Eliza Chung

Objectives: BMI may capture clinically relevant differences in host reserve and treatment vulnerability in diffuse large B‐cell lymphoma (DLBCL). We evaluated the association between BMI and survival outcomes. Methods: Adults with DLBCL receiving first‐line R‐CHOP chemoimmunotherapy from 2016 to 2026 were identified from the Lymphoma and Related Diseases Registry. BMI was analysed as underweight, normal weight/overweight, obese Class I and obese Class II/III. Overall survival (OS) and progression‐free survival (PFS) were analysed using Kaplan–Meier and Cox models adjusted for International Prognostic Index (IPI) components, sex and cardiac history. Results: Among 1600 patients, 43 (2.7%) were underweight, 303 (18.9%) had obese Class I BMI and 198 (12.4%) had obese Class II/III BMI. At median follow‐up of 35.4 months, 342 deaths occurred, including 15 in underweight patients. OS differed by BMI category (log‐rank p = 0.017), whereas PFS did not (log‐rank p = 0.082). In complete‐case multivariable analysis, underweight BMI remained associated with inferior OS (adjusted HR, 1.89; 95% CI, 1.06–3.37). Exploratory analyses did not identify chemotherapy exposure, including upfront dose reduction, or measured malnutrition/systemic illness markers that explained this association. Conclusions: Underweight BMI identified patients at increased risk of inferior survival, even after adjustment for IPI‐based prognostic factors. Obese BMI categories were not associated with inferior survival.

Mantle cell lymphoma (MCL) is a rare subtype of non-Hodgkin lymphoma, comprising <5% of all lymphoma diagnoses [1]. Despite increasingly recognized biological and clinical heterogeneity, first-line...

Background: Achieving a complete response after therapy is an important predictor of long-term survival in lymphoma patients. However, previous predictive models have primarily focused on overall survival (OS) and progression-free survival (PFS), often overlooking treatment response. Predicting the likelihood of complete response before initiating therapy can provide more immediate and actionable insights. Thus, this study aims to develop and validate predictive models for treatment response to first-line therapy in patients with B-cell lymphomas. Methods: The study used 2763 patients from the Lymphoma and Related Diseases Registry (LaRDR). The data were randomly divided into training (n = 2221, 80%) and validation (n = 553, 20%) cohorts. Seven algorithms: logistic regression, K-nearest neighbor, support vector machine, random forest, Naïve Bayes, gradient boosting machine, and extreme gradient boosting were evaluated. Model performance was assessed using discrimination and classification metrics. Additionally, model calibration and clinical utility were evaluated using the Brier score and decision curve analysis, respectively. Results: All models demonstrated comparable performance in the validation cohort, with area under the curve (AUC) values ranging from 0.69 to 0.70. A nomogram incorporating the six variables, including stage, lactate dehydrogenase, performance status, BCL2 expression, anemia, and systemic immune-inflammation index, achieved an AUC of 0.70 (95% CI: 0.65–0.75), outperforming the international prognostic index (IPI: AUC = 0.65), revised IPI (AUC = 0.61), and NCCN-IPI (AUC = 0.63). Decision curve analysis confirmed the nomogram’s superior net benefit over IPI-based systems. Conclusions: While our nomogram demonstrated improved discriminative performance and clinical utility compared to IPI-based systems, further external validation is needed before clinical integration.

Background: Clinical registries are long-term observational data collections relating to specific medical conditions, procedures, devices or health services. Objective: To assess current characteristics and outputs of clinical registries in Australia. Method: A cross-sectional survey design of Australian clinical registries listed on the Australian Commission on Safety and Quality in Health Care (ACSQHC) register as of 21 September 2023. Results: Of 107 clinical registries on the ACSQHC register that were contacted, 64 (60%) participated in the survey. Of these, 37 (58%) had been active for ⩾10 years, 38 (59%) were medical clinical registries and 35 (57%) received government funding. Clinical registry activities included research (92%), quality improvement (81%) and epidemiological monitoring (68%). Data were commonly patient-identifiable (64%) and collected by clinicians/staff (81%). A majority (55%) had real-time data available to contributing hospitals. Clinical registry outputs included providing data to researchers (97%), publications (83%), annual reports (69%) and site benchmarked reports (64%). Over half informed quality improvement activities (60%), monitored adherence to guidelines (59%) or informed policy or service planning (52%). Nearly half-supported clinical trials (49%), while one-fifth had integrated with government data frameworks. Conclusion: Australian clinical registries monitor health system performance across a breadth of clinical areas. A majority undertake regular public and hospital reporting and inform other quality improvement activities. Implications for health information management practice: Clinical registries interact with hospitals regarding their data collection and reporting activities. Health information management specialists have an important role in maximising registry data quality and therefore value to a wide variety of stakeholders.

Tumor “bulk” has historically been considered an important prognostic marker and a clinical tool to guide treatment in patients with lymphoma. However, its use and definitions in trial designs vary significantly, and it is unclear how this has influenced the relevance of bulk in contemporary practice. This comprehensive literature review evaluated the definitions, applications, and prognostic impact of bulk in phase 3 randomized trials in 4 major lymphoma subtypes. Overall, 87 studies were identified across follicular lymphoma (FL), diffuse large B-cell lymphoma (DLBCL), peripheral T-cell lymphoma (PTCL), and Hodgkin lymphoma (HL) with a wide range of bulk thresholds used (5 cm, 6 cm, 7 cm, 7.5 cm, 10 cm, and >1/3 mediastinal mass ratio [MMR]). The most common threshold was as follows: FL, 7 cm (58%); DLBCL, 7.5 cm and 10 cm (44% each); PTCL, 7.5 cm (66%); and HL, one-third MMR (91%). Bulk threshold was used by trials to determine eligibility (66%), stratification (24%), as a prognostic risk factor (37%), and as a decision tool for risk-adapted treatment, for example, radiotherapy (29%); however, bulk definitions used for these varied both between, and within, lymphoma subtypes and even within single trials in 25%. Furthermore, 32 studies incorporated bulk in prognostic analyses with only 5 showing significance for differential survival outcomes. Our analysis demonstrates high inconsistency in thresholds defining tumor bulk and use of bulk in phase 3 lymphoma trials across eligibility, stratification, therapeutic risk adaptation, and prognostication. This highlights an urgent need for international consensus on definitions of bulk within trials to improve its prognostic and predictive values and refine its application in clinical practice.

Introduction: Lymphoma is a common malignancy among adolescents and young adults (AYAs) which is generally defined as 15–39 years. Relative to other age groups, lymphoma in AYAs remains understudied with heterogeneous treatment options.Methods: We performed a retrospective review of patients aged 18–60 years in the Australasian Lymphoma and Related Diseases Registry (LaRDR) with new diagnoses of the common subtypes of lymphoma in AYAs between January 2016 and April 2023. The subtypes are classic Hodgkin lymphoma (cHL), diffuse large B‐cell lymphoma (DLBCL), primary mediastinal B‐cell lymphoma (PMBCL) and Burkitt lymphoma (BL). Patient demographics, disease characteristics, treatment and outcome data were collected, and comparisons were made between AYAs (18–39 years) and older adults (OAs) (aged 40–60).Results: AYAs had higher rates of cHL and PMBCL whereas OAs presented more frequently with DLBCL. AYAs with cHL and PMBCL had higher rates of early‐stage and low‐risk disease than OAs. In contrast, both AYAs and OAs were more likely to present with advanced‐stage DLBCL and BL. AYAs with cHL were more likely to be treated with BEACOPP as compared to OAs who were more commonly treated with ABVD. There was no significant difference in treatment regimens for DLBCL, PMBCL or BL between AYAs and OAs. AYAs with cHL had better overall survival (OS) compared to OAs; specifically, cHL AYAs had better OS and DLBCL AYAs had better progression‐free survival (PFS) and OS compared to OAs.Conclusion: The study provides valuable data on patient and disease characteristics, treatments used and outcomes in AYA compared to OA aged 40–60 years. Registry data such as from LaRDR can help improve treatment standardisation and AYA patient outcomes.Trial Registration: The authors have confirmed clinical trial registration is not needed for this submission

Polatuzumab vedotin (Pola) is an approved therapy in combination with rituximab and bendamustine for relapsed or refractory diffuse large B-cell lymphoma (RR-DLBCL) based on positive results of the landmark phase II randomised G029365 trial. However, trial results for many approved novel therapies in RR-DLBCL have not been replicated in routine care cohorts, as RR-DLBCL patient populations are heterogeneous and trial eligibility is increasingly restrictive. We evaluated outcomes from pola +/- bendamustine and rituximab in patients with RR-DLBCL enrolled in a compassionate access program with no alternative treatment options identified via the Australasian Lymphoma and Related Diseases Registry according to their eligibility for the original phase II published study. Of 58 eligible patients, 74% met the criteria deeming them ineligible for the G029365 original study at the time of pola's commencement. Median progression-free survival and overall survival in our cohort were 2.3 and 3.5 months, respectively. In contrast to the landmark trial cohort, more of our patients ceased therapy prior to completion, the majority due to progressive disease and only 8/58 received any subsequent treatment. Dismal outcomes in this Australian real-world population demonstrate trial eligibility is challenging to meet, and newer treatments can be difficult to deliver in routine care. Clinically applicable results from therapeutic studies require trial cohorts to reflect representative clinical populations wherever possible, and more research is required to address the benefit of novel agents in the increasing majority who are ineligible for modern studies.