Association of psychosocial risk factors and liver transplant evaluation outcomes in metabolic dysfunction-associated steatotic liver disease.

Abstract

The Stanford Integrated Psychosocial Assessment for Transplantation (SIPAT) is a standardized psychosocial assessment tool used in liver transplantation (LT) evaluation and has been primarily studied in patients with alcohol-associated liver disease. We aimed to evaluate the relationship between SIPAT score and metabolic syndrome severity and LT waitlist outcomes in a large cohort of patients with metabolic dysfunction-associated steatotic liver disease (MASLD). We performed a single-center retrospective cohort study of patients with MASLD evaluated for LT from 2014-2021. The utility of the previously defined total SIPAT cut-off (<21 [excellent/good candidates] vs ≥21 [minimally acceptable/high risk candidates]) was studied. Multivariable logistic regression analyses examined associations between continuous SIPAT score and LT waitlisting outcomes. Youden's J statistic was used to identify the optimal SIPAT cut-off for MASLD patients. A total of 480 patients evaluated for transplant with MASLD were included. Only 9.4% of patients had SIPAT score ≥21. Patients with SIPAT score ≥21 had higher hemoglobin A1c compared to patients with lower psychosocial risk (median (IQR): 7.8 (6.0-9.7) vs 6.6 (5.8, 7.9); p=0.04). There were no other differences in metabolic comorbidities between SIPAT groups. Increasing SIPAT score was associated with decreased odds of listing (OR: 0.82 per five-point increase; p=0.003) in multivariable models. A SIPAT of ≥12 was identified as the optimal cut-off in this population, resulting in an adjusted OR for listing of 0.53 vs SIPAT <12 (p=0.001). In this large cohort of MASLD patients evaluated for LT, few patients met the previously defined high SIPAT cut-off for transplant suitability. Nevertheless, increasing SIPAT score was associated with waitlist outcome. Our suggested SIPAT cut-off of ≥12 for MASLD patients warrants further external validation using data from other centers.