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10.1016/j.kint.2017.02.010

http://scihub22266oqcxt.onion/10.1016/j.kint.2017.02.010
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C5796779!5796779!28396115
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suck abstract from ncbi


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pmid28396115      Kidney+Int 2017 ; 92 (1): 258-66
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  • Improved clinical trial enrollment criterion to identify patients with diabetes at risk of end stage renal disease #MMPMID28396115
  • Yamanouchi M; Skupien J; Niewczas MA; Smiles A; Doria A; Stanton RC; Galecki A; Duffin KL; Pullen N; Breyer MD; Bonventre JV; Warram JH; Krolewski AS
  • Kidney Int 2017[Jul]; 92 (1): 258-66 PMID28396115show ga
  • Design of Phase III trials in diabetic nephropathy currently requires patients at high risk of progression defined as within three years of a hard end-point (end stage renal disease, 40% loss of estimated glomerular filtration rate, or death). To improve design of these trials, we used natural history data from the Joslin Kidney Studies of chronic kidney disease in patients with diabetes to develop an improved criterion to identify such patients. This included a training cohort of 279 patients with type 1 diabetes and 134 end-points within three years, and a validation cohort of 221 patients with type 2 diabetes and 88 end-points. Previous trials selected patients using clinical criteria for baseline urinary albumin to creatinine ratio and estimated glomerular filtration rate. Application of these criteria to our cohort data yielded sensitivities (detection of patients at risk) of 70-80% and prognostic values of only 52-63%. We applied Classification and Regression Trees analysis to select from among all clinical characteristics and markers the optimal prognostic criterion that divided patients with type 1 diabetes according to risk. The optimal criterion was a serum Tumor Necrosis Factor Receptor 1 level over 4.3 ng/ml alone or 2.9-4.3 ng/ml with an albumin to creatinine ratio over 1.9 g/g. Remarkably, this criterion produced similar results in both type 1 and type 2 diabetic patients. Overall, sensitivity and prognostic value were high (72% and 81%, respectively). Thus, application of this criterion to enrollment in future clinical trials could reduce sample size required to achieve adequate statistical power for detection of treatment benefits.
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