Abstract: SA-PO302
Noninvasive Prediction Tool of Nondiabetic Kidney Diseases in Patients with Type 2 Diabetes
Session Information
- Diabetic Kidney Disease: Clinical Pathology, Diagnostic and Treatment Advances
October 26, 2024 | Location: Exhibit Hall, Convention Center
Abstract Time: 10:00 AM - 12:00 PM
Category: Diabetic Kidney Disease
- 702 Diabetic Kidney Disease: Clinical
Authors
- Prasad, Narayan, Sanjay Gandhi Post Graduate Institute of Medical Sciences, Lucknow, Uttar Pradesh, India
- Veeranki, Vamsidhar, Sanjay Gandhi Post Graduate Institute of Medical Sciences, Lucknow, Uttar Pradesh, India
- Meyyappan, Jeyakumar, Sanjay Gandhi Post Graduate Institute of Medical Sciences, Lucknow, Uttar Pradesh, India
- Yadav, Brijesh, Sanjay Gandhi Post Graduate Institute of Medical Sciences, Lucknow, Uttar Pradesh, India
Background
Despite being the gold standard in detecting non-diabetic kidney diseases(NDKD) in Type-2 Diabetes Mellitus, renal biopsy has an inherent risk of life-threatening complications.The current study is aimed to develop and validate a non-invasive scoring tool to predict NDKD using clinical and laboratory variables.
Methods
We developed a model to detect NDKD using multivariable binary logistic regression analysis with the backward Wald elimination method. A nomogram was developed from the multivariate logistic regression model and the probability of NDKD was assessed for each predictor variable using the strength of association. We included all patients of T2DM who had an indication kidney biopsy for NDKD during the study period. The model performance was analyzed using AUC-ROC curve on both the derivational (internal validation) and external cohorts from other centers (multicentric external validation).
Results
Out of 538 patients, 376 were included in the derivational, and 162 in the validation cohort. The model consists of the following variables, T2DM duration< 5 years(p=0.003),absence of coronary artery disease(p=0.05),absence of diabetic retinopathy(p=0.001), oliguria(p=0.02), acute rise in s.creatinine(p< 0.001) and low serum C3 level(p=0.001) significantly predicted NDKD on renal biopsy. Using a nomogram, statistical predictive models are converted into a single numerical estimate of probability of NDKD in the form of a graph(Figure.1,2).The model performance performed robustly with an AUC-ROC of 0.869(95% CI:0.805-0.933) in the validation cohort and 0.883(95%CI:0.830–0.937) on multicentric external validation.
Conclusion
The clinical and laboratory parameter-based non-invasive prediction model robustly predicted the NDKD among T2DM patients with renal dysfunction, and the prediction model has a high sensitivity of 86% and an equally good specificity of 80%.
Figure.1
The nomogram depicting the statistical predictive models converted into a single numerical estimate of probability of NDKD in the form of a graph.
Funding
- Government Support – Non-U.S.