Frontiers in oncology · 2024
Prognostic significance of β2-microglobulin decline index in multiple myeloma.
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Zhang Tianyu, Lin Zhili, Zheng Ziwei, Wang Quanqiang, Zhou Shujuan, Zhang Bingxin, Zheng Dong, Chen Zixing, Zheng Sisi, Zhang Yu, Lin Xuanru, Dong Rujiao, Chen Jingjing, Qian Honglan, Hu Xudong, Zhuang Yan, Zhang Qianying, Jin Zhouxiang, Jiang Songfu, Ma Yongyong
Аннотация
PURPOSE: To assess the prognostic significance of β2-microglobulin decline index (β2M DI) in multiple myeloma (MM).
METHODS: 150 MM patients diagnosed with MM were enrolled in this study. Cox proportional hazards model was used to analyze the uni- and multivariate prognosis in training cohort (n=105). A new combined prognostic model containing β2M DI was built up based on the data in training cohort. The validation group was used to verify the model.
RESULTS: β2M DI showed significant correlation with prognosis in both uni- and multivariate analyses and had a good correlation with complete response (CR) rate and deep remission rate. The ROC and calibration curves in validation cohort (n=45) indicated a good predictive performance of the new model. Based on the median risk score of the training group, we classified patients into high- and low- risk groups. In both training and validation groups, patients in the low-risk group had longer overall survival (OS) time than that in the high-risk group (p<0.05).
CONCLUSION: β2M DI is a good predictive index for predicting treatment response and survival time in MM patients. The prognostic model added with β2M DI showed a better correlation with OS.
METHODS: 150 MM patients diagnosed with MM were enrolled in this study. Cox proportional hazards model was used to analyze the uni- and multivariate prognosis in training cohort (n=105). A new combined prognostic model containing β2M DI was built up based on the data in training cohort. The validation group was used to verify the model.
RESULTS: β2M DI showed significant correlation with prognosis in both uni- and multivariate analyses and had a good correlation with complete response (CR) rate and deep remission rate. The ROC and calibration curves in validation cohort (n=45) indicated a good predictive performance of the new model. Based on the median risk score of the training group, we classified patients into high- and low- risk groups. In both training and validation groups, patients in the low-risk group had longer overall survival (OS) time than that in the high-risk group (p<0.05).
CONCLUSION: β2M DI is a good predictive index for predicting treatment response and survival time in MM patients. The prognostic model added with β2M DI showed a better correlation with OS.