A novel differential diagnostic model based on multiple biological parameters for immunoglobulin A nephropathy
Autor: | Yong Wang, Zhennan Dong, Zhangming Yan, Yaping Tian, Xingwang Jia, Jing Gao |
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Jazyk: | angličtina |
Rok vydání: | 2012 |
Předmět: |
Immunoglobulin A
Adult Male medicine.medical_specialty Biopsy Health Informatics Logistic regression Fibrinogen Kidney urologic and male genital diseases lcsh:Computer applications to medicine. Medical informatics Gastroenterology Models Biological Sensitivity and Specificity Diagnosis Differential Young Adult Internal medicine medicine Humans Primary kidney disease Retrospective Studies Multiparameter analysis biology medicine.diagnostic_test business.industry Health Policy Retrospective cohort study Glomerulonephritis Glomerulonephritis IGA IgA nephropathy Middle Aged medicine.disease Linear discriminant analysis Computer Science Applications Immunology biology.protein lcsh:R858-859.7 Female Differential diagnosis business Biomarkers medicine.drug Research Article |
Zdroj: | BMC Medical Informatics and Decision Making, Vol 12, Iss 1, p 58 (2012) BMC Medical Informatics and Decision Making |
ISSN: | 1472-6947 |
Popis: | BackgroundImmunoglobulin 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.MethodsTo 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.ResultsOf the 57 biological parameters, there were 16 parameters that were significantly different (P ConclusionsThese models possess potential clinical applications in distinguishing IgAN from other primary kidney diseases. |
Databáze: | OpenAIRE |
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