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[Agency] AHRQ-Indian Health Service [Project Title] Individualized Treatment Strategies and Optimal Hematocrit Target for complex Dialysis Patient Background [Institution] Medical Technology and Practice Patterns [Amount] $0 [Summary] Almost all renal failure patients suffer from anemia as an important co- morbid condition. Epoetin therapy, approved by the FDA in 1989, is the mainstay of treatment for anemia among this population. After almost 20 years, key questions regarding the effectiveness of epoetin therapy remain unanswered. In particular, the role of comorbid conditions highly prevalent among renal failure patients with anemia is unknown. Do patients with co-morbidities including cardiovascular disease and diabetes require a different therapeutic endpoint given several recent randomized trials and Black Box warnings by the FDA to use the lowest dose of epoetin possible. Currently, there appears to be uncertainty in the nephrology community as to the optimal target hematocrit and epoetin dosing protocols for various patient groups. Objectives. The objective of our proposed study is to address the key question: What is the optimal treatment strategy based on the presence of co-morbidities such as cardiovascular disease and diabetes? The only existing epoetin RCT for the dialysis population suggests that patients with cardiovascular disease should not be targeted to normal hematocrit levels. Dialysis patients with diabetes have more severe anemia and are more resistant to treatment. To address this research question, we will also examine how does the presence of such comorbidities affect patient responsiveness to epoetin therapy? Specifically, for complex patients who are responsive to epoetin therapy (and use lower than average dose), does targeting higher than FDA-recommended hematocrits lead to better outcomes? For patients with co-morbid conditions who are not responsive (with low hematocrits and high doses), does administering high doses to target the FDA- recommended hematocrit lead to worse outcomes? Finally, given the presence of diabetes or cardiovascular disease, what is the optimal treatment strategy further disaggregated further by patient demographics such as race, gender and age affect outcome? Methods. We propose to apply causal analytical methods (inverse probability weighting models) that, unlike standard statistical methods, appropriately adjust for time-dependent confounders that are affected by prior treatment. The proposed methods are therefore well suited to address key questions concerning anemia management and mortality of dialysis patients. Significance. To date, limited RCTs and observational studies have been disaggregated by patient co-morbidities when evaluating patient outcomes in the renal failure population. The continuing use of a single hematocrit target range and dosing protocols for all dialysis patients ignores individual patient needs of those with comorbid diabetes or cardiovascular disease. Causal inference techniques have been developed and validated using randomized clinical trial data in other treatment areas. In this grant, we propose to apply these innovative techniques using Medicare administrative data to provide a basis for improved patient outcomes using 'individualized' guidelines based on common patient comorbid characteristics. Study findings might provide the basis for improved clinical guidelines and more cost-effective payer policies. [Completion/ Closing Date] 10/02/2010 [Link] link [Category] 
Medical treatments, items and services
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