Living donor liver transplantation can address disparities in transplant access for patients with primary sclerosing cholangitis.

BACKGROUND: Liver transplantation (LT) is frequently lifesaving for people living with primary sclerosing cholangitis (PSC). However, patients are waitlisted for LT according to the model for end-stage liver disease-sodium (MELD-Na) score, which may not accurately reflect the burden of living with PSC. We sought to describe and analyze the clinical trajectory for patients with PSC referred for LT, in a mixed deceased donor/living donor transplant program. METHODS: This was a retrospective cohort study from November 2012 to December 2019, including all patients with PSC referred for assessment at the University Health Network Liver Transplant Clinic. Patients who required multiorgan transplant or retransplantation were excluded. Liver symptoms, hepatobiliary malignancy, MELD-Na progression, and death were abstracted from chart review. Competing risk analysis was used for timing of LT, transplant type, and death. RESULTS: Of 172 PSC patients assessed, 84% (n = 144) were listed of whom 74% were transplanted. Mean age was 47.6 years, and 66% were male. Overall mortality was 18.2% at 2 years. During the follow-up, 16% (n = 23) were removed from the waitlist for infection, clinical deterioration, liver-related mortality or new cancer; 3 had clinical improvement. At listing, 82% (n = 118) had a potential living donor (pLD). Patients with pLD had significantly lower waitlist and liver-related waitlist mortality (HR 0.20, p<0.001 and HR 0.17, p<0.001, respectively), and higher rates of transplantation (HR 1.83, p = 0.05). Exception points were granted to 13/172 (7.5%) patients. CONCLUSIONS: In a high-volume North American LT center, most patients with PSC assessed for transplant were listed and subsequently transplanted. However, this was a consequence of patients engaging in living donor transplantation. Our findings support the concern from patients with PSC that MELD-Na allocation does not adequately address their needs.

Artificial intelligence applied to omics data in liver diseases: Enhancing clinical predictions.

Rapid development of biotechnology has led to the generation of vast amounts of multi-omics data, necessitating the advancement of bioinformatics and artificial intelligence to enable computational modeling to diagnose and predict clinical outcome. Both conventional machine learning and new deep learning algorithms screen existing data unbiasedly to uncover patterns and create models that can be valuable in informing clinical decisions. We summarized published literature on the use of AI models trained on omics datasets, with and without clinical data, to diagnose, risk-stratify, and predict survivability of patients with non-malignant liver diseases. A total of 20 different models were tested in selected studies. Generally, the addition of omics data to regular clinical parameters or individual biomarkers improved the AI model performance. For instance, using NAFLD fibrosis score to distinguish F0-F2 from F3-F4 fibrotic stages, the area under the curve (AUC) was 0.87. When integrating metabolomic data by a GMLVQ model, the AUC drastically improved to 0.99. The use of RF on multi-omics and clinical data in another study to predict progression of NAFLD to NASH resulted in an AUC of 0.84, compared to 0.82 when using clinical data only. A comparison of RF, SVM and kNN models on genomics data to classify immune tolerant phase in chronic hepatitis B resulted in AUC of 0.8793-0.8838 compared to 0.6759-0.7276 when using various serum biomarkers. Overall, the integration of omics was shown to improve prediction performance compared to models built only on clinical parameters, indicating a potential use for personalized medicine in clinical setting.