Prognostic factors in 448 newly diagnosed multiple myeloma receiving bortezomib-based induction: impact of ASCT, transplant refusal and high-risk MM.
Hoi Ki Karen TangChi Yeung FungYu Yan HwangHarold LeeGrace LauSze Fai YipBonnie KhoChi Kuen LauKwan Hung LeungElaine AuEric TseJoycelyn SimYok Lam KwongChor-Sang ChimPublished in: Bone marrow transplantation (2024)
In Hong Kong, newly diagnosed multiple myeloma (NDMM) receives bortezomib-based triplet induction. Upfront autologous stem cell transplant (ASCT) is offered to transplant eligible (TE) patients (NDMM ≤ 65 years of age), unless medically unfit (TE-unfit) or refused (TE-refused). Data was retrieved for 448 patients to assess outcomes. For the entire cohort, multivariate analysis showed that male gender (p = 0.006), international staging system (ISS) 3 (p = 0.003), high lactate dehydrogenase (LDH) (p = 7.6 × 10 -7 ) were adverse predictors for overall survival (OS), while complete response/ near complete response (CR/nCR) post-induction (p = 2.7 × 10 -5 ) and ASCT (p = 4.8 × 10 -4 ) were favorable factors for OS. In TE group, upfront ASCT was conducted in 252 (76.1%). Failure to undergo ASCT in TE patients rendered an inferior OS (TE-unfit p = 1.06 × 10 -8 , TE-refused p = 0.002) and event free survival (EFS) (TE-unfit p = 0.00013, TE-refused p = 0.002). Among TE patients with ASCT, multivariate analysis showed that age ≥ 60 (p = 8.9 × 10 -4 ), ISS 3 (p = 0.019) and high LDH (p = 2.6 × 10 -4 ) were adverse factors for OS. In those with high-risk features (HR cytogenetics, ISS 3, R-ISS 3), ASCT appeared to mitigate their adverse impact. Our data reaffirmed the importance of ASCT. The poor survival inherent with refusal of ASCT should be recognized by clinicians. Finally, improved outcome with ASCT in those with high-risk features warrant further studies.
Keyphrases
- newly diagnosed
- prognostic factors
- multiple myeloma
- end stage renal disease
- chronic kidney disease
- ejection fraction
- stem cells
- free survival
- metabolic syndrome
- type diabetes
- mesenchymal stem cells
- skeletal muscle
- big data
- lymph node
- electronic health record
- machine learning
- mental health
- adipose tissue
- patient reported
- weight loss