Predicting ideal outcome after pediatric liver transplantation: An exploratory study using machine learning analyses to leverage Studies of Pediatric Liver Transplantation Data.

Machine learning analyses allow for the consideration of numerous variables in order to accommodate complex relationships that would not otherwise be apparent in traditional statistical methods to better classify patient risk. The SPLIT registry data were analyzed to determine whether baseline demographic factors and clinical/biochemical factors in the first-year post-transplant could predict ideal outcome at 3 years (IO-3) after LT. Participants who received their first, isolated LT between 2002 and 2006 and had follow-up data 3 years post-LT were included. IO-3 was defined as alive at 3 years, normal ALT (<50) or GGT (<50), normal GFR, no non-liver transplants, no cytopenias, and no PTLD. Heat map analysis and RFA were used to characterize the impact of baseline and 1-year factors on IO-3. 887/1482 SPLIT participants met inclusion criteria; 334 had IO-3. Demographic, biochemical, and clinical variables did not elucidate a visual signal on heat map analysis. RFA identified non-white race (vs white race), increased length of operation, vascular and biliary complications within 30 days, and duct-to-duct biliary anastomosis to be negatively associated with IO-3. UNOS regions 2 and 5 were also identified as important factors. RFA had an accuracy rate of 0.71 (95% CI: 0.68-0.74), PPV = 0.83, and NPV = 0.70. RFA identified participant variables that predicted IO-3. These findings may allow for better risk stratification and personalization of care following pediatric liver transplantation.