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Published on: July 24, 2013
Development and validation of a novel frailty model for the patients with newly diagnosed multiple myeloma
Biao Tian1, Li Xu1, Shuangshuang Jia1
1Department of Hematology, Xijing Hospital, Air Force Medical University, Xi'an, 710032, China.
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.
Insights
A new frailty model for multiple myeloma (MM) patients accurately identifies frail individuals, improving survival predictions and treatment guidance. This model aids in stratifying patients and predicting adverse events, enhancing care for this aging population.
Area of Science:
- Hematology
- Geriatrics
- Oncology
Background:
- Aging is prevalent in multiple myeloma (MM), with over 30% of patients being frail at diagnosis.
- Frailty in MM patients is linked to reduced treatment tolerance, lower quality of life, and decreased survival rates.
- Existing frailty models for MM face challenges in broad application and timely treatment adjustments.
Purpose of the Study:
- To develop and validate a novel, accurate frailty model for patients with multiple myeloma (MM).
- To improve the prediction of overall survival (OS) and progression-free survival (PFS) in MM patients.
- To effectively distinguish and stratify frail subgroups within the MM patient population.
Main Methods:
- Retrospective analysis of 606 newly diagnosed MM patients.
- Development of a novel frailty model (Fmodel) using LASSO regression, random survival forest, and Cox regression.
- Comparison of Fmodel performance against the Smodel and Revised International Staging System (RISS).
Main Results:
- The Fmodel incorporates age, HCT-CI, ECOG-PS, ISS, and PNI, stratifying patients into fit, intermediate, and frail categories.
- Fmodel demonstrated superior calibration, discrimination, and predictive ability for OS and PFS compared to Smodel and RISS.
- Frail patients showed increased risk of grade ≥2 nonhematologic adverse events with conventional treatment; Fmodel accurately predicted early mortality.
Conclusions:
- A novel frailty model (Fmodel) was developed using key clinical variables (age, HCT-CI, ECOG-PS, ISS, PNI) for MM patients.
- The Fmodel effectively identifies and stratifies frail individuals within the MM population.
- This model enhances the prediction of survival outcomes and adverse events, aiding clinical decision-making.
