Dataset Information

Increasing disparity in waitlist mortality rates with increased model for end-stage liver disease scores for candidates with hepatocellular carcinoma versus candidates without hepatocellular carcinoma.

ABSTRACT: Candidates with hepatocellular carcinoma (HCC) within the Milan criteria (MC) receive standardized Model for End-Stage LIver Disease (MELD) exception points because of the projected risk of tumor expansion beyond the MC. Exception points at listing are meant to be equivalent to a 15% rusj if 90-day mortality, with additional points granted every 3 months, equivalent to a 10% increased morality risk. We analyzed the United Network for Organ Sharing database (January 1, 2005 to May 31, 2009) to compare the 90-day waitlist outcomes of HCC candidates and non-HCC candidates with similar MELD scores. Two hundred fifty-nine HCC candidates (4.1%) who were initially listed with 22 MELD exception points were removed because of death or clinical deterioration within 90 days of listing, whereas 283 non-HCC candidates (11.0%) with initial laboratory MELD scores of 21 to 23 were removed. Ninety-three HCC candidates (4.6%) with 25 exception points (after 3-6 months of waiting) were removed because of death or clinical deterioration within 90 days, whereas 805 non-HCC candidates (17.3%) with laboratory MELD scores of 24 to 26 were removed. Twenty HCC candidates (3.0%) with 28 exception points (after 6-9 months of waiting) were removed for death or clinical deterioration within 90 days, whereas 646 non-HCC candidates (23.6%) with laboratory MELD scores of 27 to 29 were removed. In multivariate logistic regression models, HCC candidates had significantly lower 90-day odds of waitlist removal for death or clinical deterioration (P < 0.001). Over time, the risk of waitlist removal for death or clinical deterioration was unchanged for HCC candidates (P = 0.17), whereas it increased significantly for non-HCC candidates. The current allotment of HCC exception points should be re-evaluated because of the stable risk of waitlist dropout for these candidates.

SUBMITTER: Goldberg D

PROVIDER: S-EPMC3319293 | biostudies-literature | 2012 Apr

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Increasing disparity in waitlist mortality rates with increased model for end-stage liver disease scores for candidates with hepatocellular carcinoma versus candidates without hepatocellular carcinoma.

Goldberg David D French Benjamin B Abt Peter P Feng Sandy S Cameron Andrew M AM

Liver transplantation : official publication of the American Association for the Study of Liver Diseases and the International Liver Transplantation Society 20120401 4

Candidates with hepatocellular carcinoma (HCC) within the Milan criteria (MC) receive standardized Model for End-Stage LIver Disease (MELD) exception points because of the projected risk of tumor expansion beyond the MC. Exception points at listing are meant to be equivalent to a 15% rusj if 90-day mortality, with additional points granted every 3 months, equivalent to a 10% increased morality risk. We analyzed the United Network for Organ Sharing database (January 1, 2005 to May 31, 2009) to co ...[more]

PMID: 22271656

Similar Datasets

Project description:Rationale & objectiveIntact cognition is generally a prerequisite for navigating through and completing evaluation for kidney transplantation. Despite kidney transplantation being contraindicated for those with severe dementia, screening for more mild forms of cognitive impairment before referral is rare. Candidates may have unrecognized cognitive impairment, which may prolong evaluation, elevate mortality risk, and hinder access to kidney transplantation. We estimated the burden of cognitive impairment and its association with access to kidney transplantation and waitlist mortality.Study designProspective cohort study.Setting & participants3,630 participants (January 2009 to June 2018) with cognitive function measured (by the Modified Mini-Mental State Examination [3MS]) at kidney transplantation evaluation at 1 of 2 transplantation centers.PredictorsCognitive impairment (3MS score<80).OutcomesListing, waitlist mortality, and kidney transplantation.Analytical approachWe estimated the adjusted chance of listing (Cox regression), risk for waitlist mortality (competing-risks regression), and kidney transplantation rate (Poisson regression) by cognitive impairment. Given potential differences in cause of cognitive impairment among those with and without diabetes, we tested whether these associations differed by diabetes status using a Wald test.ResultsAt evaluation, 6.4% of participants had cognitive impairment, which was independently associated with 25% lower chance of listing (adjusted HR, 0.75; 95% CI, 0.61-0.91); this association did not differ by diabetes status (Pinteraction=0.07). There was a nominal difference by diabetes status for the association between cognitive impairment and kidney transplantation rate (Pinteraction=0.05), while the association between cognitive impairment and waitlist mortality differed by diabetes status kidney transplantation rates (Pinteraction=0.02). Among candidates without diabetes, those with cognitive impairment were at 2.47 (95% CI, 1.31-4.66) times greater risk for waitlist mortality; cognitive impairment was not associated with this outcome among candidates with diabetes.LimitationsSingle measure of cognitive impairment.ConclusionsCognitive impairment is associated with a lower chance of being placed on the waitlist, and among patients without diabetes, with increased mortality on the waitlist. Future studies should investigate whether implementation of screening for cognitive impairment improves these outcomes.

Project description:BACKGROUND: Comparative genomics aims to detect signals of evolutionary conservation as an indicator of functional constraint. Surprisingly, results of the ENCODE project revealed that about half of the experimentally verified functional elements found in non-coding DNA were classified as unconstrained by computational predictions. Following this observation, it has been hypothesized that this may be partly explained by biased estimates on neutral evolutionary rates used by existing sequence conservation metrics. All methods we are aware of rely on a comparison with the neutral rate and conservation is estimated by measuring the deviation of a particular genomic region from this rate. Consequently, it is a reasonable assumption that inaccurate neutral rate estimates may lead to biased conservation and constraint estimates. RESULTS: We propose a conservation signal that is produced by local Maximum Likelihood estimation of evolutionary parameters using an optimized sliding window and present a Kullback-Leibler projection that allows multiple different estimated parameters to be transformed into a conservation measure. This conservation measure does not rely on assumptions about neutral evolutionary substitution rates and little a priori assumptions on the properties of the conserved regions are imposed. We show the accuracy of our approach (KuLCons) on synthetic data and compare it to the scores generated by state-of-the-art methods (phastCons, GERP, SCONE) in an ENCODE region. We find that KuLCons is most often in agreement with the conservation/constraint signatures detected by GERP and SCONE while qualitatively very different patterns from phastCons are observed. Opposed to standard methods KuLCons can be extended to more complex evolutionary models, e.g. taking insertion and deletion events into account and corresponding results show that scores obtained under this model can diverge significantly from scores using the simpler model. CONCLUSION: Our results suggest that discriminating among the different degrees of conservation is possible without making assumptions about neutral rates. We find, however, that it cannot be expected to discover considerably different constraint regions than GERP and SCONE. Consequently, we conclude that the reported discrepancies between experimentally verified functional and computationally identified constraint elements are likely not to be explained by biased neutral rate estimates.

Dataset Information

Increasing disparity in waitlist mortality rates with increased model for end-stage liver disease scores for candidates with hepatocellular carcinoma versus candidates without hepatocellular carcinoma.

Publications

Increasing disparity in waitlist mortality rates with increased model for end-stage liver disease scores for candidates with hepatocellular carcinoma versus candidates without hepatocellular carcinoma.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets