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

Huifang Wang; Qiuping Ye; Lifang Liu; Qiaoling Chen; Lixin Wei

doi:10.1093/rheumatology/keae509

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

Rheumatology (Oxford). 2024 Sep 17:keae509. doi: 10.1093/rheumatology/keae509. Online ahead of print.

Authors

Huifang Wang^{1

2

3}, Qiuping Ye^{1

2}, Lifang Liu^{1

2}, Qiaoling Chen^{1

2}, Lixin Wei^{1

2}

Affiliations

¹ Department of Nephrology, Fujian Medical University Union Hospital, Fuzhou, China.
² Fujian Institute of Clinical Immunology, Fuzhou, China.
³ Department of Nephrology, The Affiliated Hospital of Qingdao University, Qingdao, China.

PMID: 39288328
DOI: 10.1093/rheumatology/keae509

Abstract

Objective: Interstitial fibrosis and tubular atrophy (IFTA) were frequent histologic features of lupus nephritis (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 Union Hospital of Fujian Medical University. 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 ROC 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, body mass index (BMI), mean arterial pressure (MAP), logANA, C3, eGFR 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 helps physicians make clinical decisions in their comprehensive assessment.

Keywords: Interstitial fibrosis and tubular atrophy; Kidney biopsy; Lupus nephritis; Nomogram; Prediction.