Consensus-Based Ranking of Determinants for Frontline Therapy Selection in Chronic Lymphocytic Leukemia: A Conjoint Analysis.

Recent updates in Chronic Lymphocytic Leukemia (CLL) management guidelines emphasize three determinants for first-line treatment choice: patient age, clinical fitness, and key molecular features (IGHV and TP53/17p status). However, multiple therapeutic options are suggested within each clinical scenario, often without clear prioritization. We aimed to transparently rank host, disease and therapy-related determinants influencing frontline therapy selection in CLL, to enhance personalized prescription and clinical decision-making. To achieve this aim, a national expert panel of seven Italian key opinion leaders in Hematology participated in a structured consensus using conjoint analysis methodology. Five major treatment goals and fourteen host or disease-related determinants were selected based on literature and panelists expertise expert input. Conjoint analysis estimated determinant weights for each treatment goal: Time-to-Next-Treatment (TTNT), Rapidity of Disease Control, Infective Safety, Cardiovascular Safety, and Quality of Life. Furthermore, five commonly used regimens (ibrutinib, acalabrutinib, zanubrutinib, ibrutinib-venetoclax, venetoclax-obinutuzumab) were comparatively ranked according to their ability to accomplish each goal. Host/disease-related determinants with the highest impact on TTNT included life expectancy, cytopenias, and complex karyotype. Infective safety was mostly influenced by TP53 disruption and severe comorbidity burden, while cardiovascular safety was primarily affected by comorbidity type and frailty. Among therapies, zanubrutinib and ibrutinib-venetoclax achieved the most balanced profiles across goals. This national consensus provides a transparent, goal-oriented framework for CLL frontline therapy selection. Integrating conjoint analysis enables quantitative weighting of determinants and prioritization of treatment options beyond conventional categorical algorithms. The model supports evidence-based, individualized treatment strategies for CLL in clinical practice.