# The Strange Case of Functional High-Risk Multiple Myeloma Patients: Is It Possible to Identify Them in Clinical Practice?

## TL;DR

This study identifies high-risk multiple myeloma patients likely to relapse early using clinical markers and a scoring system, which could help tailor treatments.

## Contribution

A new predictive model using clinical parameters and a scoring system to identify functional high-risk multiple myeloma patients in clinical practice.

## Key findings

- Higher LDH and creatinine, lower hemoglobin and platelets are linked to early relapse in multiple myeloma.
- A scoring system based on these values and ECOG/ISS stage groups patients into low, intermediate, and high risk for early relapse.
- Treatment with anti-CD38 monoclonal antibodies reduces the chance of early relapse in high-risk patients.

## Abstract

This study examined 1026 multiple myeloma patients treated in 12 Italian centers to identify those at high risk of early relapse, known as functional high-risk (FHR). They had much shorter outcome measures compared to others. We found that higher LDH and creatinine levels, and lower hemoglobin and platelet counts, were linked to FHR. Using these values, we created a predictive formula similar to EASIX. Patients with a score above 2.0 were far more likely to be FHR. Additional risk factors included poor physical condition (ECOG ≥2) and advanced disease stage (ISS III). A scoring system grouped patients into low, intermediate, and high risk, with increasing FHR rates. Importantly, treatment with anti-CD38 monoclonal antibodies reduced the chance of early relapse. This model may help to identify high-risk patients early and tailor treatments to improve outcomes.

Background: Early relapse in multiple myeloma (MM) is a major predictor of poor prognosis, regardless of cytogenetic risk or treatment intensity. Methods: Here we analyzed 1026 MM patients treated across 12 Italian hematology centers. FHR was defined as progression-free survival (PFS) ≤18 months in transplant-eligible (TE) and ≤12 months in non-transplant-eligible (NTE) patients. Logistic regression and ROC analysis were used to identify significant predictors of FHR and build a risk score. Results: FHR status was identified in 175 patients (17%). These patients had significantly shorter PFS (7 vs. 57.5 months) and overall survival (19 months vs. not reached; p < 0.001). FHR status was associated with higher median LDH, lower Hb level, higher creatinine level and lower platelets count. Modified EASIX formula was built by these significant continuous variables, to be tested in a logistic analysis: [(LDH × creatinine)/(Hb × PLT) × 100]. A significantly higher rate of FHR was found with a score > 2.0 (89% vs. 11%, p < 0.001). Multivariate logistic analysis selected the above formula, ECOG PS ≥ 2 and ISS III as factors associated with FHR. Scoring these variables according to OR, three groups of patients were segregated with a rate of FHR patients of 7%, 29.5%, and 63.5%, respectively. Treatment with anti-CD38 monoclonal antibodies was associated with lower FHR frequency. Conclusions: This study proposes a simple, clinically applicable model to identify FHR MM patients early in their disease course. However, very in-depth biological tools, not available in clinical practice, are needed to identify singularly risk of becoming FHR.