British Journal of Haematology最新文献

Damage-associated molecular patterns (DAMPs), released into the extracellular space following tissue injury, are increasingly recognised as potent procoagulant molecules integral to haemostasis and the pathogenesis of thrombosis. Their procoagulant influence spans all phases of the cell-based model of coagulation while simultaneously extending beyond traditional haemostatic pathways through direct modulation of inflammatory and innate immune responses. By coupling coagulation and immunity, DAMPs drive the self-perpetuating cycle of immunothrombosis characteristic of many critical illnesses. Targeting this underexplored interface offers the promise of novel diagnostic and therapeutic approaches, particularly in conditions where coagulopathy coexists with hyperinflammatory states.

Investigation and treatment of acquired coagulation factor inhibitors.

Risk stratification is the backbone of clinical decision-making for patients with haematological malignancies. Models currently used in routine clinical practice often rely on static rules, conventional statistics and expert consensus. While pragmatic and accessible, these models fail to capture the complex biological interactions and patient heterogeneity, especially as data dimensionality grows given the availability of more comprehensive molecular analysis. Artificial intelligence (AI), particularly machine learning (ML), offers the potential to process such high-dimensional data, uncover latent patterns and provide dynamic, individualized risk predictions. In this review, we introduce haematologists to core concepts and limitations of AI-based risk stratification. We examine studies in myeloid and lymphoid neoplasms developing AI models that challenge current standard models and identify novel disease subtypes and risk markers. While these models offer unique potential for data-driven personalized decision support, several challenges remain to routine applicability such as training cohort composition, validation and bias mitigation highlighting an urgent need for interdisciplinary collaboration and dialogue with regulatory agencies to ensure robust and safe model deployment. As data dimensionality, digital infrastructure and regulatory frameworks evolve, AI-guided risk stratification is poised to complement and potentially reshape clinical practice in malignant haematology requiring haematologists to be aware of potential capabilities and pitfalls.

Induction chemotherapy in fit de novo acute myeloid leukaemia (AML) patients has historically combined an anthracycline with standard-dose cytarabine ('7 + 3') despite complete response (CR) rates of 50%-70%. In May 2023, our institution adopted the utilization of cladribine, cytarabine, idarubicin and venetoclax (CLIA-VEN) for intensive remission-induction therapy. In this retrospective study, outcomes of newly diagnosed adult AML patients who were consecutively treated with CLIA-VEN (n = 20; 2023-2025) were compared to a historical cohort that received 7 + 3 (n = 42; 2016-2023). The median interquartile range [IQR] age was 54 [42-63] years, 54% were male and 58% had European LeukemiaNet (ELN) 2022 adverse-risk disease. CLIA-VEN was associated with a higher cumulative incidence of composite CR (CCR) (90% vs. 54.8%; p = 0.002) and minimal residual disease (MRD)-negative CCR (93.8% vs. 40.9%; p < 0.001) at 28 days. Any grade ≥ 2 end-organ toxicity (45% vs. 59.5%; p = 0.28) and early mortality (0% vs. 16.7%; p = 0.09) were not increased with CLIA-VEN. Utilizing propensity score guided inverse probability treatment weighting to balance baseline demographics, receipt of CLIA-VEN was associated with improved overall survival (p = 0.002). Acknowledging the limitations of a retrospective single-centre study, our data suggest that CLIA-VEN has higher efficacy without increasing toxicity and the potential to prolong survival compared to the current standard of care in de novo AML patients.

Of 84 patients with acute lymphoblastic leukaemia, non-Hodgkin lymphoma or multiple myeloma, 22 developed cytomegalovirus (CMV) viraemia following chimeric antigen receptor modified T (CAR-T) cell therapy, resulting in a proportion of 26% and a median time to reactivation of 15.5 days. Sustained high level of interleukin-6 (IL-6) is a significant risk factor for CMV reactivation without significant interaction across subgroups of diagnosis and target. Other risk factors include ≥3 grade cytokine release syndrome (CRS), B-cell maturation antigen (BCMA)-directed CAR and high-dose corticosteroids. Excessive IL-6 during CRS play an important role in the frequent CMV reactivation following CAR-T-cell immunotherapy.