Prediction of prognosis in immunoglobulin a nephropathy patients with focal crescent by machine learning.

BACKGROUND AND OBJECTIVES: Immunoglobulin a nephropathy (IgAN) is the most common primary glomerular disease in the world, with different clinical manifestations, varying severity of pathological changes, common complications of crescent formation in different proportions, and great individual heterogeneous in clinical outcomes. Therefore, we aim to develop a machine learning (ML) based predictive model for predicting the prognosis of IgAN with focal crescent formation and without obvious chronic renal lesions (glomerulosclerosis <25%). MATERIALS: We retrospectively reviewed biopsy-proven IgAN patients in our hospital and cooperative hospital from 2005 to 2017. The method of feature importance of random forest (RF) was applied to conduct feature exploration of feature variables to establish the characteristic variables that are closely related to the prognosis of focal crescent IgAN. Multiple ML algorithms were attempted to establish the prediction models. The area under the precision-recall curve (AUPRC) and the area under the receiver operating characteristic curve (AUROC) were applied to evaluate the predictive performance via three-fold cross validation (namely 2 training sets and 1 validation set). RESULTS: RF was used to screen the important features, the top three of which were baseline estimated glomerular filtration rate (eGFR), serum creatine and triglyceride. Ten important features were selected as important predictors for modeling on the basis of data-driven and medical selection, predictors include: age, baseline eGFR, serum creatine, serum triglycerides, complement 3(C3), proteinuria, mean arterial pressure (MAP) and Hematuria, crescents proportion of glomeruli, Global crescent proportion of glomeruli. In a variety of ML algorithms, the support vector machine (SVM) algorithm displayed better predictive performance, with Precision of 0.77, Recall of 0.77, F1-score of 0.73, accuracy of 0.77, AUROC of 79.57%, and AUPRC of 76.5%. CONCLUSIONS: The SVM model is potentially useful for predicting the prognosis of IgAN patients with focal crescent shape and without obvious chronic renal lesions.

The role of urine IgG in the progression of IgA nephropathy with a high proportion of global glomerulosclerosis.

BACKGROUND: IgA nephropathy (IgAN), the most common glomerulonephritis in the world, is an important cause of end-stage renal disease (ESRD). It is necessary to explore new prognostic markers for predicting the activity and progress of IgAN. There are few studies on new prognostic markers in IgAN patients with high proportion of glomerulosclerosis. This study aims to explore the value of urine IgG in predicting the prognosis of IgAN patients. METHODS: The primary end point of this retrospective study was a composite event with a reduction in estimated glomerular filtration rate (eGFR) of ≥ 50% or ESRD or death. This study assessed the association between urinary IgG and clinicopathological parameters, as well as the prognosis of a high proportion of patients with global glomerulosclerotic IgAN. RESULTS: This study included 105 IgAN patients with high proportion of global glomerulosclerotic. The level of urinary protein IgG was significantly correlated with clinical prognostic factors. The level of urinary protein IgG was positively correlated with urinary protein excretion (rs = 0.834, P < 0.001), CRP (rs = 0.375, P < 0.001), and C4 (rs = 0.228, P = 0.019), and negatively correlated with eGFR (rs = - 0.307, P = 0.001). In addition, the level of urinary IgG increased with the increase of tubulointerstitial injury rate, which was positively correlated with endothelial cell proliferation and crescent (all P < 0.05). Prognostic analysis using the Cox proportional hazard regression model and Kaplan-Meier survival curve further determined that urine IgG is an independent risk factor for the prognosis of IgAN with high proportion of global glomerulosclerosis. CONCLUSIONS: This study determined that urine IgG can be used as a useful predictor of the prognosis of IgAN patients with high proportion global glomerulosclerosis. The mechanism of urine IgG trends in IgAN with high proportion of glomerulosclerosis needs further study.