Key Features Of The Pangea Model Improving Risk Stratification In Myeloma

Key Features Of The PANGEA Model: Improving Risk Stratification In ...

Key Features Of The PANGEA Model: Improving Risk Stratification In ... Irene ghobrial, md, dana farber cancer institute, boston, ma, discusses key features of the pangea model, which aims to more precisely risk stratify patients. The backbone of the pangea model can be used to improve risk assessment for patients with precursor conditions such as monoclonal gammopathy of undetermined significance and smouldering multiple myeloma to develop overt multiple myeloma.

Risk Stratification Of Plasma Cell | Int Myeloma Fn

Risk Stratification Of Plasma Cell | Int Myeloma Fn The promise study is a national effort to test people who are at higher risks of mgus, smm, and multiple myeloma. the goal of the promise study is to find these disease states early & understand why some people develop them while others do not. The 20/2/20 model is the current gold standard to stratify smoldering multiple myeloma (smm) patients at baseline into three subgroups (low, intermediate, and high) according to the risk of progression based on the free light chain ratio (flcr), m protein concentration, and percentage of bone marrow (bm) plasma cells (pc). We assembled the largest cohort to date of 2,270 smm patients from six international centers with longitudinal clinical and biological data to train and validate the pangea 2.0 risk models. The complementary nature of both models suggests potential benefit in combined application; pangea's multiple time horizons offer more detailed progression risk assessment and imwg's established framework provides valuable baseline risk stratification.

Multiple Myeloma Risk Stratification Model Tailored To Different ...

Multiple Myeloma Risk Stratification Model Tailored To Different ... We assembled the largest cohort to date of 2,270 smm patients from six international centers with longitudinal clinical and biological data to train and validate the pangea 2.0 risk models. The complementary nature of both models suggests potential benefit in combined application; pangea's multiple time horizons offer more detailed progression risk assessment and imwg's established framework provides valuable baseline risk stratification. Irene ghobrial, md, dana farber cancer institute, boston, ma, discusses key features of the pangea model, which aims to more precisely risk stratify patients with smoldering myeloma and monoclonal gammopathy of undetermined significance (mgus), and further highlights how this model may impact clinical practice. We developed an improved pangea 2.0 model that includes trajectory modeling of these biomarkers to capture evolving patterns and improve predictions of mm progression. Our goal was to develop a monoclonal gammopathy of undetermined significance and smouldering multiple myeloma stratification algorithm that utilised accessible, time varying biomarkers to model risk of progression to multiple myeloma. Of 96 patients with both 20/2/20 and pangea with bmbx risk calculations available, 90/96 (94%) had a lower predicted risk with the pangea model, 72 (75%) had a decrease of >5% in their 2‐year predicted risk, while just 6/96 (6%) had an increased predicted risk of progression to mm with pangea.

Risk Stratification In Multiple Myeloma. | Download Scientific Diagram

Risk Stratification In Multiple Myeloma. | Download Scientific Diagram Irene ghobrial, md, dana farber cancer institute, boston, ma, discusses key features of the pangea model, which aims to more precisely risk stratify patients with smoldering myeloma and monoclonal gammopathy of undetermined significance (mgus), and further highlights how this model may impact clinical practice. We developed an improved pangea 2.0 model that includes trajectory modeling of these biomarkers to capture evolving patterns and improve predictions of mm progression. Our goal was to develop a monoclonal gammopathy of undetermined significance and smouldering multiple myeloma stratification algorithm that utilised accessible, time varying biomarkers to model risk of progression to multiple myeloma. Of 96 patients with both 20/2/20 and pangea with bmbx risk calculations available, 90/96 (94%) had a lower predicted risk with the pangea model, 72 (75%) had a decrease of >5% in their 2‐year predicted risk, while just 6/96 (6%) had an increased predicted risk of progression to mm with pangea.

Key features of the PANGEA model: improving risk stratification in myeloma