Machine learning-based diagnostic prediction of IgA nephropathy: model development and validation study

Abstract

IgA nephropathy progresses to kidney failure, making early detection important. However, definitive diagnosis depends on invasive kidney biopsy. This study aimed to develop non-invasive prediction models for IgA nephropathy using machine learning. We collected retrospective data on demographic characteristics, blood tests, and urine tests of the patients who underwent kidney biopsy. The dataset was divided into derivation and validation cohorts, with temporal validation. We employed five machine learning models-eXtreme Gradient Boosting (XGBoost), LightGBM, Random Forest, Artificial Neural Networks, and 1 Dimentional-Convolutional Neural Network (1D-CNN)-and logistic regression, evaluating performance via the area under the receiver operating characteristic curve (AUROC) and explored variable importance through SHapley Additive exPlanations method. The study included 1268 participants, with 353 (28%) diagnosed with IgA nephropathy. In the derivation cohort, LightGBM achieved the highest AUROC of 0.913 (95% CI 0.906-0.919), significantly higher than logistic regression, Artificial Neural Network, and 1D-CNN, not significantly different from XGBoost and Random Forest. In the validation cohort, XGBoost demonstrated the highest AUROC of 0.894 (95% CI 0.850-0.935), maintaining its robust performance. Key predictors identified were age, serum albumin, IgA/C3, and urine red blood cells, aligning with existing clinical insights. Machine learning can be a valuable non-invasive tool for IgA nephropathy.

Keywords: Artificial intelligence; Glomerulonephritis; IgA nephropathy; Kidney biopsy; Machine learning.

Figure 1

Flow diagram of patient selection.

Figure 2

Receiver-operating characteristic curves of the machine learning models in (a) derivation cohort and (b) validation cohort.

Figure 3

Precision-recall curves of the machine learning models in (a) derivation cohort and (b) validation cohort.

Figure 4

Shapley additive explanations beeswarm plots of (a) XGBoost, (b) LightGBM, and (c) Random Forest for prediction of IgA nephropathy. LDH, lactate dehydrogenase; CK, creatine kinase; IgG, immunoglobulin G; IgA, immunoglobulin A; IgA/C3, immunoglobulin A/Complement C3 ratio; Urine RBC, urine red blood cells; UPCR, urine protein to creatinine ratio.