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<p>Autologous stem cell Transplant (ASCT)-related mortality (TRM) in AL amyloidosis remains elevated. AL amyloidosis patients (<i>n</i> = 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.</p>

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

  • 期刊论文

  • 发表日期：

    2025年2月24日

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  • Eli Muchtar,
  • Angela Dispenzieri,
  • Vaishali Sanchorawala,
  • Hamza Hassan,
  • Raphael Mwangi,
  • Matthew Maurer,
  • Francis Buadi,
  • Hans C. Lee,
  • Muzaffar Qazilbash,
  • Andrew Kin,
  • Jeffrey Zonder,
  • Sally Arai,
  • Michelle M. Chin,
  • Rajshekhar Chakraborty,
  • Suzanne Lentzsch,
  • Hila Magen,
  • Eden Shkury,
  • Caitlin Sarubbi,
  • Heather Landau,
  • Stefan Schönland,
  • Ute Hegenbart,
  • Morie Gertz

摘要

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.