ArticleBMC medical informatics and decision making2012
A novel differential diagnostic model based on multiple biological parameters for immunoglobulin A nephropathy.
Article in BMC medical informatics and decision making, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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7 citing papers in PubMed, 13 citations in OpenAlex.
- Machine learning-based diagnostic prediction of IgA nephropathy: model development and validation study.Scientific reports · 2024Article
- Microbiome and metabolome analysis to clarify the interaction between the urine microbiota and serum metabolites in Chinese patients with immunoglobulin A nephropathy.Annals of translational medicine · 2022Article
- A noninvasive artificial neural network model to predict IgA nephropathy risk in Chinese population.Scientific reports · 2022Article
- Urinary peptide panel for prognostic assessment of bladder cancer relapse.Scientific reports · 2019Article
- A non-invasive diagnostic model of immunoglobulin A nephropathy and serological markers for evaluating disease severity.Chinese medical journal · 2019Article
- A pilot and comparative study between pathological and serological levels of immunoglobulin and complement among three kinds of primary glomerulonephritis.BMC immunology · 2018Article
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6 authors at 2 institutions in 1 country.
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Abstract
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 < 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.
conclusionsThese models possess potential clinical applications in distinguishing IgAN from other primary kidney diseases.
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