The following is a summary of “Personalised progression prediction in patients with monoclonal gammopathy of undetermined significance or smouldering multiple myeloma (PANGEA): a retrospective, multicohort study,” published in the March 2023 issue of the Emergency Medicine by Cowan et al.
Based on monoclonal protein concentrations or bone marrow plasma cell percentage, patients with multiple myeloma precursors are dichotomized as having monoclonal gammopathy of unclear significance or smoldering multiple myeloma. Laboratory tests made at the time of diagnosis are used in current risk stratifications, but time-varying biomarkers are not considered. This study aimed to create a risk-stratification algorithm for multiple myeloma that uses easily-obtained, dynamic biomarkers in patients with monoclonal gammopathy of uncertain significance and smoldering disease. Adults with either monoclonal gammopathy of unclear significance or smoldering multiple myeloma were included in this retrospective multicohort research. Using a training cohort, Researchers compared many models’ abilities to predict whether or not a patient with myeloma will develop the disease (with patients at Dana-Farber Cancer Institute, Boston, MA, USA; annotated from Nov, 13, 2019, to April, 13, 2022).
To predict whether or not a patient with multiple myeloma precursor will develop into a full-blown case, researchers developed the PANGEA models using biomarker data (including monoclonal protein concentration, free light chain ratio, age, creatinine concentration, and bone marrow plasma cell percentage). Researchers compared the PANGEA models to the current criteria (20/22/smoldering criterion for multiple myeloma and the International Myeloma Working Group’s monoclonal gammopathy of uncertain significance). Researchers sample size was 6,441 people, with 1,510 (23%) having latent multiple myeloma and 4,931 (77%) having monoclonal gammopathy of unclear significance. Out of a total of 6,441 people, 3,430 (or 53%) were female.
The PANGEA model (BM) outperformed the 20/2/20 model in predicting the transition from smoldering to active multiple myeloma, with a C-statistic improvement of 0533 (0.480-0.709) at visit 1, 0613 (0.504-0.704) at visit 2, and 0.720 (0.592-0.775) at visit 3 among the validation cohort 1. Prediction of smoldering multiple myeloma progression to multiple myeloma was enhanced by the PANGEA model (no BM) in comparison to the 20/2/20 model, with the C-statistic increasing from 0.534 (0.501–0.672) at visit 1 to 0.692 (0.614–0.736) at visit 2 and from 0.573 (0.518–0.647) to 0.693 (0.605–0.734) at visit 3 in validation cohort 1. With C-statistic increases from 640 (518-0.718) to 729 (643-0.941) for the PANGEA model (BM) and 670 (523-0.729) to 879 (586-0.938) for the PANGEA model (MM), the PANGEA models outperformed the IMWG rolling model at visit 1 in the validation cohort 2 for predicting monoclonal gammopathy of undetermined significance (no BM). Patients with precursor illness will be able to get more precise assessments of their risk of progression to multiple myeloma if the PANGEA models are used in clinical practice.
Source: sciencedirect.com/science/article/pii/S2352302622003866