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

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 -score of Oxford classification. Evaluating prognosis may be hampered in patients without renal biopsy.

Methods: 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 -score prediction ( ) model based on the stacking algorithm, respectively. The 5-year ESKD prediction models using clinical variables (base model), clinical variables and real pathological -score (base model plus ), and clinical variables and (base model plus ) were developed separately in 1,168 patients with regular follow-up to evaluate whether 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 .

Results: The features selected by AUCRF for the model included age, systolic arterial pressure, diastolic arterial pressure, proteinuria, eGFR, serum IgA, and uric acid. The AUC of the 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 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); = 0.03]. There was no difference in AUC between the base model plus and the base model plus [0.90 (95% CI: 0.82-0.99) . 0.92 (95% CI: 0.85-0.98), = 0.52]. The AUC of the 5-year ESKD prediction model using was 0.93 (95% CI: 0.87-0.99) in the external validation set.

Conclusion: A pathology -score prediction ( ) 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.