Clinical Feature and Predictive Model for Transplanted Patients with Functional High-Risk Multiple Myeloma

Objective: Despite these therapeutic advancements, a subset of patients continues to exhibit poor outcomes, highlighting the complexity and heterogeneity of the disease. These subsets of patients with MM who relapsed early, even without any high-risk features at baseline, were defined as functional high-risk (FHR) MM. And the prognosis of FHR MM was worse than high-risk (HR) MM. Therefore, it is important to distinguish patients with FHR MM from patients with standard risk (SR). In this study, we explored risk factors for clinical features to identify through comparing FHR with SR. Additionally, we sought to develop a prognostic model to predict FHR MM patients with transplantation according to risk factors.

Methods: We conducted a retrospective analysis of clinical data from MM patients diagnosed between January 1, 2007, and March 22, 2024, who underwent autologous hematopoietic stem cell transplantation (ASCT) with a minimum follow-up of 24 months after transplantation. The cases were randomly divided into a training set (151 cases, 70%) and a validation set (65 cases, 30%). Univariate and multivariate analyses were performed to identify independent risk factors for FHR MM. A predictive model was constructed using logistic regression analysis and internally validated.

Results: A total of 357 MM patients were included in our study, with 49 (13.73%) classified as FHR, 167 (46.78%) as SR, and 141 (39.50%) as GHR. The FHR groups had the shortest survival compared with the HR and the SR groups, including PFS and OS. The median PFS was 22.5 months, 49.7 months, and 111.2 months in the FHR, HR, and SR groups, respectively. The median OS was 39.7 months, 71 months, and 132.1 months in the FHR, HR, and SR groups, respectively. Univariate and multivariate analyses revealed that LDH ≥190U/L (OR=2.70, P=0.015), PET-CT SUVmax≥7.5 at onset of MM (OR=3.13, P=0.035), the proportions of PET-CT SUVmax decline after induction therapy ≥80% (OR=4.79, P=0.044), PLT <80*10^9/L (OR=10.55, P=0.012), and ferritin ≥650μg/L (OR=4.76, P=0.031) were independent risk factors for FHR MM. Additionally, Pattern B of M-protein decline (OR=0.39, P=0.012) and achieving CR after ASCT (OR=0.37, P=0.009) were identified as independent protective factors. A predictive model incorporating these factors demonstrated areas under the ROC curves of 0.753 for the training set and 0.857 for the validation set. Calibration plots, Decision Curve Analysis (DCA) curves, and Hosmer-Lemeshow test results indicated that the model has good predictive accuracy.

Conclusion: This study developed a predictive nomogram model for FHR MM patients with transplantation, which shows promising predictive capabilities.

No relevant conflicts of interest to declare.