Development and validation of a predictive model for the progression of diabetic kidney disease to kidney failure.

Renal Failure
Yaqi ChengZhanzheng Zhao

Abstract

Introduction: A good prediction model plays an important role in determining the progression to diabetic kidney disease. We aimed to create a model to predict progression to kidney failure in patients with diabetic kidney disease.Methods: We retrospectively assessed 641 patients with type 2 diabetic kidney disease as derivation cohort and 280 patients as external out time validation cohort. We used a combination of clinical guidance and univariate logistic regression to select the relevant variables. We calculated the discrimination and calibration of different models. The best model was selected according to the optimal combination of discrimination and calibration.Results: During the 3 years follow up, there were 272 outcomes (42%) in derivation cohort and 138 outcomes (49%) in external validation cohort. The final variables selected in the multivariate logistics regression were age, gender, hemoglobin, NLR, serum cystatin C, eGFR, 24-h urine protein, and the use of oral hypoglycemic drugs. We developed four different models as clinical, laboratory, lab-medication, and full models according to these independent risk factors. Laboratory model performed well in both discrimination and calibration among all the models (C-statist...Continue Reading

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KFRE
R Project for Statistical Computing
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