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Background <p>The predominance of ageing in patients with multiple myeloma (MM) results in at least 30% of patients being classified as frail at diagnosis. Compared with healthy individuals, frail individuals exhibit reduced treatment tolerance and lower quality of life, which are correlated with decreased survival rates. Although various frailty models for MM have been developed, challenges remain in their broad application and timely adjustment of treatment based on frailty evaluations across heterogeneous patient groups.</p> Methods <p>This retrospective study analysed data from 606 patients with newly diagnosed MM at Xijing Hospital between May 2006 and August 2022. The dataset was randomly divided into a development set (<i>N</i> = 424) and a validation set (<i>N</i> = 182). A novel frailty model (Fmodel) was developed using LASSO regression, random survival forest, and both univariate and multivariate Cox regression analyses. The model can predict overall survival (OS) and progression-free survival (PFS) in patients with MM while distinguishing frail subgroups. Its performance was compared with the previously established frailty model (Smodel) and the Revised International Staging System (RISS).</p> Results <p>The Fmodel incorporates five variables: age, HCT-CI, ECOG-PS, ISS, and PNI. It stratified patients into three categories: fit, intermediate fit, and frail. Compared with the Smodel and RISS, the Fmodel exhibited robust discrimination and stability in predicting OS and PFS in both the development and validation sets and demonstrated superior calibration, discrimination, clinical applicability, and predictive ability. Frail patients were found to be at a greater risk for grade ≥ 2 nonhematologic adverse events (AEs) when receiving conventional-dose treatment (<i>p</i> &lt; 0.05). Furthermore, the Fmodel provided accurate predictions of early mortality in patients with MM.</p> Conclusion <p>We developed a novel frailty model for patients with MM based on age, HCT-CI, ECOG-PS, ISS, and PNI, effectively identifying the frail population.</p>

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Development and validation of a novel frailty model for the patients with newly diagnosed multiple myeloma

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月28日

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  • Biao Tian,
  • Li Xu,
  • Shuangshuang Jia,
  • Wenrui Sun,
  • Chunyan Zhang,
  • Juan Feng,
  • Juan Hui,
  • Miaoyu Li,
  • Wanting Xiao,
  • Lulu Wang,
  • Ruochen Wu,
  • Yanxia Weng,
  • Zhenyang Li,
  • Jiao Jia,
  • Zeyi Ai,
  • Hailong Tang,
  • Guangxun Gao

摘要

Background

The predominance of ageing in patients with multiple myeloma (MM) results in at least 30% of patients being classified as frail at diagnosis. Compared with healthy individuals, frail individuals exhibit reduced treatment tolerance and lower quality of life, which are correlated with decreased survival rates. Although various frailty models for MM have been developed, challenges remain in their broad application and timely adjustment of treatment based on frailty evaluations across heterogeneous patient groups.

Methods

This retrospective study analysed data from 606 patients with newly diagnosed MM at Xijing Hospital between May 2006 and August 2022. The dataset was randomly divided into a development set (N = 424) and a validation set (N = 182). A novel frailty model (Fmodel) was developed using LASSO regression, random survival forest, and both univariate and multivariate Cox regression analyses. The model can predict overall survival (OS) and progression-free survival (PFS) in patients with MM while distinguishing frail subgroups. Its performance was compared with the previously established frailty model (Smodel) and the Revised International Staging System (RISS).

Results

The Fmodel incorporates five variables: age, HCT-CI, ECOG-PS, ISS, and PNI. It stratified patients into three categories: fit, intermediate fit, and frail. Compared with the Smodel and RISS, the Fmodel exhibited robust discrimination and stability in predicting OS and PFS in both the development and validation sets and demonstrated superior calibration, discrimination, clinical applicability, and predictive ability. Frail patients were found to be at a greater risk for grade ≥ 2 nonhematologic adverse events (AEs) when receiving conventional-dose treatment (p < 0.05). Furthermore, the Fmodel provided accurate predictions of early mortality in patients with MM.

Conclusion

We developed a novel frailty model for patients with MM based on age, HCT-CI, ECOG-PS, ISS, and PNI, effectively identifying the frail population.