@article{SchneiderSchneiderScharnagletal.2013,
  author    = {Schneider, Andreas and Schneider, Markus P. and Scharnagl, Hubert and Jardine, Alan G. and Wanner, Christoph and Drechsler, Christiane},
  title     = {Predicting erythropoietin resistance in hemodialysis patients with type 2 diabetes},
  series = {BMC Nephrology},
  volume    = {14},
  journal   = {BMC Nephrology},
  number    = {67},
  doi       = {10.1186/1471-2369-14-67},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-128695},
  year      = {2013},
  abstract  = {Background: Resistance to ESAs (erythropoietin stimulating agents) is highly prevalent in hemodialysis patients with diabetes and associated with an increased mortality. The aim of this study was to identify predictors for ESA resistance and to develop a prediction model for the risk stratification in these patients. Methods: A post-hoc analysis was conducted of the 4D study, including 1015 patients with type 2 diabetes undergoing hemodialysis. Determinants of ESA resistance were identified by univariate logistic regression analyses. Subsequently, multivariate models were performed with stepwise inclusion of significant predictors from clinical parameters, routine laboratory and specific biomarkers. Results: In the model restricted to clinical parameters, male sex, shorter dialysis vintage, lower BMI, history of CHF, use of ACE-inhibitors and a higher heart rate were identified as independent predictors of ESA resistance. In regard to routine laboratory markers, lower albumin, lower iron saturation, higher creatinine and higher potassium levels were independently associated with ESA resistance. With respect to specific biomarkers, higher ADMA and CRP levels as well as lower Osteocalcin levels were predictors of ESA resistance. Conclusions: Easily obtainable clinical parameters and routine laboratory parameters can predict ESA resistance in diabetic hemodialysis patients with good discrimination. Specific biomarkers did not meaningfully further improve the risk prediction of ESA resistance. Routinely assessed data can be used in clinical practice to stratify patients according to the risk of ESA resistance, which may help to assign appropriate treatment strategies.},
  language  = {en}
}