A noninvasive artificial neural network model to predict IgA nephropathy risk in Chinese population

Abstract

Renal biopsy is the gold standard for Immunoglobulin A nephropathy (IgAN) but poses several problems. Thus, we aimed to establish a noninvasive model for predicting the risk probability of IgAN by analyzing routine and serological parameters. A total of 519 biopsy-diagnosed IgAN and 211 non-IgAN patients were recruited retrospectively. Artificial neural networks and logistic modeling were used. The receiver operating characteristic (ROC) curve and performance characteristics were determined to compare the diagnostic value between the two models. The training and validation sets did not differ significantly in terms of any variables. There were 19 significantly different parameters between the IgAN and non-IgAN groups. After multivariable logistic regression analysis, age, serum albumin, serum IgA, serum immunoglobulin G, estimated glomerular filtration rate, serum IgA/C3 ratio, and hematuria were found to be independently associated with the presence of IgAN. A backpropagation network model based on the above parameters was constructed and applied to the validation cohorts, revealing a sensitivity of 82.68% and a specificity of 84.78%. The area under the ROC curve for this model was higher than that for logistic regression model (0.881 vs. 0.839). The artificial neural network model based on routine markers can be a valuable noninvasive tool for predicting IgAN in screening practice.

Figure 1

The flow diagram of subjects screening and grouping.

Figure 2

ROC curve of logistic regression modeling for predicting IgAN. (A) Area under the ROC curves were 0.92 in training set. (B) Area under the ROC curves were 0.839 in validation set.

Figure 3

The structure of the artificial neural networks model and BP-ANN training process.

Figure 4

ROC curve of BP-ANN for predicting IgAN. (A) Area under the ROC curves were 0.965 in training set. (B) Area under the ROC curves were 0.881 in validation set.