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

Abstract

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.

Keywords: ideal outcome; machine learning; pediatric liver transplant.

Figure 1.

Participant inclusion/exclusion diagram

Figure 2.

Example schematic of random forests analysis. In this example schematic, the classifier is predicting success (Yes or No). A subset of participant variables from a subset of participants is used in each conditional inference tree to generate a prediction. The subsets of variables and participants can differ for each tree. Participants in the subset used to build a tree are in bag, and those outside of the subset are out-of-bag. Rather than splitting the data set once for training and then validation, as if often done with other methods, random forests incorporates training and testing within individual trees by always holding some participants out (i.e. out-of-bag). The number of variables used to build a tree is tuned because allowing use of all variables can limit generalizability due to overfitting. Each of the individual trees may have high bias for overfitting the data, yet in random forests analysis, the average of each of the individual trees is used to generate the classifier prediction. The classifier then uses out-of-bag error measurement to determine the accuracy rate by validating the classifier on participants (about a third of the total data) that were not used in building each tree.

Figure 3.

Reason(s) for failure to achieve IO-3 profile by a.) Number of abnormal IO-3 component variables for participants not achieving IO-3 and b.) Frequency of component variables for participants not achieving IO-3 ALT – alanine aminotransferase; GGT - gamma-glutamyl transferase; PTLD – post-transplant lymphoproliferative disease

Figure 4.

Descriptive heat map of predictor variables available at 1-year and ideal outcome Legend: Ideal outcome at 3 years: Black indicates participants who did not meet definition of IO-3; Light gray indicates participants who did meet definition of IO-3. Green/Red: Green – favorable; Red – unfavorable Blue/Yellow: Blue – yes; Yellow – no Purple – continuous variable; higher value is depicted with deeper purple ALF – acute liver failure; UNOS – United Network for Organ Sharing; ICU – intensive care unit; GFR – glomerular filtration rate; hrs – hours; min – minutes; mos – months

Figure 5.

Random forests analysis ranking of variable importance Legend: < signifies that the variable predicts no ideal outcome; > signifies that the variable predicts ideal outcome hrs – hours; GFR – glomerular filtration rate; ALF – acute liver failure; UNOS – United Network for Organ Sharing; ICU – intensive care unit; min – minutes; yrs – years; mos – months; PELD – Pediatric end-stage liver disease; US – United States