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 to 2021. The utility of the previously defined total SIPAT cutoff (<21 [excellent/good candidates] vs. ≥21 [minimally acceptable/high-risk candidates]) was studied. Multivariable logistic regression analyses examined associations between continuous SIPAT scores and LT waitlisting outcomes. The Youden J statistic was used to identify the optimal SIPAT cutoff for patients with MASLD. A total of 480 patients evaluated for transplant with MASLD were included. Only 9.4% of patients had a 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 5-point increase; p = 0.003) in multivariable models. A SIPAT of ≥12 was identified as the optimal cutoff in this population, resulting in an adjusted OR for a listing of 0.53 versus SIPAT <12 ( p = 0.001). In this large cohort of patients with MASLD evaluated for LT, few patients met the previously defined high SIPAT cutoff for transplant suitability. Nevertheless, increasing the SIPAT score was associated with waitlist outcomes. Our suggested SIPAT cutoff of ≥12 for patients with MASLD warrants further external validation using data from other centers.