BACKGROUND: Renal diseases in diabetes include diabetic nephropathies (DN) and non-diabetic renal diseases (NDRD). The clinical differentiation between these two categories is usually not so clear and effective. This study aims to develop a quantified differential diagnostic model. METHODS: We consecutively screened the diabetic patients with overt proteinuria but no severe renal failure for kidney biopsy from 1993 to 2003. The finally enrolled 110 patients were divided into two groups according to pathological features (60 in DN group and 50 in NDRD group). Clinical and laboratory data were compared between two groups. Then a diagnostic model was developed based on the logistic regression analysis. RESULTS: Forty-six percent of patients were NDRD including a variety of pathological types. Many differences between DN and NDRD were found by comparison of the clinical indices. In the final logistic regression analysis, only diabetes duration (Dm), systolic blood pressure (Bp), HbA1c (Gh), haematuria (Hu) and diabetic retinopathy (Dr) showed statistical significance. Based on the logistic regression model: pi = e(z)/(1 + e(z)), a diagnostic model was constructed as follows: P(DN) = exp(-13.5922 + 0.0371Dm + 0.0395Bp + 0.3224Gh - 4.4552Hu + 2.9613Dr)/ [1 + exp(-13.5922 + 0.0371Dm + 0.0395Bp + 0.3224Gh - 4.4552Hu + 2.9613Dr)]. P(DN) was the probability of DN diagnosis (P(DN) >or= 0.5 as DN, P(DN) < 0.5 as NDRD). Validation tests showed that this model had good sensitivity (90%) and specificity (92%). CONCLUSIONS: This diagnostic model may be helpful to clinical differentiation of DN and NDRD in type 2 diabetic patients with overt proteinuria.
BACKGROUND:Renal diseases in diabetes include diabetic nephropathies (DN) and non-diabetic renal diseases (NDRD). The clinical differentiation between these two categories is usually not so clear and effective. This study aims to develop a quantified differential diagnostic model. METHODS: We consecutively screened the diabeticpatients with overt proteinuria but no severe renal failure for kidney biopsy from 1993 to 2003. The finally enrolled 110 patients were divided into two groups according to pathological features (60 in DN group and 50 in NDRD group). Clinical and laboratory data were compared between two groups. Then a diagnostic model was developed based on the logistic regression analysis. RESULTS: Forty-six percent of patients were NDRD including a variety of pathological types. Many differences between DN and NDRD were found by comparison of the clinical indices. In the final logistic regression analysis, only diabetes duration (Dm), systolic blood pressure (Bp), HbA1c (Gh), haematuria (Hu) and diabetic retinopathy (Dr) showed statistical significance. Based on the logistic regression model: pi = e(z)/(1 + e(z)), a diagnostic model was constructed as follows: P(DN) = exp(-13.5922 + 0.0371Dm + 0.0395Bp + 0.3224Gh - 4.4552Hu + 2.9613Dr)/ [1 + exp(-13.5922 + 0.0371Dm + 0.0395Bp + 0.3224Gh - 4.4552Hu + 2.9613Dr)]. P(DN) was the probability of DN diagnosis (P(DN) >or= 0.5 as DN, P(DN) < 0.5 as NDRD). Validation tests showed that this model had good sensitivity (90%) and specificity (92%). CONCLUSIONS: This diagnostic model may be helpful to clinical differentiation of DN and NDRD in type 2 diabeticpatients with overt proteinuria.
Authors: E C Yenigun; F Dede; R Ozturk; D Turgut; E Koc; S V Piskinpasa; N Ozkayar; A R Odabas Journal: Hippokratia Date: 2015 Apr-Jun Impact factor: 0.471
Authors: Giorgina B Piccoli; Giorgio Grassi; Gianfranca Cabiddu; Marta Nazha; Simona Roggero; Irene Capizzi; Agostino De Pascale; Adriano M Priola; Cristina Di Vico; Stefania Maxia; Valentina Loi; Anna M Asunis; Antonello Pani; Andrea Veltri Journal: Rev Diabet Stud Date: 2015-08-10