Determinants of Eligibility and Timing of Autologous Transplantation in Multiple Myeloma: The Role of R-MCI and Diagnostic Plasma Cell Burden.

Background: Autologous stem cell transplantation (ASCT) remains a cornerstone in the management of multiple myeloma (MM). However, the optimal timing of ASCT and the factors determining whether patients ultimately undergo transplantation remain unclear in real-world practice. The Revised Myeloma Comorbidity Index (R-MCI) was developed to quantify patient fitness but its influence on transplant eligibility and timing has not been fully characterized. Methods: We conducted a retrospective single-center study including 137 patients with newly diagnosed MM between 2015 and 2025. Clinical parameters recorded at diagnosis included age, sex, performance status, renal and pulmonary function, cytogenetic risk, International Staging System (ISS) stage, and bone marrow plasma cell infiltration. The R-MCI was calculated for all patients. Transplant timing was categorized as early (≤12 months) or delayed (>12 months) after diagnosis. Logistic regression analysis was performed, and variables with p < 0.10 in univariate analyses were included in the multivariate model. Early versus delayed ASCT was defined as ≤12 months or >12 months from diagnosis, respectively. Results: ASCT was performed in 61/137 patients (44.5%), while 42.6% of these underwent early transplantation. Transplanted patients were significantly younger (<65 years: 82.0% vs. 25.0%, p < 0.001) and had lower R-MCI scores (median 0 vs. 1, p < 0.001) compared with non-transplanted patients, while plasma cell infiltration and ISS stage did not differ. In multivariate analysis, R-MCI was the only variable showing a trend toward predicting early transplantation (OR 0.27, 95% CI 0.07-1.06, p = 0.06). Conclusions: In real-world MM management, patients quantified by R-MCI appear to play a more prominent role than disease burden in determining both eligibility for and timing of ASCT. Incorporating comorbidity indices alongside ISS may enhance individualized transplant decision-making and optimize treatment outcomes.