Improvement in sCD25 by day 7 is the strongest single predictor of pre-BMT mortality. CART analysis, a predictive algorithm used in machine learning, was performed based on pre-BMT mortality as the dependent variable. Independent variables included baseline (pretreatment), day 7, and day 14 parameters. CART growth limits were defined with the Gini method with minimum parent node cases of 3, minimum child node cases of 2, with cross-validation using 10 sample folds. All laboratory parameters (assessed at baseline and weekly thereafter) and their associated improvement from baseline values were included in this analysis. Nonlaboratory parameters included persistent fever at days 7 and 14, splenomegaly at diagnosis, presence of hemophagocytosis, and NK cell activity. CART analysis including imputed data (for any missing data) gave an identical result and is shown in supplemental Figure 6. Improvement from baseline = (difference between baseline and day X)/baseline value. NK cell, natural killer cell.