JMIR Publications

ABSTRACT

Background:

Severe tubular atrophy/interstitial fibrosis are critical pathological features associated with poor prognosis in IgA nephropathy (IgAN). Early identification of patients at high risk for severe tubular damage could guide clinical management and improve outcomes.

Objective:

To construct and validate a predictive model for assessing the risk of severe tubular atrophy and interstitial fibrosis in patients diagnosed with IgAN.

Methods:

A total of 3,276 patients from the Hankou branch of Tongji Hospital were retrospectively enrolled for model development. A predictive model for severe tubular atrophy/interstitial fibrosis was constructed based on independent predictors identified through univariate analysis, LASSO regression, and stepwise logistic regression. Further, the model underwent internal and external validation and performance evaluation using six machine learning algorithms: random forest, generalized linear model, decision tree, gradient boosting decision tree, extreme gradient boosting, and support vector machine.

Results:

In this study, 8 variables were identified as independent predictors and used to construct a predictive model for severe tubular atrophy/interstitial fibrosis: Logit (P) = 0.011 × age (years) + 0.324 × hypertension history – 0.302 × education + 0.111 × RDW-CV – 0.152 × direct bilirubin (μmol/L) + 0.003 × uric acid (μmol/L) – 0.021 × eGFR (ml/min/1.73 m²) + 1.151 × ln(24-hour urine microalbumin) (mg/24h). The AUC for the predictive model was 0.860. The AUCs of the six machine learning algorithms ranged from 0.793 to 0.880 in internal validation and from 0.785 to 0.862 in external validation.

Conclusions:

We developed a concise and clinically useful model for predicting severe tubular atrophy/interstitial fibrosis in IgA nephropathy. It offers a non-invasive tool for risk assessment when biopsy is not feasible, aiding personalized treatment decisions.