Abstract
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%). 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). 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%. The SVM model is potentially useful for predicting the prognosis of IgAN patients with focal crescent shape and without obvious chronic renal lesions.
Highlights
IgA nephropathy (IgAN) is the most common primary glomerular disease in the world, accounting for more than 40% of primary glomerular diseases
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
The support vector machine (SVM) model is potentially useful for predicting the prognosis of IgAN patients with focal crescent shape and without obvious chronic renal lesions
Summary
IgA nephropathy (IgAN) is the most common primary glomerular disease in the world, accounting for more than 40% of primary glomerular diseases. The variable of crescent is not included in the two prediction models recommended by the 2021 KDIGO guidelines, due to the crescent is highly related to race/ethnicity and the use of immunosuppressants after biopsy [6] Of note, this problem could be solved by the importance ranking function of RFs, from which the importance score of each factor would be applied to reflect its own contribution. The predictive value of clinicopathological parameters should be explored to predict ESRD for a more suitable prective model for Chinese IgAN patients. This indicates that there is a clear need for a predictive model that includes crescents as an important predictor to predict disease progression in IgAN. The area under the precisionrecall 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)
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