Machine learning in predicting T-score in the Oxford classification system of IgA nephropathy - Xu, Lin-Lin; Zhang, Di; Weng, Hao-Yi; Wang, Li-Zhong; Chen, Ruo-Yan; Chen, Gang; Shi, Su-Fang; Liu, Li-Jun; Zhong, Xu-Hui; Hong, Shen-Da; Duan, Li-Xin; Lv, Ji-Cheng; et al. (Frontiers in Immunology, 2023)
Background: Immunoglobulin A nephropathy (IgAN) is one of the leading causes of end-stage kidney disease (ESKD). Many studies have shown the significance of pathological manifestations in predicting the outcome of patients with IgAN, especially T score of Oxford classification. Evaluating prognosis may be hampered in patients without renal biopsy.A baseline dataset of 690 patients with IgAN and an independent follow-up dataset of 1168 patients were used as training and testing sets to develop the pathology T score prediction (Tpre) model based on the stacking algorithm, respectively. The 5-year ESKD prediction models using clinical variables (base model), clinical variables and real pathological T score (base model plus Tbio), and clinical variables and Tpre (base model plus Tpre) were developed separately in 1168 patients with regular follow-up to evaluate whether the Tpre could assist in predicting ESKD. In addition, an external validation set containing 355 patients was used to evaluate the performance of the 5-year ESKD prediction model using Tpre.The features selected by AUCRF for the Tpre model included age, systolic arterial pressure, diastolic arterial pressure, proteinuria, eGFR, serum IgA, and uric acid. The AUC of the Tpre was 0.82 (95% CI: 0.80-0.85) in an independent testing set. For the 5-year ESKD prediction model, the AUC of the base model was 0.86 (95% CI: 0.75-0.97). When the Tbio was added into the base model, an increase in AUC (from 0.86 (95% CI: 0.75-0.97) to 0.92 (95% CI: 0.85-0.98); P = 0.03). There was no difference in AUC between the base model plus Tpre and the base model plus Tbio (0.90 (95% CI: 0.82-0.99) vs. 0.92 (95% CI: 0.85-0.98), P = 0.52). The AUC of 5year ESKD prediction model using Tpre was 0.93 (95% CI: 0.87-0.99) in the external validation set.A pathology T score prediction (Tpre) model using routine clinical characteristics was constructed, which could predict the pathological severity and assist clinicians to predict the prognosis of IgAN patients lacking kidney pathology scores.