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Clinician assessments of health status predict mortality in patients with end-stage liver disease awaiting liver transplantation.
- Author(s): Lai, Jennifer C;
- Covinsky, Kenneth E;
- Hayssen, Hilary;
- Lizaola, Blanca;
- Dodge, Jennifer L;
- Roberts, John P;
- Terrault, Norah A;
- Feng, Sandy
- et al.
Published Web Locationhttps://doi.org/10.1111/liv.12792
Background & aimsThe US liver allocation system effectively prioritizes most liver transplant candidates by disease severity as assessed by the Model for End-Stage Liver Disease (MELD) score. Yet, one in five dies on the wait-list. We aimed to determine whether clinician assessments of health status could identify this subgroup of patients at higher risk for wait-list mortality.
MethodsFrom 2012-2013, clinicians of all adult liver transplant candidates with laboratory MELD≥12 were asked at the clinic visit: 'How would you rate your patient's overall health today (0 = excellent, 5 = very poor)?' The odds of death/delisting for being too sick for the transplant by clinician-assessment score ≥3 vs. <3 were assessed by logistic regression.
ResultsThree hundred and forty-seven liver transplant candidates (36% female) had a mean follow-up of 13 months. Men differed from women by disease aetiology (<0.01) but were similar in age and markers of liver disease severity (P > 0.05). Mean clinician assessment differed between men and women (2.3 vs. 2.6; P = 0.02). The association between clinician-assessment and MELD was ρ = 0.28 (P < 0.01). 53/347 (15%) died/were delisted. In univariable analysis, a clinician-assessment score ≥ 3 was associated with increased odds of death/delisting (2.57; 95% CI 1.42-4.66). After adjustment for MELD and age, a clinician-assessment score ≥ 3 was associated with 2.25 (95% CI 1.22-4.15) times the odds of death/delisting compared to a clinician-assessment score < 3.
ConclusionsA standardized clinician assessment of health status can identify liver transplant candidates at high risk for wait-list mortality independent of MELD score. Objectifying this 'eyeball test' may inform interventions targeted at this vulnerable subgroup to optimize wait-list outcomes.
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