Development and validation of a nomogram for predicting the interstitial fibrosis and tubular atrophy in patients with lupus nephritis.

OBJECTIVE

Interstitial fibrosis and tubular atrophy (IFTA) were frequent histologic features of LN, and LN patients with IFTA have poor renal outcomes. In this study, we aimed to construct prediction models for the IFTA in LN patients.

METHODS

This retrospective study included 303 patients with biopsy-proven LN at the Affiliated Hospital of Qingdao University and Fujian Medical University Union Hospital. The participants were randomly divided into development and validation cohorts. They were further divided into IFTA and non-IFTA groups. The least absolute shrinkage and selection operator (LASSO) regression model with laboratory test results collected at the time of kidney biopsy was used to optimize feature selection for the risk model. Multivariable logistic regression analysis was applied to build a predicting model incorporating the feature selected in the LASSO regression model. Discrimination, calibration, and clinical usefulness of the predicting model were assessed using the C-index, calibration plot, and receiver operating characteristic curve analysis. Internal validation was assessed using the bootstrapping validation. A nomogram for individual assessment was constructed based on the preferable model.

RESULTS

Predictors contained in the prediction nomogram included age, BMI, mean arterial pressure, log antinuclear antibody (logANA), C3, estimated glomerular filtration rate and serum uric acid. The model displayed good discrimination with a C-index of 0.794 (95% CI 0.734-0.854) and good calibration. High C-index value of 0.857 (95% CI 0.776-0.938) could still be reached in the interval validation. A nomogram model based on the LASSO model was created for producing a probability score of IFTA in LN patients.

CONCLUSION

With excellent predictive abilities, the nomogram may provide a simple and reliable tool to distinguish LN patients with IFTA and help physicians make clinical decisions in their comprehensive assessment.