A model for predicting day-100 stem cell transplant-related mortality in AL amyloidosis

Abstract

Autologous stem cell Transplant (ASCT)-related mortality (TRM) in AL amyloidosis remains elevated. AL amyloidosis patients (n = 1718) from 9 centers, transplanted 2003–2020 were included. Pre-ASCT variables of interest were assessed for association with day-100 all-cause mortality. A random forest (RF) classifier with 10-fold cross-validation assisted in variable selection. The final model was fitted using logistic regression. The median age at ASCT was 58 years. Day-100 TRM occurred in 75 patients (4.4%) with the predominant causes being shock, high-grade arrhythmia, and organ failure. Ten factors were associated with day-100 TRM on univariate analysis. RF classifier using these variables generated a model with an area under the curve (AUC) of 0.72 ± 0.12. To refine the model selection using importance hierarchy function, a 4-variable model [NT-proBNP/BNP, serum albumin, ECOG performance status (PS), and systolic blood pressure] was built with an AUC of 0.70 ± 0.12. Based on logistic regression coefficients, ECOG PS 2/3 was assigned two points while other adverse predictors 1-point each. The model score range was 0–5, with a day-100 TRM of 0.46%, 3.2%, 5.8%, and 14.5% for 0, 1, 2, and ≥3 points, respectively. This model to predict day-100 TRM in AL amyloidosis allows better-informed decision-making in this heterogeneous disease.