Background & Aims: The 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.

Methods: From 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.

Results: Three 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.

Conclusions: A 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.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4517979PMC
http://dx.doi.org/10.1111/liv.12792DOI Listing

Publication Analysis

Top Keywords

clinician assessments
8
assessments health
8
health status
8
end-stage liver
8
liver disease
8
liver transplant
8
transplant candidates
8
liver
6
status predict
4
predict mortality
4

Similar Publications

Objective: To compare fall risk scores of hearing aids embedded with inertial measurement units (IMU-HAs) and powered by artificial intelligence (AI) algorithms with scores by trained observers.

Study Design: Prospective, double-blinded, observational study of fall risk scores between trained observers and those of IMU-HAs.

Setting: Tertiary referral center.

View Article and Find Full Text PDF

Purpose: A relative afferent pupillary defect (RAPD) is a characteristic clinical sign of optic neuritis (ON). Here, we systematically evaluated ultrasound pupillometry (UP) for the detection of an RAPD in patients with ON, including a comparison with infrared video pupillometry (IVP), the gold standard for objective pupillometry.

Materials And Methods: We enrolled 40 patients with acute (n = 9) or past (n = 31) ON (ON+), 31 patients with multiple sclerosis (MS) without prior ON, and 50 healthy controls (HC) in a cross-sectional observational study.

View Article and Find Full Text PDF

Objectives: Neurocritically ill patients are at high risk for developing delirium, which can worsen the long-term outcomes of this vulnerable population. However, existing delirium assessment tools do not account for neurologic deficits that often interfere with conventional testing and are therefore unreliable in neurocritically ill patients. We aimed to determine the accuracy and predictive validity of the Fluctuating Mental Status Evaluation (FMSE), a novel delirium screening tool developed specifically for neurocritically ill patients.

View Article and Find Full Text PDF

General Purpose: To provide a summary of six articles published in 2023 that provide important new data or insights about pressure injuries (PIs).

Target Audience: This continuing education activity is intended for physicians, physician assistants, nurse practitioners, and registered nurses with an interest in skin and wound care.

Learning Objectives/outcomes: After participating in this educational activity, the participant will:1.

View Article and Find Full Text PDF

Background: This study aims to evaluate the capabilities and limitations of large language models (LLMs) for providing patient education for men undergoing radiotherapy for localized prostate cancer, incorporating assessments from both clinicians and patients.

Methods: Six questions about definitive radiotherapy for prostate cancer were designed based on common patient inquiries. These questions were presented to different LLMs [ChatGPT‑4, ChatGPT-4o (both OpenAI Inc.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!