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01.12.2012 | Research article | Ausgabe 1/2012 Open Access

BMC Medical Informatics and Decision Making 1/2012

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

Zeitschrift:
BMC Medical Informatics and Decision Making > Ausgabe 1/2012
Autoren:
Jing Gao, Yong Wang, Zhennan Dong, Zhangming Yan, Xingwang Jia, Yaping Tian
Wichtige Hinweise

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.

Competing interests

The authors have no competing interests to declare.

Authors’ contributions

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.

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.
Zusatzmaterial
Additional file 1: PATIENT RESEARCH CONSENT FORM. (DOC 26 KB)
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Additional file 2: Table S1. Hispathologic diagnosis. (XLS 38 KB)
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Additional file 3: Table S2. Results of T test and U test of 57 biologic parameters. (DOC 144 KB)
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Additional file 4: Table S3. C statistics in ROC curves of 57 biologic parameters. (DOC 144 KB)
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Additional file 5: Table S4. Effects on the basic models by adding the 12 pre-select biological parameters. (DOC 48 KB)
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Authors’ original file for figure 1
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Authors’ original file for figure 2
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Authors’ original file for figure 3
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Authors’ original file for figure 4
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Authors’ original file for figure 5
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Authors’ original file for figure 6
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