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SummaryIn individuals with high‐count monoclonal B‐cell lymphocytosis (MBL), we investigated if lymphadenopathy or splenomegaly found by imaging was associated with shorter time to first chronic lymphocytic leukaemia (CLL) therapy (TTFT) and overall survival (OS). Individuals with MBL seen at Mayo Clinic (2002–2019) were retrospectively divided into three cohorts based on imaging studies within 1 year of diagnosis: no imaging studies (Cohort A); imaging with no evidence of lymphadenopathy/splenomegaly (Cohort B); imaging with evidence of lymphadenopathy/splenomegaly (Cohort C). We compared baseline characteristics, TTFT and OS across the MBL cohorts to a cohort of individuals with small lymphocytic lymphoma (SLL). A total of 1078 patients were included: 640 with MBL and 438 with SLL. Compared to Cohort B, individuals in Cohort C were more likely to have unmutated immunoglobulin heavy chain variable region (IGHV) (43% vs. 25% p = 0.016), high‐risk fluorescence in situ hybridization (FISH) (del17p and del11q in 15% vs. 4%, p = 0.038) and higher expression of CD38 (31% vs. 16%; p = 0.011). After adjusting for sex and CLL‐International Prognostic Index (IPI), lymphadenopathy/splenomegaly was associated with a shorter TTFT (hazard ratio [HR] = 2.04, 95% confidence interval [CI]: 1.02–4.04, p = 0.042) but not OS (HR = 1.09, 95% CI: 0.62–1.92, p = 0.775). Lymphadenopathy/splenomegaly on imaging in individuals with high‐count MBL is associated with a more unfavourable risk profile and shorter time to first CLL‐directed therapy.

Immunotherapy has significantly affected cancer treatment and survival rates, accompanied by an increase in immune-related adverse events (irAEs) requiring new management strategies. irAEs can affect various organ systems and have varying severity levels, with higher rates observed when combining immune checkpoint inhibitors. National organizations such as ASCO, the National Comprehensive Cancer Network, the Society for Immunotherapy of Cancer, and the European Society for Medical Oncology have created guidelines for managing irAEs. This chapter expands on these guidelines by discussing practical strategies to improve the multidisciplinary management in irAE care, focusing on the who, what, and how to bridge gaps in care and enhance collaboration between academic and community oncology practices. Effective irAE management involves early recognition and guideline-adherent approaches using a multidisciplinary team, including oncologists, other subspecialists, primary care clinicians, and all care team members. Institutions are developing methods to integrate irAE care into clinical workflows, such as incorporating urgent care clinics and e-consults for efficient irAE management and developing hub-and-spoke models to extend specialized care from academic centers to community hospitals for equitable care delivery. Additionally, effective patient education is critical for improving irAE recognition and health literacy. The new ASCO Community of Practice called the Alliance for Support and Prevention of Immune-Related Adverse Events consortium and patient advocacy group Standing Together to Optimize Research, Interventions, and Education in irAEs initiatives aim to advance irAE clinical care, research, and education through global collaboration, standardized data collection, and improved outreach to patients and caregivers.

PURPOSE Integrating palliative care into oncology is essential, yet disparities in access and quality persist, particularly in low- and middle-income countries (LMICs). The ASCO guidelines advocate for early, routine, interdisciplinary palliative care for patients with advanced cancer. Barriers to implementing these recommendations include resource limitations, inadequate training, and cultural perceptions. Recognizing these challenges is essential for improving equitable access to palliative care worldwide. METHODS This prospective survey assessed adherence to ASCO recommendations for palliative care integration among LMIC health care providers (HCPs). Participants were recruited via e-mail, social media, and a list of members involved in the ASCO Palliative Care Communities of Practice from February to May 2024. The survey included sections on sociodemographic information, self-perceived adherence to ASCO guidelines on a 5-point Likert scale, and open-ended questions on implementation barriers. Data were collected using Research Electronic Data Capture system. Participants were grouped by WHO regions. Descriptive statistics were used to summarize characteristics and adherence scores, and chi-square tests were used to evaluate regional differences. Thematic analysis identified key themes from open-ended responses. RESULTS One hundred eighty HCPs participated; 62% was female, and 51.1% was age 35-44 years. Most were physicians (66%), and 50% lacked palliative care specialization. Adherence to ASCO guidelines varied, with early palliative care referrals ranging from 50% in the Americas region to 0% in the Western Pacific region. Key barriers included lack of policy support (25%), unmet educational needs (22%), and accessibility constraints (19%). CONCLUSION Addressing identified barriers through evidence-based advocacy, comprehensive policy changes, training, and continuing education programs is essential for integrating palliative care into oncology services across LMICs, promoting health equity for patients with cancer.

INTRODUCTION Atrial fibrillation (AFib) is a recognized complication in patients with chronic lymphocytic leukemia (CLL) treated with Bruton tyrosine kinase inhibitors (BTKis). Early detection and management of AFib are critical due to its association with increased morbidity and mortality. Traditional detection methods, such as intermittent EKGs and wearable devices, often capture only brief snapshots of cardiac activity, potentially missing episodes. Recent advances in artificial intelligence (AI) have enabled the development of algorithms that analyze electrocardiograms (EKGs) to predict AFib risk. These algorithms can potentially identify patients with undetected AFib and assess risk at a single point, reducing the need for continuous monitoring. This study evaluates the utility of an AI-enabled EKG algorithm in predicting AFib in patients with CLL treated with ibrutinib and acalabrutinib. METHODS Patients with CLL treated with ibrutinib and acalabrutinib were identified from a single-center database. Patients with CLL who received BTKi therapy at Mayo Clinic between 2015 and 2024 with available EKGs in the EMR were included. Patients were excluded if they received BTKi for non-CLL indications or if patients with CLL had documented AFib without accompanying EKGs in their medical records. An AI-enabled EKG algorithm developed by Attia et al. (Lancet, 2019) was used to estimate AFib probability (AFibAI). EKGs were categorized into pre-treatment, during treatment, and post-treatment phases. Mean values of all EKGs within each phase were used for analysis to reduce heterogeneity. Paired t-test was used for continuous variables and ROC analysis was used to evaluate diagnostic performance. A p-value of <0.05 was used for statistical analysis. RESULTS The analysis included 383 individual CLL patients (74% male, median age71; ace/ethnicity not available), of which 79 were treated with acalabrutinib and 304 with ibrutinib, with a total of 5844 EKGs analyzed. The AFibAI was significantly higher in EKGs with documented AFib compared to other EKGs, mostly normal sinus rhythm (0.5710 vs. 0.1857, p <0.001). Pre-treatment AFibAI was higher in patients who ultimately developed AFib after starting BTKi treatment compared to those who did not. Mean AFibAI at baseline was 0.2176 for those who developed AFib and 0.0986 for those who did not (p <0.001; difference between means: -0.1191 +/- 0.02005, 95% CI: 0.-0.1585 - 0.0796). Differences of baseline AFibAI were similar when stratified by BTKi. Trends in AFibAI scores indicated a significant increase during and after BTKi treatment in patients who developed AFib, with mean score of 0.2176 at baseline to 0.4064 post-treatment (p<0.001). In patients who did not develop Afib during BTKi therapy, there was minimal increase of AFibAI scores from 0.1041 at baseline to 0.1620 post-treatment (p = 0.0002). The ROC analysis showed an area under the curve of 0.7280 (SE = 0.03911, 95% CI: 0.6514 to 0.8047, p <0.001), indicating fair discriminatory ability. The sensitivity at the optimal threshold (0.1202) was 65.45% and the specificity was 74.59%, with a Youden's Index of 0.4004. To achieve a sensitivity of ≥90% for detecting patients who ultimately develop AFib, a threshold of 0.2368 was established (specificity of 41.82%). DISCUSSION Our study demonstrates the efficacy of an AI-enabled EKG algorithm in estimating atrial fibrillation risk among CLL patients treated with BTKis. We demonstrated that baseline AFibAI was notably higher in patients who ultimately developed AFib, suggesting that it could potentially identify patients at elevated risk before the onset of AFib. Early identification would allow for closer monitoring and timely intervention, potentially mitigating complications. Pending validation with an external cohort, the 90% sensitivity threshold could help identify the patients at highest risk, where other treatment options could be considered. The trend across different treatment phases revealed a significant increase in scores during treatment, indicating that AFibAI is responsive to changes induced by BTKi therapy and suggest its utility for patient monitoring. Practical questions remain about its impact on clinical decision-making and management. Future prospective studies should confirm the validity of identifying high-risk patients and its role in both treatment decisions and patient outcomes.