An AI model classifies risks of early relapse post-CAR T-cell therapy in a multicenter real-world population with DLBCL.

Accumulating real-world (RW) evidence of axicabtagene ciloleucel (axi-cel) has demonstrated comparable performance to that of pivotal trials. However, ∼57% of patients eventually relapse, with most requiring additional therapies. Being able to identify patients with risk of early relapse enables clinicians to consider additional interventions to extend survival outcomes. This study aimed to first comprehensively evaluate the RW performance of axi-cel in the multicenter University of California Health Systems using automated computational approaches. Second, we developed a decision tree machine learning (ML) model to identify patients with risks of early relapse within 6 months. A total of 416 adult patients with diffuse large B-cell lymphoma (DLBCL) receiving axi-cel between 2017 and 2024 were included in the study. The median progression-free survival (PFS) and overall survival (OS) were 10.1 and 54.4 months; the 18-month PFS and OS rates were 41.6% and 65.5%, respectively. Severe CRS and ICANS were observed in 18.8% and 32.5% of patients. The ML model, relying on age and 6 routinely measured laboratory tests (lactate dehydrogenase, C-reactive protein, ferritin, hematocrit, platelet count, and prothrombin time), achieved a high area under the receiver operating characteristic curve score of 0.82. The decision curve analysis indicated positive net benefit of the model across a broad range (0-0.7) of decision thresholds, suggesting clinical utility in diverse scenarios. This study further confirmed the RW performance of axi-cel in diverse populations. Our ML model represented a novel approach to identify patients that may benefit from additional interventions to extend survival outcomes. Following prospective confirmation study, our model and decision tree approach may support clinical decision making in patients with high-risk DLBCL.