GDF-15 and sST-2 act as biomarkers of disease severity but not independent predictors in idiopathic membranous nephropathy

Development of a nomogram for membranous nephropathy prediction in patients with primary Sjögren's syndrome: a 6-year retrospective study

Objectives

Nephritis is a life-threatening complication of primary Sjögren's syndrome (pSS), with membranous nephropathy (MN) being prevalent. Renal biopsy is the gold standard for MN diagnosis, but it is invasive and cannot be repeatedly performed. This study aimed to develop a nomogram for the prediction of MN in patients with pSS.

Methods

This retrospective study included patients with pSS admitted to the Rheumatology and Immunology Department of the First Affiliated Hospital of China Medical University between January 2015 and January 2021. A nomogram was developed using multivariable logistic regression analysis and evaluated using receiver operating characteristic (ROC) curve analysis. Bootstrap resampling analysis (1,000 times) was performed to evaluate the nomogram for discrimination and the calibration curve for consistency.

Results

A total of 237 patients with pSS [aged 53.00 (44.00, 61.00) years] were included, with 35 pSS-MN patients. Based on clinical practice and multivariable logistic regression analysis, seven variables associated with pSS-MN were selected, including white blood cells, creatine, complement 3, rheumatoid factor, antinuclear antibodies, anti-SSA antibody, and interstitial lung disease. The area under the ROC curve was 0.860 (95% confidence interval: 0.796-0.919), indicating good predictive power. In addition, the nomogram exhibited excellent performance, as demonstrated by the calibration curve and decision curve analysis.

Conclusion

This study developed a risk prediction nomogram for MN in patients with pSS, with high predictive power. It may be used to improve the management of patients with pSS.

Prediction Models of Primary Membranous Nephropathy: A Systematic Review and Meta-Analysis

BACKGROUND

Several statistical models for predicting prognosis of primary membranous nephropathy (PMN) have been proposed, most of which have not been as widely accepted in clinical practice.

METHODS

A systematic search was performed in MEDLINE and EMBASE. English studies that developed any prediction models including two or more than two predictive variables were eligible for inclusion. The study population was limited to adult patients with pathologically confirmed PMN. The outcomes in eligible studies should be events relevant to prognosis of PMN, either disease progression or response profile after treatments. The risk of bias was assessed according to the PROBAST.

RESULTS

In all, eight studies with 1237 patients were included. The pooled AUC value of the seven studies with renal function deterioration and/or ESRD as the predicted outcomes was 0.88 (95% CI: 0.85 to 0.90; I² = 77%, p = 0.006). The paired forest plots for sensitivity and specificity with corresponding 95% CIs for each of these seven studies indicated the combined sensitivity and specificity were 0.76 (95% CI: 0.64 to 0.85) and 0.84 (95% CI: 0.80 to 0.88), respectively. All seven studies included in the meta-analysis were assessed as high risk of bias according to the PROBAST tool.

CONCLUSIONS

The reported discrimination ability of included models was good; however, the insufficient calibration assessment and lack of validation studies precluded drawing a definitive conclusion on the performance of these prediction models. High-grade evidence from well-designed studies is needed in this field.