Increasingly, nomogram is used by clinicians to estimate and predict the risk of disease for patients, and include easy-to-use digital interfaces that improve diagnostic and predictive efficiency by incorporating multiple independent predictors. In this study, we developed and validated a non-invasive diagnostic prediction model that integrates 5 independent features (IgA, ALB, P, 24hUpro and age) for the prediction of IgAN in primary GN. This model displayed an excellent level of discrimination and a high AUC of 0.89. Besides, the model was validated by 5-fold cross validation with an AUC of 0.88. The independent test also certified a good level of discrimination with an AUC of 0.82. The calibration plot indicated good consistency between the actual and predicted diagnoses.
Currently, renal biopsy still is the gold standard for clinical diagnosis and evaluation the degrees of IgA nephropathy. However, renal biopsy is an invasive method and is not suitable to biopsy patients with mild disease and in countries and districts lacking screening programs disease. Therefore, study of non-invasive clinical features related to the diagnosis of IgAN and development of non-invasive diagnosis model have important clinical significance for the early diagnosis of IgAN. Due to the lack of non-invasive methods to predict IgAN, we developed a model with non-invasive clinical features to predict IgAN and used nomogram to calculate risk for individual patient.
As early as in 1995, the joint committee of the special study group on progressive glomerular diseases, the Ministry of Health and Welfare of Japan, and the Japanese Society of Nephrology reported serum IgA of more than 350 mg/dl in adults as one of the diagnostic criteria for IgA nephropathy. Since then, several researchers have reported serum IgA as diagnostic and prognostic markers of IgAN[19, 20]. Recently, studies have found that serum IgA/C3 ratio had better diagnostic and prognostic value for IgAN[8, 21, 22]. At the beginning of the model building, we also considered including serum IgA/C3 ratio in the nomogram model, but unfortunately it did not improve the diagnostic value of the model. So, in this nomogram, serum IgA was included instead of IgA/C3 ratio.
Galactose deficient IgA1 (Gd-IgA1) is a critical molecule in the pathogenesis of IgAN. Galactose deficiency of O-linked glycans in the hinge region of IgA1 is the beginning of a sequence of events that may lead to renal injuries. The formation of galactose-deficient IgA1 (Gd-IgA1) is the pivotal step of multi-hit pathogenesis of IgAN. Meanwhile, Gd-IgA1 was suggested as a potential disease-specific biomarker that predicts disease activity and prognosis[25, 26]. However, due to the limitation of testing methods, Gd-IgA1 has not been used as a routine test in clinic. So, Gd-IgA1 was not considered to be included in this nomogram model.
In this study, serum IgG levels is significantly increased in IgAN patients compared with non-IgAN patients. Serum IgG concentration at baseline is a predictive marker for the prognosis of IgAN. Serum IgG level is an independent risk factor for poor outcomes in IgAN at the time of renal biopsy. Every 1 g/L decrease in serum IgG level was associated with a 1.74-fold increased risk of the incidence of composite renal outcomes. Furthermore, IgG deposits in the mesangium and capillary loops predict adverse renal outcome in patients with IgAN. However, serum IgG level did not distinguish IgAN well from primary GN and not improve the efficiency of the diagnostic model.
The strength of our study suggested by including few features and applying a non-invasive model we can obtain a good prediction power, thus minimize the tests needed for patients and could easily be adopted in clinical practice. In addition, we divided eligible patients into training and validation datasets using two independent set to evaluate the multivariate logistic regression model both internally and externally. In addition, the predictive power of the novel model was very high (AUC of 0.82) in independent test cohorts, suggesting its high diagnostic value.
However, this study had several limitations. Firstly, the model has been developed and validated in monocentric retrospective cohort of primary GN patients. So, multicenter retrospective cohort study is needed to verify the generalizability of the results in other primary GN patients. Second, the retrospective study design could not exclude the confounding effects. Further prospective cohort study needed to be done to increase the generalizability and deal with confounding effect.
In conclusion, this study developed a nomogram that incorporating IgAN, ALB, P, 24hUpro, and age. Our nomogram displayed excellent performance in both training and validation sets for predicting IgAN from primary GN, and thereby can be used conveniently either in triage or as a replacement for renal biopsy to facilitate the individualized prediction of IgAN diagnosis in patients with primary GN.