The novel SALT-M score predicts 1-year post-transplant mortality in patients with severe acute-on-chronic liver failure

Background & Aims: Twenty-eight-day mortality ranges from 30-90% in patients with acute-on-chronic liver failure grades 2/3 (severe ACLF). Though liver transplantation (LT) has demonstrated a survival benefit, the scarcity of donor organs and uncertainty regarding post-LT mortality among patients with severe ACLF may cause hesitancy. We developed and externally validated a model to predict 1-year post-LT mortality in severe ACLF, called the Sundaram ACLF-LT-Mortality (SALT-M) score, and estimated the median length of stay (LoS) after LT (ACLF-LT-LoS).Methods: In 15 LT centers in the US, we retrospectively identified a cohort of patients with severe ACLF transplanted between 2014-2019, followed up to Jan'2022. Candidate predictors included demographics, clinical and laboratory values, and organ failures. We selected predictors in the final model using clinical criteria and externally validated them in two French cohorts. We provided measures of overall performance, discrimination, and calibration. We used multivariable median regression to estimate LoS after adjusting for clinically relevant factors.Results: We included 735 patients, of whom 521 (70.8%) had severe ACLF (120 ACLF-3, external cohort). The median age was 55 years, and 104 with severe ACLF (19.9%) died within 1-year post-LT. Our final model included age >50 years, use of 1/=2 ino-tropes, presence of respiratory failure, diabetes mellitus, and BMI (continuous). The c-statistic was 0.72 (derivation) and 0.80 (validation), indicating adequate discrimination and calibration based on the observed/expected probability plots. Age, respiratory failure, BMI, and presence of infection independently predicted median LoS. Conclusions: The SALT-M score predicts mortality within 1-year after LT in patients with ACLF. The ACLF-LT-LoS score pre-dicted median post-LT stay. Future studies using these scores could assist in determining transplant benefits.