99 research outputs found

    A Sequential Stratification Method for Estimating the Effect of a Time-Dependent Experimental Treatment in Observational Studies

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    Survival analysis is often used to compare experimental and conventional treatments. In observational studies, the therapy may change during follow-up and such crossovers can be summarized by time-dependent covariates. Given the ever-increasing donor organ shortage, higher-risk kidneys from expanded criterion donors (ECD) are being transplanted. Transplant candidates can choose whether to accept an ECD organ (experimental therapy), or to remain on dialysis and wait for a possible non-ECD transplant later (conventional therapy). A three-group time-dependent analysis of such data involves estimating parameters corresponding to two time-dependent indicator covariates representing ECD transplant and non-ECD transplant, each compared to remaining on dialysis on the waitlist. However, the ECD hazard ratio estimated by this time-dependent analysis fails to account for the fact that patients who forego an ECD transplant are not destined to remain on dialysis forever, but could subsequently receive a non-ECD transplant. We propose a novel method of estimating the survival benefit of ECD transplantation relative to conventional therapy (waitlist with possible subsequent non-ECD transplant). Compared to the time-dependent analysis, the proposed method more accurately characterizes the data structure and yields a more direct estimate of the relative outcome with an ECD transplant.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/66010/1/j.1541-0420.2006.00527.x.pd

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

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    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

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    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

    The Survival Benefit of Liver Transplantation

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    Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/73611/1/j.1600-6143.2004.00703.x.pd

    Survival Benefit-Based Deceased-Donor Liver Allocation

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

    Reimbursement and economic factors influencing dialysis modality choice around the world

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    The worldwide incidence of kidney failure is on the rise and treatment is costly; thus, the global burden of illness is growing. Kidney failure patients require either a kidney transplant or dialysis to maintain life. This review focuses on the economics of dialysis. Alternative dialysis modalities are haemodialysis (HD) and peritoneal dialysis (PD). Important economic factors influencing dialysis modality selection include financing, reimbursement and resource availability. In general, where there is little or no facility or physician reimbursement or payment for PD, the share of PD is very low. Regarding resource availability, when centre HD capacity is high, there is an incentive to use that capacity rather than place patients on home dialysis. In certain countries, there is interest in revising the reimbursement structure to favour home-based therapies, including PD and home HD. Modality selection is influenced by employment status, with an association between being employed and PD as the modality choice. Cost drivers differ for PD and HD. PD is driven mainly by variable costs such as solutions and tubing, while HD is driven mainly by fixed costs of facility space and staff. Many cost comparisons of dialysis modalities have been conducted. A key factor to consider in reviewing cost comparisons is the perspective of the analysis because different costs are relevant for different perspectives. In developed countries, HD is generally more expensive than PD to the payer. Additional research is needed in the developing world before conclusive statements may be made regarding the relative costs of HD and PD
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