BMC Med Inform Decis Mak - A novel differential diagnostic model based on multiple biological parameters for immunoglobulin A nephropathy.

Tópicos

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Resumo

CKGROUND: 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.

Resumo Limpo

ckground immunoglobulin nephropathi igan common form glomerulonephr china accur diagnosi igan depend renal biopsi lack noninvas practic classif method discrimin igan primari kidney diseas object studi develop classif model auxiliari diagnosi igan use multiparamet analysi various biolog parametersmethod establish optim classif model case igan vs nonigan recruit statist analyz model valid anoth casesresult biolog paramet paramet signific differ p igan nonigan combin fibrinogen serum immunoglobulin level manifest found signific predict igan valid accuraci logist regress discrimin analysi model respect predict probabl cutoff respect predict probabl cutoff predict probabl equat contain combin fibrinogen serum iga level manifest patient least probabl igan predict probabl lower patient least probabl nonigan result net reclassif improv certif serum immunoglobulin fibrinogen classif power discrimin igan noniganconclus model possess potenti clinic applic distinguish igan primari kidney diseas

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