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    MRD and Biomarkers

    Evaluating ethnicity-specific risk stratification models of multiple myeloma with global risk stratification models in real life scenario

    P-172: Evaluating ethnicity-specific risk stratification models of multiple myeloma with global risk stratification models in real life scenario

    Friday, September 27, 2024
    Introduction: Ethnicity-related biological diversity affects treatment outcomes, yet global risk stratification models for multiple myeloma (MM) often overlook this factor. Previously, we introduced ethnicity-specific risk stratification models: the Consensus-based Risk-Stratification System (CRSS) and Modified Risk Staging (MRS), both incorporating high-risk cytogenetic aberrations (HRCA). This study compares these models in Indian MM patients against global risk stratification models.

    Methods: Patients with MM (n = 244) were stratified as per the ISS, R-ISS, MRS, CRSS, and R2-ISS models. In addition, patients were labelled as high-risk (HR) based on the presence of IgH translocations [t(4::14, t(14::16, t(14::20)] with either 1pdel and/or 1q gain or 17pdel and/or TP53 mutation and compared with those not having HR features.

    Progression-free survival (PFS) and overall survival (OS) were assessed using Kaplan-Meier survival curves and Cox proportional hazard models. The risk stratification models were compared, and a higher Harrel's C-index and a lower Akaike Information Criterion (AIC) indicated the superiority of a model.

    Results: Out of 244 patients with MM, 163 were treated with a triplet regimen (VRD=86, VCD=59, VTD=11, VPD=7), 67 with a doublet regimen (VD=20, RD=37, DT=10), while 9 and 5 patients, respectively, received melphalan and cyclophosphamide-based regimens as their initial therapy.

    The ISS and MRS models do not consider HRCA, and the ethnicity-specific model, namely MRS, demonstrated better performance with a superior C-index for both PFS and OS compared to the ISS. Among the models incorporating HRCA, the CRSS, which includes ethnicity-specific information, yielded the highest C-index and the lowest AIC (Table 1).



    Table 1: Performance Indices of Various Risk Stratification Models for Progression-Free Survival (PFS) and Overall Survival (OS) in Multiple Myeloma


























































































    Model


    PFS


    OS


    HR


    95% CI of HR


    C-Index


    AIC


    HR


    HR - 95% CI


    C-Index


    AIC


    ISS


    1.888


    1.421-2.510


    0.6154


    1259.84


    1.538


    1.179-2.0


    0.5609


    1438.7


    MRS


    1.902


    1.462-2.47


    0.6273


    1259.8


    1.865


    1.443-2.411


    0.6134


    1427





    R-ISS


    2.304


    1.715-3.059


    0.6359


    1251.97


    1.781


    1.347-2.356


    0.5886


    1433.65


    CRSS


    2.484


    1.889-3.265


    0.6678


    1240.2


    2.034


    1.59-2.60


    0.6244


    1417.8


    RISS2


    1.995


    1.517-2.624


    0.62


    1254.37


    1.95


    1.479-2.571


    0.6089


    1423.87


    HR-MM


    2.008


    1.288-3.130


    0.5456


    1275.01


    2.296


    1.555-3.390


    0.5716


    1435.35




    Conclusions: The current study highlights the improved efficacy of risk stratification models that utilize ethnicity-specific thresholds for prognostic biomarkers in MM. The CRSS and MRS models include extra biomarkers, i.e., serum calcium, hemoglobin, age, and eGFR, along with modified thresholds for albumin and beta2 microglobulin. Consideration of the unique biology of specific populations and incorporating additional parameters can help improve the performance of risk stratification models.