Discovery Analysis of Associations Between MicroRNAs (MiRs) and Both Pre-Transplant Comorbidity Burden and Post-Transplant Mortality in Patients (Pts) with Acute Leukemia (AL) in Complete Remission (CR) Given Allogeneic Hematopoietic Cell Transplantation

100 TRM. A cohort of 28,236 acute leukemia, adult allogeneic HSCT recipients were analyzed. Twenty four variables were included. In the second phase, by applying a repetitive computerized simulation, factors necessary for optimal prediction were explored: algorithm type, size of data set, number of included variables, and performance in specific subpopulations. Models were assessed and compared on the basis of the area under the receiver operating characteristic curve (AUC). We developed 6 ML based prediction models for day 100 TRM. Optimal AUCs ranged from 0.65-0.68. Predictive performance plateaued for a population size ranging from n1⁄45647-8471, depending on the algorithm (Figure 1). A feature selection algorithm ranked variables according to importance. Provided with the ranked variable we data, discovered that a range of 6-12 ranked variables were necessary for optimal prediction, depending on the algorithm. Predictive performance of models developed for specific subpopulations ranged from an average of 0.59 to 0.67 for patient in second complete remission and patients receiving reduced intensity conditioning respectively. In summary, we present a novel computational approach for predictionmodel development and analysis in the field of HSCT. Using data commonly collected on transplant patients, our simulation elucidates outcome prediction limiting factors. Regardless of the methodology applied, predictive performance converged when sampling more than 5000 patients. Few variables “carry the weight” with regard to predictive influence. Overall, the presented findings reveal a phenomenon of predictive saturation with data traditionally collected. Improving predictive performance will likely require additional types of input like genetic, biologic and procedural factors.