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Recent reports have raised concerns about the association of chimeric antigen receptor T cell (CAR-T) with non-negligible cardiotoxicity, particularly atrial arrhythmias. First, we conducted a pharmacovigilance study to assess the reporting of atrial arrhythmias following CD19-directed CAR-T. Subsequently, to determine the incidence, risk factors and outcomes of atrial arrhythmias post-CAR-T, we compiled a retrospective single-centre cohort of non-Hodgkin lymphoma patients. Only commercial CAR-T products were considered. Atrial arrhythmias were nearly fourfold more likely to be reported after CAR-T therapy compared to all other cancer patients in the FAERS (adjusted ROR = 3.76 [95% CI 2.67-5.29]). Of the 236 patients in our institutional cohort, 23 (10%) developed atrial arrhythmias post-CAR-T, including 12 de novo arrhythmias, with most (83%) requiring medical intervention. Atrial arrhythmias frequently co-occurred with cytokine release syndrome and were associated with higher post-CAR-T infusion peak levels of IL-10, TNF-alpha and LDH, and lower trough levels of fibrinogen. In a multivariable analysis, risk factors for atrial arrhythmia were history of atrial arrhythmia (OR = 6.80 [2.39-19.6]) and using CAR-T product with a CD28-costimulatory domain (OR = 5.17 [1.72-18.6]). Atrial arrhythmias following CD19-CAR-T therapy are prevalent and associated with elevated inflammatory biomarkers, a history of atrial arrhythmia and the use of a CAR-T product with a CD28 costimulatory domain., (© 2024 British Society for Haematology and John Wiley & Sons Ltd.)

Comorbidity assessment before allogeneic haematopoietic cell transplantation (allo-HCT) is essential for estimating non-relapse mortality (NRM) risk. We previously developed the Simplified Comorbidity Index (SCI), which captures a small number of 'high-yield' comorbidities and older age. The SCI was predictive of NRM in myeloablative CD34-selected allo-HCT. Here, we evaluated the SCI in a single-centre cohort of 327 patients receiving reduced-intensity conditioning followed by unmanipulated allografts from HLA-matched donors. Among the SCI factors, age above 60, mild renal impairment, moderate pulmonary disease and cardiac disease were most frequent. SCI scores ranged from 0 to 8, with 39%, 20%, 20% and 21% having scores of 0-1, 2, 3 and ≥4 respectively. Corresponding cumulative incidences of 3-year NRM were 11%, 16%, 22% and 27%; p = 0.03. In multivariable models, higher SCI scores were associated with incremental risks of all-cause mortality and NRM. The SCI had an area under the receiver operating characteristic curve of 65.9%, 64.1% and 62.9% for predicting 1-, 2- and 3-year NRM versus 58.4%, 60.4% and 59.3% with the haematopoietic cell transplantation comorbidity index. These results demonstrate for the first time that the SCI is predictive of NRM in patients receiving allo-HCT from HLA-matched donors after reduced-intensity conditioning., (© 2023 British Society for Haematology and John Wiley & Sons Ltd.)

Data describing outcomes of chimeric antigen receptor (CAR) T-cell therapy in patients with secondary central nervous system (SCNS) involvement of mantle cell lymphoma (MCL) are limited. We identified 10 patients with MCL and SCNS involvement treated with anti-CD19 CAR T-cell therapy at three US academic centres. Frequent objective responses were observed in the CNS (86%) and systemically (90%), and the 1-year progression-free survival was 47%. Seven patients developed immune-effector-cell-associated-neurotoxicity-syndrome (n = 2 Grade 1, n = 5 Grade 3). Our results suggest that anti-CD19 CAR T-cell therapy in this setting is feasible and additional data regarding neurotoxicity in this population may be warranted., (© 2023 British Society for Haematology and John Wiley & Sons Ltd.)

Digitalization of the medical record and integration of genomic methods into clinical practice have resulted in an unprecedented wealth of data. Machine learning is a subdomain of artificial intelligence that attempts to computationally extract meaningful insights from complex data structures. Applications of machine learning in haematological scenarios are steadily increasing. However, basic concepts are often unfamiliar to clinicians and investigators. The purpose of this review is to provide readers with tools to interpret and critically appraise machine learning literature. We begin with the elucidation of standard terminology and then review examples in haematology. Guidelines for designing and evaluating machine-learning studies are provided. Finally, we discuss limitations of the machine-learning approach., (© 2020 British Society for Haematology and John Wiley & Sons Ltd.)