Citations

Abstract Introduction: The introduction of the Bruton tyrosine kinase inhibitor acalabrutinib combined with bendamustine and rituximab (ABR) as first-line (1L) therapy for mantle cell lymphoma (MCL) in older patients (pts) is reshaping the approach to treatment sequencing in this disease. The primary analysis of the phase 3 ECHO trial (NCT02972840) with data cutoff Feb 15, 2024 (median time on study 44.9 mo) demonstrated improved progression-free survival (PFS) with ABR vs placebo plus BR (PBR; hazard ratio [HR] 0.73; 95% confidence interval [CI] 0.57–0.94; P=0.0160) in pts ≥65 y with treatment-naive (TN) MCL. Here we present updated results after 50 mo of follow-up, including time to next treatment 2 (TTNT2; ie, initiation of third-line [3L] therapy). Methods: Pts aged ≥65 y with TN MCL and ECOG performance status ≤2 were randomly assigned 1:1 to receive ABR or PBR. Randomization was stratified according to geographic region and simplified MIPI score. BR was given for 6 cycles followed by rituximab maintenance for 2 y in pts achieving a partial or complete response (CR). Acalabrutinib (100 mg twice daily) or placebo was administered at study start until progressive disease (PD) or unacceptable toxicity. Crossover to acalabrutinib was permitted at PD. The primary endpoint was PFS per independent review committee. TTNT2, which was used as a surrogate for PFS2 (time from initial treatment to second disease progression), was defined as time from randomization to second subsequent (3L) antilymphoma therapy after discontinuation of randomized treatment or death. Results: In total, 299 pts were randomized to each arm; median age was 71 y. At data cutoff (February 15, 2025), median (range) time on study was 51.86 (0.03–93.04) mo and median (range) follow-up through reverse Kaplan-Meier method for PFS was 60.8 (0–88.5) mo. Median TTNT2 was not reached (NR) in the ABR arm and 73.8 mo in the PBR arm, establishing a 24% reduction in the risk of initiating 3L therapy or death with ABR vs PBR (stratified HR 0.76; 95% CI 0.59–0.98). In total, 33 (11.0%) and 100 (33.4%) pts received second-line [2L] treatment (median time to 2L therapy, 26.3 vs 19.6 mo) and 10 (3.3%) and 37 (12.4%) received 3L treatment in the ABR and PBR arms, respectively. When COVID-19 deaths were censored (in alignment with prespecified analyses from the primary study), the HR further improved (stratified HR 0.67; 95% CI 0.50–0.89; median TTNT2 NR in any arm). In total, 71.6% and 78.3% of pts discontinued acalabrutinib and placebo, respectively; the most common reasons (>5%) for discontinuation were adverse events (AEs; 44.5%, ABR; 32.4%, PBR) and objective PD (12.7%, ABR; 30.8%, PBR). Median PFS was 72.5 and 47.8 mo in the ABR and PBR arms, respectively (stratified HR 0.68; 95% CI 0.53–0.87; P=0.002). In total, 108 (36.1%) and 116 (38.8%) pts in each arm died; median overall survival (OS) was NR in both arms (stratified HR 0.87; 95% CI 0.67–1.13), with a 36-mo OS rate of 73.8% with ABR vs 68.3% with PBR. With 1 additional year of follow-up since the primary analysis, rates of grade ≥3 AEs and serious AEs in the ABR arm were comparable to previously reported rates, indicating that prolonged acalabrutinib exposure does not appear to result in cumulative toxicities. Three new cases of grade 3/4 neutropenia were reported, with no marked changes in the rates of grade ≥3 or serious infections. The rate of grade ≥3 cardiac events remained consistent with the primary analysis. In the ABR arm, 1 new case of grade 3/4 atrial fibrillation and 1 new case of grade 3/4 hypertension were reported. Rates of thrombocytopenia, major hemorrhage, and ventricular tachyarrhythmia remain unchanged. Conclusion: In this updated analysis of the ECHO trial, despite crossover to acalabrutinib being permitted in the PBR arm, fewer pts in the ABR arm were in need of subsequent treatment (2L and 3L), with longer TTNT2 in the ABR arm. These results suggest that 1L treatment with ABR provides a 24% reduction in risk of initiating 3L therapies or death. Further follow-up has demonstrated that ABR offers improved efficacy (ie, PFS) vs PBR, with no long-term safety concerns with acalabrutinib maintenance. The updated safety findings further support the favorable benefit–risk profile of acalabrutinib in TN MCL. Altogether, these data support the potential of acalabrutinib in combination with BR as 1L treatment, rather than in later lines, to modify long-term treatment outcomes in pts with TN MCL.

Abstract Background Chimeric-antigen-receptor (CAR) T-cell therapy has transformed outcomes for relapsed or refractory hematologic malignancies, yet early identification of patients at highest risk for death remains elusive. A prognostic model that leverages only the information routinely available at the time of CAR-T evaluationcould guide pre-infusion counseling, intensity of follow-up, and resource allocation. Methods We analyzed a prospectively maintained cohort of CAR-T recipients treated at Mayo Clinic to develop and test our model. For each patient, data (vital signs, lab results, cardiac assessments [ECG and echocardiography], and PET/CT images) from initial CAR-T evaluation (up to 60 days prior to CAR-T infusion) was considered. Vital signs and labs were left un-imputed, while ECG, echocardiographic parameters, and PET/CT features were forward-filled from the preceding 60 days. Each patient-day was labeled as “outcome present” or “no outcome,” enabling supervised training of gradient-boosting models to predict overall survival (OS) at 6 months post CAR-T infusion. Model performance was evaluated using the area under the ROC curve (AUC) with 10,000-fold bootstrapping with data shuffling between each iteration to derive confidence intervals for stability. To enhance interpretability, we also developed a simplified model using the top 19 features (determined by Shapley value-based importance), aiming to preserve predictive accuracy with a smaller feature set. Results A total of 570 CAR-T cell treated patients were included in this study (476 patients in the training cohort and 94 patients in the validation cohort). The median age was 64 years and 61% males. The primary diagnoses were B-cell lymphoma (324 patients) and multiple myeloma (246 patients). A total of 4,851 individual vital-sign readings, 11,781 laboratory measurements were included. PET/CT images were available in 318 patients. The ML model demonstrated high discriminative ability for predicting OS at 6 months post CAR-T infusion. Vital signs alone produced a mean AUC of 0.63; radiomics features alone produced a mean AUC of 0.71, while laboratory tests alone produced a mean AUC of 0.84. The features most contributory to each single-modality model were then combined into a simplified multimodal model with a total of 19 features, which produced a mean AUC of 0.87, with a 95% CI of 0.73 - 0.97. The 19 features included in the simplified model were: LDH, ferritin, uric acid, eosinophils, fibrinogen, D-dimer, heart rate, mean arterial blood pressure, QTc, QT interval, and the radiomics features of sphericity, first-order Kurtosis, first-order mean, first-order energy, first-order total energy, shape maximum2D diameter (sagittal plane), SUV CoV, SUVp90, shape flatness. This multimodal predictive model was then applied to the validation cohort (uninvolved with model training or testing), which produced an AUC of 0.93, with a 95% CI of 0.84 - 0.98. Conclusions Our ML-driven approach successfully predicts overall survival 6 months post CAR-T infusion, highlighting those individuals at most risk of poor outcomes at the time of CAR-T evaluation. The strong performance of the multimodal version of the model suggests that each data domain contributes complementary resolving power, and by including novel data types, such as PET CT radiomics features, the most accurate predictions could be realized. By providing accurate, near-real-time risk stratification for poor outcomes within 6 months of CAR-T infusion, the model could facilitate proactive patient management. Prospective studies of the integration of this ML model into CAR-T treatment workflows will be crucial to confirm its clinical benefits and to facilitate its adoption in practice.

Abstract Background: The landscape of healthcare delivery in hematology has undergone significant transformations with the increasing adoption of telehealth services for appointments and communications in conjunction with the 2016 21st Century Cures Act providing patients complete access to their health information. Digital healthcare services in hematology offer benefits, including convenience and equitable access to those limited by travel or financial burdens. However, they also have inherent risks, including loss of in-person communication, disparities in access to technology, differences in digital literacy, and increased provider workload and burnout. Thus, we analyzed contemporary utilization patterns of hematology healthcare and provider preferences to optimize implementation of evolving hybrid care models. Methods: We analyzed data from the Mayo Clinic (Rochester, MN) Hematology Appointments Office and the Hematology Call Center as well as digital messaging practices using Epic's SlicerDicer tool. Data was collected between January 2023 and January 2025, during which patients had access to the electronic health record (EHR) and direct messaging capabilities. An optional survey was distributed to Hematology providers including physician consultants, fellows, and advanced practice providers (APP) to assess practice preferences. Results: During the analyzed period, a total of 85,899 hematology appointments were completed and captured all adult ages, including 18-34 (4.6%), 35-49 (9.2%), 50-64 (25.3%), 65-79 (48.3%), and 80+ (12.6%). Although in-person, video, and telephone visit options were available, in-person visits predominated across all age groups, with 80.6% of all completed appointments occurring in-person, 12.9% via video, and 6.5% via telephone. Notably, patients aged 80+ had the second highest combined use of video and telephone visits, with over 20% of their appointments conducted virtually. There was a total of 6,893 no-show and cancelled appointments, with 79.9% of these scheduled in-person, 14.8% via video, and 5.3% via telephone. There were over 4,000,000 portal messages in the hematology division with over 1,000,000 sent directly to hematology providers. Patients sent 105,284 new messages, of which 47.2% were new patient advice requests to providers which cascaded to 343,103 additional messages. Patients made 155,036 telephone calls, which cascaded to 407,578 messages over the portal, 78.5% of which were sent directly to providers. Notably, utilization of both portal messaging and telephone calls was similar across all age cohorts, including those 80+ years. Providers only initiated 14,159 new messages to patients yet sent a total of 354,172 messages. The hematology provider survey had a response rate of 42% (53 of 129). Respondents included 45.3% consultants, 45.3% fellows, and 9.4% APPs. 92.5% and 90.6% of providers preferred in-person appointments for new visits and established visits on therapy, respectively. In contrast, 52.8% preferred virtual appointments for established patients not currently receiving therapy. 73.6% and 52.8% of survey respondents either strongly agreed or agreed that virtual appointments are advantageous for patients and providers, respectively. Additionally, only 49.1% felt portal messages were necessary to provide high quality care and 64.2% preferred appointments over communicating through messages. 86.8% of providers reported spending more than 30 minutes daily responding to in-basket messages, however, 94.3% reported not routinely billing for their time. Conclusions: In the digital era, hematology healthcare delivery is multifaceted and increasingly demanding with high rates of patient engagement in their care via various mediums. To our knowledge, this is the largest analysis combining EHR utilization and appointment data with provider preferences to better understand digital hematology care. Digital appointments and communications were common amongst all age groups, including older patients, and had relatively low cancellations rates. Promotion of virtual visits to facilitate patient-provider conversations in addition to standardization of digital communication workflows are low-risk, high-yield interventions to mitigate the cascading of EHR-based communications, especially for those not actively receiving therapy. Together, these could decrease provider burnout while increasing healthcare system operational capacities to provide optimal patient care and satisfaction.

Abstract Background: De novo secondary central nervous system (CNS) lymphoma (SCNSL), characterized by concurrent systemic and CNS aggressive B-cell lymphoma at diagnosis, is a rare entity with a dismal prognosis. The prognostic significance of the initial anatomical compartment of CNS involvement (parenchymal vs. leptomeningeal) is not well-defined. We sought to analyze outcomes based on CNS localization in a single-institution cohort. Methods We retrospectively reviewed 34 patients with de novo SCNSL (including diffuse large B-cell lymphoma [DLBCL], high-grade B-cell lymphoma, not otherwise specified (HGBCL NOS), and double-/triple-hit lymphoma) treated at the Mayo Clinic between 2002 and 2025. Clinicopathological features, radiologic findings, treatment details, and survival outcomes were abstracted from the medical records. Patients were categorized by the primary site of CNS involvement. Overall survival (OS) and progression-free survival (PFS) were estimated using the Kaplan-Meier method, and groups were compared with the log-rank test. Results The study cohort had a median age of 61.5 years (range 19-79) and a median International Prognostic Index score of 3 (range 2-4). Pathological subtypes included double-/triple-hit lymphoma (n=14, 41.2%), non-germinal center B-cell like (non-GCB) DLBCL (n=11, 32.4%), germinal center B-cell like (GCB) DLBCL (n=8, 23.5%), and HGBCL NOS (n=1, 2.9%). Among patients with available data, 14 (43.8%) were positive for MYC rearrangement, 13 (54.2%) for BCL2 rearrangement, and 10 (45.5%) for BCL6 rearrangement. First-line regimens received by patients included MR-CHOP (n=18, 52.9%), DA-R-EPOCH (n=5, 14.7%), combined CODOX-M/IVAC (n=4, 11.7%), R-CHOP with intrathecal methotrexate (n=3, 8.8%), miscellaneous therapies (n=3, 8.8%), and MRT (n=1, 2.9%). The complete response rate was 58.8% (n=20). Intrathecal chemotherapy was administered to 61.8% of patients, and consolidation with autologous stem cell transplant was performed in 35.3%. Based on the CNS compartment involved, de novo SCNSL (DN-SCNSL) were classified into two subtypes: the leptomeningeal subtype, defined as leptomeningeal involvement with or without parenchymal CNS disease (LM-DN-SCNSL; n=22, 64.7%), and the parenchymal subtype, defined as parenchymal-only CNS involvement (P-DN-SCNSL; n=12, 35.3%). Parenchymal disease was multifocal in 75% of cases, supratentorial in 58.3%, and associated with a midline shift in 30%. Although no tested factors (including pathological subtype, age, sex, and bone marrow involvement) were significantly associated with the anatomical compartment, a non-significant trend was noted for BCL6 rearrangement to be more common in the leptomeningeal group (8/13, 61.5%) than the parenchymal group (2/8, 25.0%) (P = 0.18). Survival outcomes were analyzed for both anatomical and pathological classifications. For the parenchymal versus leptomeningeal cohorts, 2- and 5-year Overall Survival (OS) rates were 75.0% vs. 45.1% and 41.0% vs. 38.7%, respectively (P = 0.34). For Progression-Free Survival (PFS), the 2- and 5-year rates were 48.6% vs. 32.5% and 29.2% vs. 26.0%, respectively (P = 0.53). The 2-year OS rates by pathological subtype were 81.8% for non-GCB DLBCL, 45.8% for DHL/THL, and 37.5% for GCB DLBCL (P = 0.27). Survival outcomes (OS/PFS) were not significantly associated with patient age, sex, pathological subtype, or anatomical compartment. Conclusion DN-SCNSL can be anatomically classified into two subtypes: LM-DN-SCNSL and P-DN-SCNSL. While a prognostic trend favoring the parenchymal subtype was observed, the difference did not achieve statistical significance, a finding likely attributable to the small cohort size. This classification may hold clinical utility for guiding risk stratification, and larger studies are needed to validate these findings and identify the most optimal treatments for each subtype of DN-SCNSL.

Abstract Background Secondary central nervous system lymphoma (SCNSL) is a rare but significant complication, affecting approximately 2–10% of patients with diffuse large B-cell lymphoma (DLBCL), often leading to poor outcomes. The CNS-International Prognostic Index (CNS-IPI) remains a widely adopted tool for estimating CNS relapse risk; however, recent advances in molecular profiling and evolving treatment paradigms have challenged the adequacy of CNS-IPI alone in guiding risk assessment. This study evaluates the prognostic relevance of CNS-IPI in conjunction with molecular and clinical factors, specifically cell-of-origin (COO), gene expression, and frontline treatment regimens. The aim is to refine risk stratification and identify patients at highest risk for SCNSL. Method Building on our previous study, we analyzed data from patients with newly diagnosed DLBCL between 2002 and 2023, enrolled in the Mayo Clinic/University of Iowa Lymphoma Molecular Epidemiology Resource. Patients with primary CNS lymphoma, CNS involvement at diagnosis, or who received CNS-directed therapy were excluded. COO was determined using the Hans algorithm and/or Nanostring profiling. Multivariate cause-specific Cox regression models were used to estimate hazard ratios (HR) and 95% confidence intervals (CIs) for predictors of CNS relapse that were measured at the time of initial DLBCL diagnosis. Cumulative incidence with competing risks and cause-specific Cox models were used to analyze relapse risk. Result We included 2137 patients in our analysis, median age at diagnosis was 63 (IQR 53-72), with 41.6% (n=890) females. The albumin level was below normal in 32.5% (n=350), 64.0% (n=1367) had CNS-IPI of intermediate/high, 37.5% (n=586) had non-germinal center B-cell (GCB) COO, and 14.5% (n=309) were double expressor positive. At a median follow-up of 83 months (IQR 42 -143), 82 (3.8%) SCNSL relapses were observed; the 3 and 5 yrs cumulative incidence for SCNSL from time of DLBCL diagnosis were 3.7% (95% CI 3.0-4.7) and 3.8% (95% CI 3.1-4.8) respectively. In models accounting for death as a competing risk, the 5-year cumulative SCNSL incidence estimate for patients with a CNS-IPI low was 1.5% (95% CI 0.8-2.7), Intermediate was 4.9% (95% CI 3.7-6.4), and high was 6.1% (95% CI 3.8-9.6). The 5-year cumulative SCNSL incidence estimate for patients with non-GCB COO was 5.2% (95% CI 3.6-7.4) compared to 2.4% (95% CI 1.6-3.6; p=0.001) for GCB patients, and the 5-year cumulative SCNSL incidence for double expressor positive patients was 8.6% (95% 5.8-12.6) compared to 1.5% (95% CI 0.8-2.7; p<0.001) for double expressor negative patients. In a multivariable model (including CNS-IPI, COO, double expressor, treatment group, extranodal involvement, and albumin group), CNS-IPI intermediate (HR 2.85 [1.58-5.14]; p<0.001) and high (HR 4.31 [2.15-8.65]; p<0.001), non-GCB COO (HR 2.36 [1.37-4.06]; p=0.002], double expressor (HR 5.56 [2.88-10.73]; p<0.001), and below normal albumin (HR 1.89 [1.02-3.50]; p=0.042) were independently associated with increased risk of SCNSL. The majority of the SCNSL relapse occurred within the first 2 years after DLBCL diagnosis; 81.7% (n=67), with only 6.1% (n=5) occurring after 5 years. For patients who developed SCNSL, the median OS from the time of SCNSL diagnosis was 24 months (95% CI 17-34), with 5 years OS of 31% (95% CI 22-43). These findings are being validated using an external, international cohort, and final analysis will be presented at the time of presentation at ASH. Conclusion In this large, prospectively followed cohort with long-term follow-up data, SCNSL remains a rare but devastating complication of DLBCL, with a 5-year cumulative incidence of 3.8% and it is associated with very poor long-term survival. While CNS-IPI remains a strong predictor of SCNSL risk, our study identifies non-GCB COO and double expressor phenotype as independent, additive molecular risk factors. These findings suggest that current risk stratification tools may underestimate the risk of CNS relapse in molecularly defined high-risk subgroups. Considering the poor outcomes linked to SCNSL and the absence of a connection with frontline treatment types, future research should aim to enhance risk prediction models through molecular profiling and explore prophylactic strategies to reduce the likelihood of SCNSL.

Abstract Background: Immune checkpoint inhibitors (ICIs) have been established as standard of care for patients with classical Hodgkin lymphoma (cHL), including relapsed or refractory (r/r) disease. However, ICIs discontinued in some patients due to treatment-related toxicities or disease progression may later be re-initiated. The outcomes of this common clinical practice of ICI re-initiation, or ‘rechallenge,‘ have not been evaluated. Here, we present real-world efficacy and safety data of ICI rechallenge in patients with cHL. Methods: We conducted a retrospective cohort study of patients with cHL treated with ICIs between 2015 and 2024 at Mayo Clinic sites. Patients were identified via institutional records using diagnostic and treatment codes. Included patients were adults ≥18 years with histologically confirmed diagnosis of cHL receiving ≥1 cycle of ICI therapy. Patients in the rechallenge cohort received a second course of ICI therapy following discontinuation of the initial ICI for any reason. The control cohort included patients with ICI discontinuation without subsequent rechallenge. Primary outcomes included progression-free survival (PFS), event-free survival (EFS), and duration of response (DOR), defined respectively as time from first ICI initiation to disease progression, time from first ICI initiation to disease progression, adverse events, treatment change, or death, and time from first recorded response per positron emission tomography–computed tomography until the occurrence of disease progression. Secondary outcomes included overall survival (OS), and incidence and severity of irAEs following ICI rechallenge. Survival outcomes were compared using propensity-score-matched (PSM) cohorts with a proportional Cox model. Results: A total of 145 patients were included. Of these, 30 (21%) underwent ICI rechallenge, while 115 (79%) received no subsequent ICI treatments. The median age was 40 years (interquartile range [IQR] 26-66). Most were male (59%), predominantly nodular sclerosis subtype (71%), Ann Arbor stage III-IV disease (65%), and ECOG ≤ 1 (96%). ICIs were utilized for r/r disease in 66%, with median 6 (IQR 4-13) cycles and follow up of 40.8 months (IQR 21.1–78.1). Patients received median 1 prior line of therapy (IQR 0-2.5). During initial ICI treatment, 34/145 patients (23%) experienced irAEs, including 17 patients (57%) in the rechallenge group and 17 (15%) in the non-rechallenge group. Most irAEs were of grade 1–2 severity, though 10 (30%) experienced grades ≥3 irAEs. irAE severity did not differ between groups. Among patients who underwent ICI rechallenge, the majority (53%) discontinued due to irAEs. The decision to pursue ICI rechallenge was driven predominantly by disease progression (63%). Among the 1:1 PSM cohorts using bone marrow transplant status and the International Prognostic Score, PFS did not significantly differ between rechallenged vs. non-rechallenged groups (HR 1.7, 95% CI 0.60–4.5, p=0.32). Similarly, no difference was noted among the groups in EFS and DOR using 1:3 PSM cohorts using the number of ICI cycles (HR 1.6, 95% CI 0.83–3.0, p = 0.16 and HR 1.6, 95% CI 0.37–7.0, p = 0.53, respectively). In the 1:1 matched cohort, ICI rechallenge was not associated with inferior OS compared to those without rechallenge (HR 1.6, 95% CI 0.48–5.4, p=0.43). irAE occurred in 20%, including pneumonitis (n=2), hepatitis (n=1), and pancreatitis (n = 1). Of the patients who had experienced irAEs during their initial ICI course, only two developed recurrent irAEs of the same type upon rechallenge, both at reduced severity. Overall, 80% of rechallenged patients did not experience any irAE with their second ICI course. Conclusions: ICI rechallenge was not associated with inferior PFS, EFS, DOR or OS compared patients without rechallenge. Among patients receiving ICI rechallenge after irAE, recurrence of same irAE was rare. ICI rechallenge may be a feasible and potentially effective strategy in selected patients with cHL.

Abstract Introduction: Brentuximab vedotin (BV) was approved by the US FDA for relapsed/refractory (R/R) classical Hodgkin lymphoma (cHL) in 2011 and for previously untreated advanced stage cHL in 2018. The immune checkpoint inhibitors (ICIs) nivolumab and pembrolizumab were approved for R/R cHL in 2016 and 2017, respectively. These novel agents have significantly changed the landscape of cHL management in recent years. In this study, we analyzed data from the National Cancer Database (NCDB) to investigate whether survival outcomes have improved in the novel agent era (2011 and beyond) for patients with cHL in the US. Methods: Patients diagnosed with cHL between 2004 and 2020 were identified in the NCDB. Those with missing survival data were excluded. Three treatment eras were defined based on the timing of FDA approval of novel agents: Chemo era (2004–2010), BV era (2011–2015), and ICI era (2016–2020). Patient and disease characteristics were compared using the Chi-square test. Survival analyses were conducted using the Kaplan-Meier method and Cox proportional hazards model. Results: A total of 85,488 patients with newly diagnosed cHL were identified: 36,150 in the Chemo era, 27,058 in the BV era, and 22,280 in the ICI era. Compared to the chemo era, the BV and/or ICI eras included slightly more patients aged ≥66 (17.3% vs 17.8% vs 19.5%), male (53.6% vs 54.6% vs 53.8%, p=0.042), Black (11.3% vs 12.9% vs 12.5%), Hispanic (9.0% vs 9.9% vs 10.9%), with Charlson-Deyo score ≥1 (14.2% vs 16.1% vs 17.2%), B symptoms (44.1% vs 46.9% vs 46.9%), treated at academic centers (32.4% vs 35.9% vs 36.4%), on Medicaid (9.5% vs 12.2% vs 13.7%) or Medicare (19.3% vs 20.3% vs 21.7%), and with higher income (≥4th quartile by 2008-2012 standard; 34.5% vs 34.3% vs 36.3%) (all p<0.001 except for sex, noted above). Ann Arbor stage data were not available in the ICI era. There were slightly more patients with advanced-stage disease in the BV era compared to the Chemo era (45.4% vs 40.3%, p<0.001). IPS scores were available for only 2,344 patients, with a similar distribution (0–2 vs 3–7) across the three eras (p=0.957). The median follow-up was 6.9 years (95% CI 6.9-7.0). Overall survival (OS) significantly improved in the novel agent eras: the 5-year OS rate was 79.8% (95% CI, 79.4%–81.2%) for patients diagnosed in the Chemo era, 81.9% (95% CI, 81.9%–82.4%) in the BV era, and 84.1% (95% CI, 83.5%–84.8%) in the ICI era (p<0.001). Statistically significant OS improvements were observed across all subgroups, including age (≤40: 92.8% vs 94.9% vs 96.5%, 41-65: 80.5% vs 81.8% vs 84.4%, ≥66: 41.4% vs 46.6% vs 52.7%), sex (male: 78.0% vs 80.0% vs 81.9%, female: 81.9% vs 84.1% vs 86.7%), race (White: 79.9% vs 81.6% vs 83.7%, Black: 77.2% vs 81.2% vs 84.6%, others: 84.5% vs 87.0% vs 89.3%), ethnicity (Hispanic 79.7% vs 82.3% vs 85.1%, Non-Hispanic 80.0% vs 81.9% vs 84.1%), Charlson-Deyo score (0: 83.5% vs 85.4% vs 87.9%, 1: 62.8% vs 68.2% vs 73.2%, 2: 42.5% vs 52.6% vs 58.3%, 3+: 49.0% vs 54.4% vs 51.7%), B symptoms (yes: 74.7% vs 78.9% vs 81.5%, no: 84.3% vs 85.1% vs 87.5%), treatment facility type (academic: 69.8% vs 74.0% vs 77.1%, non-academic: 66.4% vs 67.6% vs 70.2%, unknown: 92.9% vs 95.0% vs 96.5%), insurance type (private: 89.9% vs 91.2% vs 93.4%, Medicare: 46.5% vs 52.3% vs 57.2%, Medicaid: 79.1% vs 84.5% vs 87.7%) and income level (Q1: 74.1% vs 77.1% vs 81.1%, Q2 77.7% vs 80.1% vs 81.3%, Q3 79.8% vs 81.6% vs 85.2%, Q4: 82.8% vs 84.9% vs 87.0%) (all p<0.001). In multivariate Cox regression analysis adjusting for age, sex, race, ethnicity, Charlson-Deyo score, B symptoms, treatment facility type, insurance type, and income level, diagnosis in the BV era (HR = 0.856; 95% CI, 0.827–0.885; p<0.001) and ICI era (HR = 0.666; 95% CI, 0.636–0.698; p<0.001) was independently associated with improved OS compared to the Chemo era. Conclusions: In this large NCDB-based analysis, survival outcomes for patients with cHL significantly improved in the BV and ICI eras compared to the Chemo era. OS improvement was consistent across all subgroups and remained independent of age, sex, race, ethnicity, Charlson-Deyo score, B symptoms, treatment facility type, insurance type, and income level in multivariate analysis. These findings may reflect the positive impact of BV and ICIs on survival in patients with cHL.

Abstract Description Cellular immune responses are critical for protection from environmental and genetic insults, including pathogens and cancer. However, immunity can also drive pathogenic responses that damage organs and drive pathogenesis. Immune response quality is influenced by several factors including genetics, age, sex and the environment. Environmental influences including everyday activities and lifestyle have been shown to modulate the immune system but are often difficult to monitor. We have developed a system of serial monitoring of peripheral blood mononuclear cells that coincided with wearable device activity monitoring to further understand how biometric data correlates with the composition of blood derived immune cells. Using a 31-marker CyTOF phenotyping panel we monitored blood derived immune cell composition over a 14-week period in an individual volunteer while biometric data was collected via wearable technology. To determine whether interventions can be introduced that change peripheral blood immune cell phenotypes our volunteer introduced lifestyle changes at specific intervals in this period of monitoring. These included intermittent fasting and the introduction of regular exercise. In addition, the individual was immunized against COVID-19 during this time to provide an immune challenge that would perturb immune homeostasis. In this study, we find that lifestyle changes can promote alterations in immune cell phenotypes identified using high parameter mass cytometry. Topic Categories Translational and Interventional Immunology (TI)

592 Background: While not all fevers occurring during hematopoietic stem cell transplantation (HCT) are dangerous, episodes of febrile neutropenia tend to be managed aggressively given the significant mortality and morbidity associated with infectious complications of transplantation. This can potentially lead to unnecessary diagnostic testing, antibiotic treatment, and prolonged hospital stays for non-infectious fever etiologies such as engraftment. Our group has previously demonstrated that high-frequency body temperature monitoring (HFTM) using a wearable device detects fevers earlier in transplant recipients (Flora et al, Cancer Cell, 2021). We hypothesized that applying supervised machine learning to HFTM data collected from inpatient allogeneic HCT patients would allow for accurately distinguishing between non-infectious and infectious etiologies of fever and could potentially guide safe de-escalation of antibiotics. Methods: We analyzed 74 independent fever events detected in HCT patients (n=42) outfitted with an FDA-cleared, wireless sensor measuring axillary temperatures every 2 minutes for the duration of their respective hospitalizations. After data filtering, independent fever events were then retrospectively grouped by fever etiology using physician-determined annotation as previously described (Flora et al., 2021). A split ratio of 80:20 was used to create training and test sets from the parent data set, consisting of 59 and 15 fever events for each split, respectively. Training sets were used to create a classification algorithm identifying bacterial infections requiring antibiotics using the machine learning model XGBoost. The performance of this model was then evaluated using test sets of the remaining fevers (n=15). Results: Averaging over 1000 test sets of 15 independent fevers comprising 5 bacterial infection-related fevers and 10 non-infectious fevers, our model correctly identified 82% (mean recall) of cases requiring antibiotics with a mean precision of 0.41. The model also correctly identified 42% of non-infectious fevers (mean true negative rate, 0.42) that would otherwise have been treated with unnecessary antibiotics. The mean AUCPR was 0.58, with the best performing individual model achieving an AUCPR of 0.97. Conclusions: The preliminary results of this study demonstrate that our classification model shows promise for differentiating fevers requiring antibiotics due to bacterial infections from other non-infectious fever etiologies. Prospective studies further developing supervised machine learning models for subcategorization of febrile neutropenia could pave the way for safely de-escalating antibiotics in HCT and other immunocompromised patients undergoing cancer treatment monitored using HFTM.