGboost), and Cox regression models. The concordance index (C-index), integrated Brier scores (IBS), net reclassification index (NRI), and integrated discrimination improvement (IDI) were used to evaluate discrimination, calibration, and risk classification, respectively.
RESULTS: A total of 263 patients were enrolled. The median follow-up time was 57.3 months, with 124 (47.1%) patients experiencing combined events. Age, blood urea nitrogen, serum uric acid, serum potassium, glomeruli sclerosis ratio, hemoglobin, and tubular atrophy/interstitial fibrosis were identified as risk factors. The RSF model predicted the prognosis with a C-index of 0.871 (0.842, 0.900) in training cohort and 0.810 (0.732, 0.888) in test cohort, which was higher than the models built by SSVM model (0.794 [0.753, 0.835] and 0.805 [0.731, 0.879], respectively), XGboost model (0.840 [0.797, 0.883] and 0.799 [0.723, 0.875], respectively) and Cox regression (0.776 [0.727, 0.825] and 0.793 [0.713, 0.873], respectively). NRI and IDI showed that the RSF model exhibited superior performance than the Cox model.
CONCLUSION: Our model introduced seven risk factors that may be useful in predicting the progression of IgAN patients with CKD stage 3 or 4 and proteinuria ≥1.0 g/d. The RSF model is applicable for identifying the progression of IgAN and has outperformed than SSVM, XGboost, and Cox models.
METHODS: Patients who came from Xijing Hospital, spanning December 2008 to January 2020 were divided into training and test cohorts randomly, with a ratio of 7:3, achieving ESKD and death as study endpoints. Created prediction models for IgAN patients based on 66 clinical and pathological characteristics using the random survival forests (RSF), survival support vector machine (SSVM), eXtreme Gradient Boosting (XGboost), and Cox regression models. The concordance index (C-index), integrated Brier scores (IBS), net reclassification index (NRI), and integrated discrimination improvement (IDI) were used to evaluate discrimination, calibration, and risk classification, respectively.
RESULTS: A total of 263 patients were enrolled. The median follow-up time was 57.3 months, with 124 (47.1%) patients experiencing combined events. Age, blood urea nitrogen, serum uric acid, serum potassium, glomeruli sclerosis ratio, hemoglobin, and tubular atrophy/interstitial fibrosis were identified as risk factors. The RSF model predicted the prognosis with a C-index of 0.871 (0.842, 0.900) in training cohort and 0.810 (0.732, 0.888) in test cohort, which was higher than the models built by SSVM model (0.794 [0.753, 0.835] and 0.805 [0.731, 0.879], respectively), XGboost model (0.840 [0.797, 0.883] and 0.799 [0.723, 0.875], respectively) and Cox regression (0.776 [0.727, 0.825] and 0.793 [0.713, 0.873], respectively). NRI and IDI showed that the RSF model exhibited superior performance than the Cox model.
CONCLUSION: Our model introduced seven risk factors that may be useful in predicting the progression of IgAN patients with CKD stage 3 or 4 and proteinuria ≥1.0 g/d. The RSF model is applicable for identifying the progression of IgAN and has outperformed than SSVM, XGboost, and Cox models.