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R-ISS and NLR-ISS can Predict Time to Treatment in Smoldering Myeloma | Abstract
Journal of Clinical Trials

Journal of Clinical Trials
Open Access

ISSN: 2167-0870

+44 20 3868 9735

Abstract

R-ISS and NLR-ISS can Predict Time to Treatment in Smoldering Myeloma

Romano A, Consoli ML, Auteri G, Parisi M, Parrinello NL, Giallongo C, Tibullo D, Conticello C and Di Raimondo F

Objectives: We recently identified the ratio between absolute neutrophils count and absolute lymphocyte count, NLR ≥ 2, combined to ISS as a predictor of progression free survival (PFS) and overall survival (OS) in patients younger than 65 years with symptomatic Multiple Myeloma (MM). We retrospectively examined the NLR-ISS in 165 consecutive smoldering Myeloma (sMM) accessed our Center between January 2004 and June 2014.

Methods: NLR was calculated using data obtained from the complete blood count (CBC) at diagnosis and subsequently correlated with time to treatment (TTT) for symptomatic MM. All patients underwent to bone marrow evaluation to estimate plasma cells infiltration (BMPC) and cytogenetic alterations detectable by FISH, Magnetic Resonance Imaging (MRI) to detect bone lesions, serum free-lite chain evaluation (sFLC). Patients with bone marrow plasma cells >60% or lytic lesions at MRI were excluded from further analysis.

Results: We identified 127 patients with sMM defined accordingly to the updated IMWG 2015 guidelines. The median NLR was 1.7 (range 0.6-10.5), lower than the value previously found for MM 1.9 (range 0.4-15.9, p=0.005. Higher NLR was independent from ISS stage, BMPC amount, high-risk FISH and sFLC.

Using NLR ≥ 2 we could not predict TTT. Indeed, in univariate analysis only BMPC ≥ 30% (p=0.003), albumin <3.5 g/dL (p=0.008), beta-2 microglobulin >3.5 g/L (p=0.0001), ratio of uninvolved/involved sFLC (p=0.0002), immunoparesis (p=0.016) and LDH (p<0.0001) could predict TTT. In multivariate analysis, these three parameters were independent (p<0.0001). In multivariate analysis, LDH and beta-2 microglobulin were weak but significant independent predictors of outcome. Since both are part of R-ISS, we applied ISS, R-ISS and NLR-ISS to identify TTT at 60 months. R-ISS resulted the strongest system to distinguish patients in stage I and stage II with TTT at 60 months respectively 92% and 62.7% (p=0.0002). NLR-ISS could distinguish patients in stage I and stage II with TTT at 60 months respectively 91.9% and 67.8% (p=0.007).

Conclusion: We could not confirm previously proposed parameters to predict time to treatment using the new definition of sMM. However, ISS and its improved variants R-ISS and NLR-ISS were able to identify patients in stage I with excellent outcome at 60 months. Prospective larger series are needed to use R-ISS to identify high-risk sMM.