Electronic supplementary material
The online version of this article (doi:10.1186/1472-6947-12-58) contains supplementary material, which is available to authorized users.
The authors have no competing interests to declare.
All of the authors read and approved the final manuscript. GJ was in charge of testing the parameters, analyzing the data, and preparing the first draft of the manuscript; WY established the inclusion and exclusion criteria for the study, and determined the study groups to which the selected cases belonged to, according to the histopathologic diagnosis; DZN wrote and completed the first draft of this manuscript; YZM completed the analysis of the net reclassification improvement; JXW collected the cases and samples; TYP planned and designed the study. All authors read and approved the final manuscript.
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.
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.
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.
These models possess potential clinical applications in distinguishing IgAN from other primary kidney diseases.