Risk Stratification of Newly Diagnosed Multiple Myeloma: Predictive Value of First‐Year Biomarker Trajectories Identified by Group‐Based Trajectory Modeling
- 1Department of Hematology, Institute of Hematology, West China Hospital, Sichuan University, Chengdu, China.
- 2West China School of Medicine, Sichuan University, Chengdu, China.
- 3Beijing Goodwill Hessian Health Technology Co. Ltd., Beijing, China.
Abstract
Background Multiple myeloma (MM) exhibits significant heterogeneity. Traditional risk stratification often relies on static baseline data, failing to capture the dynamic evolution of biomarkers. This study aimed to develop a prognostic model incorporating longitudinal biomarker trajectories to improve risk stratification for newly diagnosed MM patients. Methods We retrospectively analyzed 635 patients from West China Hospital between February 1, 2017 and December 30, 2022. Biomarkers including M protein, free light chains (FLC), creatinine (Cr), hemoglobin (Hb), immunoglobulin levels, and others were collected monthly within the first‐year post‐treatment. Patients were randomly divided into an internal training (70%) and validation (30%) cohort. Group‐based trajectory modeling (GBTM) and Kaplan–Meier analysis were used to identify distinct biomarker evolution patterns, and the GBTM risk model was validated by the lowest mean squared error (LMSE) method. Cox regression analysis and a nomogram were employed to develop a prognostic model to provide individualized survival probability estimates and were validated in the internal validation cohort using time‐dependent ROC analysis. Results GBTM identified dFLC, IgM, Cr, and Hb trajectories as significant prognostic factors. Patients scoring ≥ 3 (high‐risk) showed significantly poorer overall survival than those scoring < 3 (low‐risk) in both the training (median OS 32.2 months vs. not reached, p < 0.0001) and validation cohorts (36.8 months vs. not reached, p < 0.005). Then we established a nomogram that included the GBTM risk group, ISS stage, age, frontline treatment, pulmonary disease, cardiovascular disease, and thrombotic events. The nomogram demonstrated good predictive performance with AUC values consistently above 0.7 for 2‐ and 3‐year survival in both cohorts (Training: 0.71, 0.70; Validation: 0.70, 0.70). Conclusion Our study successfully developed a prognostic risk stratification model for MM patients using biomarker trajectories. This model may help identify high‐risk patients and predict long‐term survival, offering a valuable tool for personalized treatment planning.
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BibTeX
@article{bai2026risk,
title = {{Risk Stratification of Newly Diagnosed Multiple Myeloma: Predictive Value of First‐Year Biomarker Trajectories Identified by Group‐Based Trajectory Modeling}},
author = {Hexiang Bai and Wenjiao Tang and Yan Li and Bing Xiang and Kunlian Li and Min Sun and Yuhuan Zheng and Li Zhang and Ting Niu},
journal = {Cancer Medicine},
year = {2026},
month = nov,
doi = {10.1002/cam4.72503},
url = {https://doi.org/10.1002/cam4.72503},
note = {CC BY-NC-ND},
}
RIS
TY - JOUR
TI - Risk Stratification of Newly Diagnosed Multiple Myeloma: Predictive Value of First‐Year Biomarker Trajectories Identified by Group‐Based Trajectory Modeling
AU - Hexiang Bai
AU - Wenjiao Tang
AU - Yan Li
AU - Bing Xiang
AU - Kunlian Li
AU - Min Sun
AU - Yuhuan Zheng
AU - Li Zhang
AU - Ting Niu
PY - 2026
DA - 2026/11/01
JO - Cancer Medicine
DO - 10.1002/cam4.72503
UR - https://doi.org/10.1002/cam4.72503
AB - Background Multiple myeloma (MM) exhibits significant heterogeneity. Traditional risk stratification often relies on static baseline data, failing to capture the dynamic evolution of biomarkers. This study aimed to develop a prognostic model incorporating longitudinal biomarker trajectories to improve risk stratification for newly diagnosed MM patients. Methods We retrospectively analyzed 635 patients from West China Hospital between February 1, 2017 and December 30, 2022. Biomarkers including M protein, free light chains (FLC), creatinine (Cr), hemoglobin (Hb), immunoglobulin levels, and others were collected monthly within the first‐year post‐treatment. Patients were randomly divided into an internal training (70%) and validation (30%) cohort. Group‐based trajectory modeling (GBTM) and Kaplan–Meier analysis were used to identify distinct biomarker evolution patterns, and the GBTM risk model was validated by the lowest mean squared error (LMSE) method. Cox regression analysis and a nomogram were employed to develop a prognostic model to provide individualized survival probability estimates and were validated in the internal validation cohort using time‐dependent ROC analysis. Results GBTM identified dFLC, IgM, Cr, and Hb trajectories as significant prognostic factors. Patients scoring ≥ 3 (high‐risk) showed significantly poorer overall survival than those scoring < 3 (low‐risk) in both the training (median OS 32.2 months vs. not reached, p < 0.0001) and validation cohorts (36.8 months vs. not reached, p < 0.005). Then we established a nomogram that included the GBTM risk group, ISS stage, age, frontline treatment, pulmonary disease, cardiovascular disease, and thrombotic events. The nomogram demonstrated good predictive performance with AUC values consistently above 0.7 for 2‐ and 3‐year survival in both cohorts (Training: 0.71, 0.70; Validation: 0.70, 0.70). Conclusion Our study successfully developed a prognostic risk stratification model for MM patients using biomarker trajectories. This model may help identify high‐risk patients and predict long‐term survival, offering a valuable tool for personalized treatment planning.
SN - 2045-7634
ER -