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Predicting Progression of Smoldering Multiple Myeloma With Novel Dynamic Model

By: Sarah Campen, PharmD
Posted: Wednesday, May 4, 2022

Irene Ghobrial, MD, of Dana-Farber Cancer Institute, Boston, and colleagues have created a multivariable algorithm that integrates dynamic changes in clinical variables to enhance disease progression risk predictions in patients with smoldering multiple myeloma. The details of the predictive model, named PANGEA, were presented at the American Association for Cancer Research (AACR) Annual Meeting 2022 (Abstract 2259/13). “These findings demonstrate that disease progression from smoldering multiple myeloma to multiple myeloma, which likely occurs by the acquisition of sequential changes to the plasma cell clone, can be tracked by trends in clinical values, thus improving prognostication for precursor patients,” stated the study authors.

PANGEA is an international retrospective cohort of 1,095 patients with smoldering multiple myeloma, 254 (23%) of whom experienced progression to multiple myeloma. Each patient had baseline and serial time points of clinical and biologic variables. With the cohort, the researchers modeled progression to multiple myeloma with Cox regression using time-dependent and continuous clinical variables; the model’s performance was validated using data from patients at Dana-Farber.

Next, the PANGEA cohort was used to validate current models of smoldering multiple myeloma progression, including the baseline model of the 20/2/20 International Myeloma Working Group criteria using binary cutoffs of initial measurements. This model was then extended to allow for restratification by the 20/2/20 criteria over time, thus creating a dynamic model.

The authors found that rates of change in a set of myeloma-specific clinical variables, unrestricted to those of the 20/2/20 criteria, improved the predictive ability of the model. For example, changes in disease indicators such as age and creatinine were highly predictive of imminent disease progression. “The resulting multivariable, dynamic algorithm is a dramatic improvement over current clinical standards in predicting progression from smoldering multiple myeloma to multiple myeloma disease,” they concluded.

Disclosure: For full disclosures of the study authors, visit

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