A novel prognostic model integrating host vulnerability and tumor biology for elderly patients with diffuse large B-cell lymphoma: the BAMAL score

Background: Diffuse large B-cell lymphoma (DLBCL) is the most prevalent subtype of non-Hodgkin lymphoma in adults, with a median age of diagnosis over 65 years. The clinical management of this population presents a growing challenge due to patient heterogeneity, multiple comorbidities, and reduced tolerance to standard immunochemotherapy. Existing prognostic models, such as the International Prognostic Index (IPI), Revised-IPI (R-IPI), and National Comprehensive Cancer Network-IPI (NCCN-IPI), primarily focus on tumor-centric features and often fail to adequately capture patient-specific vulnerability, leading to imprecise risk stratification. This study aimed to develop and internally validate a new, practical prognostic model, the BAMAL score, which integrates established tumor features with novel geriatric assessment parameters and biochemical markers, and to quantitatively compare its predictive performance with traditional models. Methods: This was a single-center retrospective cohort study of 136 consecutive, newly diagnosed DLBCL patients aged 65 years or older, treated at the First Affiliated Hospital of Zhengzhou University between January 1, 2022, and June 30, 2023. Univariate and multivariate Cox proportional hazards regression analyses were employed to identify independent prognostic factors for overall survival (OS) and progression-free survival (PFS). To account for host vulnerability and competing mortality risks inherent to the elderly, the BAMAL score was constructed using independent prognostic factors identified via backward stepwise multivariate Cox regression (incorporating continuous variables), which were subsequently dichotomized for model formulation. Chi-square tests and trend tests were utilized to evaluate the associations between BAMAL grading, treatment modalities, and therapeutic responses. Discriminatory performance was evaluated using time-dependent Area Under the Curve (AUC) and compared via DeLong's test, while model fit was assessed using the Akaike Information Criterion (AIC). Additionally, calibration curves were plotted to assess the agreement between predicted and observed survival at 1 and 2 years, and Decision Curve Analysis (DCA) was performed to determine the clinical net benefit of the model for both OS and PFS at these time points. Results: ). Nevertheless, the BAMAL score provided the best model fit as evidenced by the lowest AIC value (366.5). Furthermore, calibration curves demonstrated excellent agreement between predicted and observed probabilities for both OS and PFS at 1 and 2 years. Decision curve analysis (DCA) indicated that while the BAMAL score demonstrated clinical net benefit comparable to NCCN-IPI and IPI/R-IPI for 1-year prediction, it exhibited superior net benefit for 2-year prediction, particularly in the high-risk threshold probability range (>60%), where traditional indices showed limited utility. Conclusion: The BAMAL score is a novel, practical, and robust prognostic model for elderly DLBCL patients. It successfully integrates features of tumor biology with host-related vulnerability, providing superior predictive accuracy and clinical net benefit compared to standard indices. Crucially, the score offers actionable insights for decision-making: supporting the optimization of standard immunochemotherapy for intermediate-risk patients, while identifying high-risk patients for whom current cytotoxic regimens are futile, underscoring the urgent need for novel, chemotherapy-free therapeutic approaches.