A Risk Stratification System in Myeloma Patients with Autologous Stem Cell Transplantation

Simple Summary Autologous stem cell transplantation (ASCT) is a longstanding myeloma treatment, but patient outcomes vary. In a retrospective study of 5259 patients with multiple myeloma (MM) at the University of Arkansas, we identified adverse prognostic factors, including delayed MM-diagnosis-to-ASCT duration, high serum ferritin, and low transferrin levels. These findings may enhance existing prognostic models. We also pinpointed poor prognosis markers, such as high serum calcium and low platelet counts, albeit in a smaller patient subset. Utilizing seven accessible high-risk variables, we devised a four-stage system, validated in both the training dataset and an independent cohort of 514 ASCT-treated MM patients from the University of Iowa. This staging system's robust validation underscores its potential clinical utility, providing insights into cytogenetic risk factors. The ATM4S system presents a practical approach to refine prognostic assessments and guide personalized treatment strategies in ASCT-treated MM patients.Abstract Autologous stem cell transplantation (ASCT) has been a mainstay in myeloma treatment for over three decades, but patient prognosis post-ASCT varies significantly. In a retrospective study of 5259 patients with multiple myeloma (MM) at the University of Arkansas for Medical Sciences undergoing ASCT with a median 57-month follow-up, we divided the dataset into training (70%) and validation (30%) subsets. Employing univariable and multivariable Cox analyses, we systematically assessed 29 clinical variables, identifying crucial adverse prognostic factors, such as extended duration between MM diagnosis and ASCT, elevated serum ferritin, and reduced transferrin levels. These factors could enhance existing prognostic models. Additionally, we pinpointed significant poor prognosis markers like high serum calcium and low platelet counts, though they are applicable to a smaller patient population. Utilizing seven easily accessible high-risk variables, we devised a four-stage system (ATM4S) with primary stage borders determined through K-adaptive partitioning. This staging system underwent validation in both the training dataset and an independent cohort of 514 ASCT-treated MM patients from the University of Iowa. We also explored cytogenetic risk factors within this staging system, emphasizing its potential clinical utility for refining prognostic assessments and guiding personalized treatment approaches.