End‐stage liver disease candidates at the highest model for end‐stage liver disease scores have higher wait‐list mortality than status‐1A candidates

Candidates with fulminant hepatic failure (Status‐1A) receive the highest priority for liver transplantation (LT) in the United States. However, no studies have compared wait‐list mortality risk among end‐stage liver disease (ESLD) candidates with high Model for End‐Stage Liver Disease (MELD) scores to those listed as Status‐1A. We aimed to determine if there are MELD scores for ESLD candidates at which their wait‐list mortality risk is higher than that of Status‐1A, and to identify the factors predicting wait‐list mortality among those who are Status‐1A. Data were obtained from the Scientific Registry of Transplant Recipients for adult LT candidates (n = 52,459) listed between September 1, 2001, and December 31, 2007. Candidates listed for repeat LT as Status‐1 A were excluded. Starting from the date of wait listing, candidates were followed for 14 days or until the earliest occurrence of death, transplant, or granting of an exception MELD score. ESLD candidates were categorized by MELD score, with a separate category for those with calculated MELD > 40. We compared wait‐list mortality between each MELD category and Status‐1A (reference) using time‐dependent Cox regression. ESLD candidates with MELD > 40 had almost twice the wait‐list mortality risk of Status‐1A candidates, with a covariate‐adjusted hazard ratio of HR = 1.96 ( P = 0.004). There was no difference in wait‐list mortality risk for candidates with MELD 36‐40 and Status‐1A, whereas candidates with MELD 20 ( P = 0.6). Conclusion : Candidates with MELD > 40 have significantly higher wait‐list mortality and similar posttransplant survival as candidates who are Status‐1A, and therefore, should be assigned higher priority than Status‐1A for allocation. Because ESLD candidates with MELD 36‐40 and Status‐1A have similar wait‐list mortality risk and posttransplant survival, these candidates should be assigned similar rather than sequential priority for deceased donor LT. (H epatology 2012)Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/89518/1/24632_ftp.pd

Factors that affect deceased donor liver transplantation rates in the United States in addition to the model for end‐stage liver disease score

Under an ideal implementation of Model for End‐Stage Liver Disease (MELD)–based liver allocation, the only factors that would predict deceased donor liver transplantation (DDLT) rates would be the MELD score, blood type, and donation service area (DSA). We aimed to determine whether additional factors are associated with DDLT rates in actual practice. Data from the Scientific Registry of Transplant Recipients for all adult candidates wait‐listed between March 1, 2002 and December 31, 2008 (n = 57,503) were analyzed. Status 1 candidates were excluded. Cox regression was used to model covariate‐adjusted DDLT rates, which were stratified by the DSA, blood type, liver‐intestine policy, and allocation MELD score. Inactive time on the wait list was not modeled, so the computed DDLT hazard ratios (HRs) were interpreted as active wait‐list candidates. Many factors, including the candidate's age, sex, diagnosis, hospitalization status, and height, prior DDLT, and combined listing for liver‐kidney or liver‐intestine transplantation, were significantly associated with DDLT rates. Factors associated with significantly lower covariate‐adjusted DDLT rates were a higher serum creatinine level (HR = 0.92, P < 0.001), a higher bilirubin level (HR = 0.99, P = 0.001), and the receipt of dialysis (HR = 0.83, P < 0.001). Mild ascites (HR = 1.15, P < 0.001) and hepatic encephalopathy (grade 1 or 2, HR = 1.05, P = 0.02; grade 3 or 4, HR = 1.10, P = 0.01) were associated with significantly higher adjusted DDLT rates. In conclusion, adjusted DDLT rates for actively listed candidates are affected by many factors aside from those integral to the allocation system; these factors include the components of the MELD score itself as well as candidate factors that were considered but were deliberately omitted from the MELD score in order to keep it objective. These results raise the question whether additional candidate characteristics should be explicitly incorporated into the prioritization of wait‐list candidates because such factors are already systematically affecting DDLT rates under the current allocation system. Liver Transpl, 2012. © 2012 AASLD.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/95560/1/23548_ftp.pd

Kidney and Pancreas Transplantation in the United States, 1996–2005

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/73647/1/j.1600-6143.2006.01781.x.pd

Prevalence and Outcomes of Multiple-Listing for Cadaveric Kidney and Liver Transplantation

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/73815/1/j.1600-6135.2003.00282.x.pd

Association of Center Volume with Outcome After Liver and Kidney Transplantation

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/73934/1/j.1600-6143.2004.00462.x.pd

Survival Benefit-Based Deceased-Donor Liver Allocation

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/74806/1/j.1600-6143.2009.02571.x.pd

Recovery and Utilization of Deceased Donor Kidneys from Small Pediatric Donors

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/72146/1/j.1600-6143.2006.01353.x.pd

WindPACT Turbine Design Scaling Studies: Technical Area 4 -- Balance-of-Station Cost

DOE's Wind Partnerships for Advanced Component Technologies (WindPACT) program explores the most advanced wind-generating technologies for improving reliability and decreasing energy costs. The first step in the WindPact program is a scaling study to bound the optimum sizes for wind turbines, to define size limits for certain technologies, and to scale new technologies. The program is divided into four projects: Composite Blades for 80-120-meter Rotors; Turbine, Rotor, and Blade Logistics; Self-Erecting Tower and Nacelle Feasibility; and Balance-of-Station Cost. This report discusses balance-of-station costs, which includes the electrical power collector system, wind turbine foundations, communications and controls, meteorological equipment, access roadways, crane pads, and the maintenance building. The report is based on a conceptual 50-megawatt (MW) wind farm site near Mission, South Dakota. Cost comparisons are provided for four sizes of wind turbines: 750 kilowatt (kW), 2.5 MW, 5.0 MW, and 10.0 MW

Factors that affect deceased donor liver transplantation rates in the United States in addition to the model for end‐stage liver disease score

Under an ideal implementation of Model for End-stage Liver Disease (MELD)-based liver allocation, the only factors that would predict DDLT rates are MELD score, blood type, and donation service area (DSA). We aimed to determine whether additional factors are associated with DDLT rates in actual practice. Methods: Data from the Scientific Registry of Transplant Recipients on all adult candidates waitlisted between 03/01/2002 and 12/31/2008 (n=57,503) were analyzed. Status-1 candidates were excluded. Cox regression was used to model covariate-adjusted DDLT rates, stratified by DSA, blood type, liver-intestine policy and allocation MELD. Inactive time on the waitlist was not modeled, such that the computed DDLT hazard ratios (HR) are interpreted as “among actively listed candidates”. Results: Many factors, including candidate age, gender, prior DDLT, diagnosis, hospitalization status, height, and combined listing for liver-kidney and liver-intestine were significantly associated with DDLT rates. Factors associated with significantly lower covariate-adjusted DDLT rates were higher serum creatinine (HR=0.92; p<0.0001); higher bilirubin (HR=0.996; p=0.001), and receipt of dialysis (HR=0.83; p<0.0001). Mild ascites (HR=1.15; p<0.0001) and hepatic encephalopathy (Grade 1–2, HR=1.05; p=0.0236; grade 2–3, HR=1.10, p=0.0103) were associated with significantly higher adjusted DDLT rates. Conclusions: Adjusted DDLT rates among actively listed candidates are affected by many factors aside from those integral to the allocation system; including the components of the MELD score itself, as well as candidate factors that were considered but deliberately omitted from the MELD score in order to keep it objective. These results raise the question of whether additional candidate characteristics should be explicitly incorporated into the prioritization of wait list candidates; since such factors are already systematically affecting DDLT rates under the current allocation system