Development of a Predictive Model for Deceased Donor Organ Yield.

Abstract

Optimizing organ yield (number of organs transplanted per donor) is a modifiable way to increase the number of organs available for transplant. Historically, models to predict donor organ yield have been developed based ordinary least squares regression and ordinal logistic regression; however, alternative modeling methodology may be superior to conventional approaches.1,2 In this preliminary analysis, rather than treating organ yield as a continuous outcome, we modeled the number of organs transplanted per donor as counts. We aimed to compare different linear and nonlinear statistical models for count responses to predict deceased donor organ yield. We used data from the OPTN database from 2000 to 2016 to parameterize our exploratory models. The initial set of predictors for deceased donor organ yield was derived from published studies.1-3 We included adult deceased donors between 18 and 84 years of age that had at least 1 organ procured for transplantation. 75 350 records met inclusion criteria. We used 80% of the data for derivation in a cross-validation analysis and the remainder of the data as a validation set. The cross-validation analysis was replicated 50 times, and the random holdouts consisted of 20% of the derivation cohort. The following models were evaluated: ordinary least squares regression,1 ordinal logistic regression,2 Poisson regression, negative binomial regression, general additive models, classification and regression trees, random forests, bootstrap aggregated classification and regression trees, boosted classification and regression trees, Bayesian additive regression trees (BART), multivariate adaptive regression splines, artificial neural networks, and mean-only models. Among the models, BART resulted in the lowest error on predicting the number of organs transplanted per deceased donor. Two-sample t tests showed that the BART had significantly lower mean absolute error (MAE) when predicting deceased donor organ yield (all P < 0.001). On average, this model presented a MAE of 0.867 throughout the cross-validation analysis, and a MAE of 0.856 when tested in the validation set. The BART showed that deceased donor organ yield had a negative nonlinear relationship with age, body mass index, terminal blood urea nitrogen, terminal laboratory creatinine, aspartate aminotransferase, terminal laboratory total bilirubin; a positive nonlinear relationship with organ recovery time, partial pressure of oxygen levels, and last serum sodium; and more complex nonlinear relationships with alanine aminotransferase and the ratio of partial pressure arterial oxygen and fraction of inspired oxygen. Bayesian additive regression trees would improve prediction from at least 63 organs per 1000 donors (compared with an ordinary least squares regression1) to at most 120 organs per 1000 donors (compared with an ordinal logistic regression2). Through the use of BART, we were able to obtain higher predictive accuracy for organ yield. This model allows for nonlinear relationships among the predictors and the number of organs transplanted per deceased donor, which likely explains the superior performance compared with conventional models. In conclusion, our preliminary analysis shows that the BART methodology is superior in predicting deceased donor organ yield and can potentially serve as an aid to assess organ procurement organization performance, reduce geographic disparities, and in forecasting future organ availability. A forthcoming article will include the finalized analysis.