title: Center variation in the use of nonstandardized model for end-stage liver disease exception points.
abstract: The Model for End-Stage Liver Disease (MELD) score is an imperfect prognosticator of waitlist dropout, so transplant centers may apply for exception points to increase a waitlist candidate's priority on the waitlist. Exception applications are categorized as recognized exceptional diagnoses (REDs; eg, hepatocellular carcinoma) and non-REDs (eg, cholangitis). Although prior work has demonstrated regional variation in the use of exceptions, no work has examined the between-center variability. We analyzed all new waitlist candidates from February 27, 2002 to June 3, 2011 to explore variations in the use of non-REDs, for which no strict exception criteria exist. There were 58,641 new waitlist candidates, and 4356 (7.4%) applied for a non-RED exception. The number of applications increased steadily over time, as did the approval rates for such applications: from <50% in 2002 to nearly 75% in 2010. When we adjusted for patient factors, there was significant variability (P < 0.001) in the use of non-RED exceptions in 8 of 11 United Network for Organ Sharing (UNOS) regions and in the approval of these exceptions in 6 of 11 UNOS regions. The variability in the use and approval of non-REDs was clinically significant: waitlist candidates with approved exceptions were significantly more likely to undergo transplantation (68.3% versus 53.4%, P < 0.001) and were less likely to be removed for death or clinical deterioration (10.4% versus 16.2%, P < 0.001). Increased median MELD score at transplantation within a donor service area was the only center factor associated with increased odds of applying for exceptions, while no center factors were associated with having non-RED exceptions approved. Further work is needed to identify other sources of variation to ensure the appropriate and equitable use of non-RED exceptions.
authors.affiliation: Division of Gastroenterology, Department of Medicine, Philadelphia, PA; Clinical Center for Epidemiology and Biostatistics, Perelman School of Medicine, Philadelphia, PA.
authors.forename: David S
authors.initials: DS
authors.lastname: Goldberg
authors.forename: George
authors.initials: G
authors.lastname: Makar
authors.forename: Therese
authors.initials: T
authors.lastname: Bittermann
authors.forename: Benjamin
authors.initials: B
authors.lastname: French
mesh_terms: D000328:Adult; D000368:Aged; D002391:Catchment Area, Health; D003661:Decision Support Techniques; D005260:Female; D015276:Health Care Rationing; D006292:Health Priorities; D054625:Healthcare Disparities; D006801:Humans; D008107:Liver Diseases / Q000175:diagnosis* / Q000401:mortality / Q000601:surgery*; D016031:Liver Transplantation; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D015999:Multivariate Analysis; D016017:Odds Ratio; D010352:Patient Dropouts; D011237:Predictive Value of Tests; D012307:Risk Factors; D012720:Severity of Illness Index; D013997:Time Factors; D009927:Tissue and Organ Procurement; D014850:Waiting Lists / Q000401:mortality