A novel differential diagnostic model based on multiple biological parameters for immunoglobulin A nephropathy

Abstract

Background: Immunoglobulin A nephropathy (IgAN) is the most common form of glomerulonephritis in China. An accurate diagnosis of IgAN is dependent on renal biopsies, and there is lack of non-invasive and practical classification methods for discriminating IgAN from other primary kidney diseases. The objective of this study was to develop a classification model for the auxiliary diagnosis of IgAN using multiparameter analysis with various biological parameters.

Methods: To establish an optimal classification model, 121 cases (58 IgAN vs. 63 non-IgAN) were recruited and statistically analyzed. The model was then validated in another 180 cases.

Results: Of the 57 biological parameters, there were 16 parameters that were significantly different (P < 0.05) between IgAN and non-IgAN. The combination of fibrinogen, serum immunoglobulin A level, and manifestation was found to be significant in predicting IgAN. The validation accuracies of the logistic regression and discriminant analysis models were 77.5 and 77.0%, respectively at a predictive probability cut-off of 0.5, and 81.1 and 79.9%, respectively, at a predictive probability cut-off of 0.40. When the predicted probability of the equation containing the combination of fibrinogen, serum IgA level, and manifestation was more than 0.59, a patient had at least an 85.0% probability of having IgAN. When the predicted probability was lower than 0.26, a patient had at least an 88.5% probability of having non-IgAN. The results of the net reclassification improvement certificated serum Immunoglobulin A and fibrinogen had classification power for discriminating IgAN from non-IgAN.

Conclusions: These models possess potential clinical applications in distinguishing IgAN from other primary kidney diseases.

Figure 1

Respective ROC Curve of sIgA, ALB and Ca between IgAN and non-IgAN. ROC curves for serum immunoglobulin A level (sIgA), ALB (albumin) and Ca (calcium) in immunoglobulin A nephropathy (IgAN) and non-immunoglobulin A nephropathy (non-IgAN) patients. The state variable is IgAN.

Figure 2

Correlation coefficients between two variables of pre-selected variables.

Figure 3

Figure3Area under ROC curve of the predicted probability of IgAN with “FIB + sIgA + Manifestation” combination from logistic regression. Area under the ROC curve for predicting immunoglobulin A nephropathy (IgAN) with the equation derived via logistic regression analysis, which includes the “fibrinogen (FIB) + serum immunoglobulin A level (sIgA) + manifestation” combination. The state variable is IgAN.

Figure 4

Area under ROC curve of the predicted probability of IgAN with “FIB + sIgA + Manifestation” combination from discriminant analysis. Area under the ROC curve for predicting immunoglobulin A nephropathy (IgAN) with the equation derived via discriminant analysis, which includes the “fibrinogen (FIB) + serum immunoglobulin A level (sIgA) + manifestation” combination. The state variable is IgAN.

Figure 5

Misdiagnosis rates of the two models with different cut-offs for the predicted probability.

Figure 6

Decision procedure for the diagnosis of immunoglobulin A (IgA) and non-IgA nephropathy in patients with suspected kidney disease.