Machine learning in predicting T-score in the Oxford classification system of IgA nephropathy

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 1,168 patients were used as training and testing sets to develop the pathology T-score prediction (T _pre) 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 T _bio), and clinical variables and T _pre (base model plus T _pre) were developed separately in 1,168 patients with regular follow-up to evaluate whether T _pre could assist in predicting ESKD. In addition, an external validation set consisting of 355 patients was used to evaluate the performance of the 5-year ESKD prediction model using T _pre.

The features selected by AUCRF for the T _pre model included age, systolic arterial pressure, diastolic arterial pressure, proteinuria, eGFR, serum IgA, and uric acid. The AUC of the T _pre 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 T _bio was added to the base model, there was 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 T _pre and the base model plus T _bio [0.90 (95% CI: 0.82-0.99) vs. 0.92 (95% CI: 0.85-0.98), P = 0.52]. The AUC of the 5-year ESKD prediction model using T _pre was 0.93 (95% CI: 0.87-0.99) in the external validation set.

A pathology T-score prediction (T _pre) 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.