Holmes, Lucy

1633 Background: CAR-T cell therapy has transformed outcomes of hematologic malignancies and is poised to expand into solid malignancies. However, broader use of CAR-T remains constrained by the need for timely cytokine release syndrome (CRS) detection. Current episodic monitoring may miss early physiologic changes. Wearable digital health technologies (DHTs), offering continuous monitoring and real-time alerts, could enable earlier CRS detection, further support outpatient delivery, and expand access to CAR-T and potentially other immunotherapies associated with CRS. Methods: This single-center observational study (NCT05018208) enrolled patients receiving FDA-approved CAR-T products in the outpatient setting at Mayo Clinic, Rochester MN. Management of post-CAR-T complications (e.g. CRS) followed the institution’s standard of care. Patients were monitored from day -5 to day +30 post infusion with intermittent remote patient monitoring (SoC) and an arm-worn wearable device for continuous monitoring (DHT). Patients were included in this analysis if DHT data were available for ≥ 5h of data/day or ≥ 2h of data/night on ≥2 days or nights for baseline and ≥1 day or night within 72h prior to CRS, or at least 2h within 4h of CRS onset, or if DHT was available for ≥7 days/nights post infusion with no CRS event. CRS detection time by DHT was defined as the onset of pyrexia or threshold-based deviation from an individual’s baseline pulse distributions. Results: 32 patients were included in this report (median age 64 years, gender 12 F/20 M, diagnoses 18 MM/14 NHL). 25 patients experienced a CRS event, with maximum CRS grades ranging from 1-3 (16 G1, 8 G2, 1 G3). 24/25 patients with CRS required tocilizumab/steroid. Median CRS onset time was 3.4 days (range 0.4-18.4). 12/32 patients had sufficient DHT data (7 CRS, 5 no CRS) for analysis. 5 patients with CRS had a DHT detection time in advance of the SoC by a median of 13h (range 2.8 - 69.2). The DHT prediction of CRS by baseline HR change was earlier than the first episodic fever by a median of 14.5h (range 13-77.5). The remaining 2/7 patients who did not have CRS detected by the DHT had missing DHT data for more than 45h before a treated CRS event. DHT detected CRS in 2/5 patients without CRS according to SoC. One patient had fever caught with intermittent monitoring. The other was treated for neutropenia fever with suspicion of CRS and/or infection. Conclusions: These results offer evidence that continuous DHT can identify CRS events earlier than intermittent monitoring (SoC). Despite limitations related to compliance with wearable device use and data missingness, and variability in clinical management of CRS, we highlight the usability of DHT and its potential for future studies with optimized strategies to address these limitations. Clinical trial information: NCT05018208 .

Abstract Large Language Models (LLM) are being widely adopted into the medical field for their impressive ability to analyze and summarize large amounts of text data. These models enable clinicians and researchers to extract meaningful insights from complex datasets and may assist with decision making. Here we present our workflows and application of LLM for the interpretation and summarization of clinical data related to CAR-T cell therapy. Using an LLM (Gemini 2.5 pro) in the Google Cloud Computing (GCP) environment, two applications were developed to analyze CAR-T cell therapy clinical data: 1) extracting and summarizing CRS and ICANS event-related data to streamline the compliance team workflow, and 2) identifying features available at time of CAR-T infusion able to classify patients into high- or low-monitoring needs 14 days post CAR-T. Patient data (vitals, labs, hematology notes, and EKGs) were extracted from the electronic medical record (EMR) using Google BigQuery into SQL tables in GCP. For each application, relevant data fields were retrieved, formatted into JSON objects, and embedded in the LLM prompt for context-aware processing. Both applications have undergone iterative prompt engineering after analyzing the LLM output against ground truth data in the EMR. For the application extracting and summarizing CRS and ICANS events, the LLM was optimized on Mayo Clinic Rochester data. When compared to the IEC compliance database, the LLM achieved 100% accuracy and F1 score for CRS events, and 96% accuracy and 82% F1 score for ICANS events. The match rate for CRS and ICANS grades were 83% and 89% respectively. We applied the same LLM to Mayo Clinic Arizona (MCA) and Mayo Clinic Florida (MCF) cases, who document clinical notes and toxicity flowsheet differently, and achieved an accuracy (MCA: 93%, MCF:93%) and F1 score (MCA: 96%, MCF: 96%) for CRS and accuracy (MCA: 81%, MCF:82%) and F1 score (MCA: 76%, MCF: 75%) for ICANS. LLM was able to capture events missed by manual reviews. For most of the discrepancies where compliance team final adjudication is needed, LLM will be updated to flag discrepancies for review by the compliance team. For the application related to the monitoring needs 14 days post CAR-T, the LLM identified 5 categories predictive of high or low monitoring needs post CAR-T infusion (disease status, inflammatory and tumor burden markers, hematologic status, renal function and performance status). Our model achieved a sensitivity of 85.7%, specificity of 23.8%, and F1 score of 65.5% for our first cohort, compared with data extracted by the LLM from the EMR. For our second cohort, with demographics statistically similar to cohort 1 and using the same 5 categories, the LLM achieved a sensitivity of 83.3%, a specificity of 28.6%, and F1 score of 65.4%. Our workflow and applications of LLM’s provide examples and guidance to others interested in applying LLM’s for clinical and research applications. Citation Format: Emmanuel Contreras Guzman, Matthew Jankowski, Andre De Menezes Silva Corraes, Malvika Gupta, Monica L. Shaw, Madiha Iqbal, Talal Hilal, Saurabh Chhabra, Ricardo Daniel Parrondo, Jody K. Mclean, Kim R. Riester, Kayla Joseph, Melinda Tan, Holly Ross, Cleyonia Barnett, Sylvia Carter, Semy Girmay, Rachel Wolan, Milana Ramsey, Christian Downhour, Kristy Morgan, Shae Sibley, Erica Rushing, Lucy Holmes, Allison Burgstahler, Stephen M. Ansell, Hassan Alkhateeb, Matthew Hathcock, Ramona Bruno, Allison C. Rosenthal, Hemant Murthy, Patrick B. Johnston, Jonas Paludo, Yi Lin. Applications of large language models to CAR-T cell therapy clinical data using Google cloud computing [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 2740.

Abstract Background: Tarlatamab, a Delta-like ligand (DLL3)/CD3-targeted bispecific T-cell engager (TCE) is FDA approved in patients (pts) with extensive stage small cell lung cancer (ES-SCLC), after progression on frontline chemoimmunotherapy. We aimed to evaluate the immune cell profile in pts who received this therapy in standard-of-care (SOC) practice with progression free survival (PFS) less than and greater than two months (mo). Methods: Patients who received tarlatamab at Mayo Clinic Rochester and consented to immuno phenotyping of blood are included in the present study. Immune phenotyping was performed on whole blood by flow cytometry and analyzed by Kaluza. Data analysis was performed with Microsoft Excel and PRISM. Results: Eighteen patients with median age 64 (range 37-79) were included in the study. 66% of our cohort were women and 83% had present or past history of smoking, with an average of 40 pack years (range 26-60). With a median follow-up of 2 months, the median PFS for the cohort was 1.5 mo (range 0.33-2.76 months). 61% (13/18) of patients had PFS <2mo. 39% (5/18) had PFS>2mo and of which, three patients had partial response with one maintaining stable disease and one patient with mixed response. At baseline (BL), there were no difference in T cell, CD4 or CD8 cell count between patients in the PFS<2mo and PFS>2mo groups. Patients with PFS<2mo had higher exhausted CD8 T cells compared to those in the PFS>2mo group (CD8+PD1+TIGIT+CD57+, PFS<3mo vs >3mo, cells/mL: 7.14±4.28, 1.58±1.37, p=0.009). Additionally, when analyzing the B-cell population, the PFS<2mo group had lower percentage of B cells compared to the group with PFS>2mo at baseline (6.0±7.5, 10.4±14, p=0.02). At day 7, the group with PFS<2mo had lower Treg compared to the group with PFS>2mo (2.88±1.38, 4.60±1.47, p=0.02). Also, the PFS<2mo group had an increase of B cells compared to the group with PFS>2mo at day 7(<2mo vs >2mo: 10.0±14, 4.37±3.07, p=0.04). Finally, the group with PFS>2mo had decrease in total monocytes (mono), classical mono, and immunosuppressive cells (PFS<2mo vs >2mo, Mono: 340±347, 253±106, p=0.04. Classical mono: 305±283, 155±109, p=0.02. CD14+HLA-DRneg: 105.8±96, 12±39, p=0.03) compared to the group with PFS<2mo by day 7. While no changes were seen between BL and day 7 for intermediate mono in PFS<2mo group, PFS>2mo group had decreased post treatment intermediate monocytes (BL vs day 7, cells/mL, intermed mono: 32.5±15,17.7±6.2, p=0.03). Conclusion: In this study investigating the SOC outcomes of tarlatamab, early progression was associated with higher presence of exhausted CD8 T cells, B cells, and immunosuppressive monocytes. Analysis of additional patients will be shared at AACR meeting. Citation Format: Dhauna Karam Prasad, Andre De Menezes Silva Corraes, Malvika Gupta, Audrey Ma, Chen Wu, Zuoyi Shao, Kevin Reagan, Rayaan Kamal, Ashley Potter, Abdullah Al-Ajmi, Syeda Mina, Sykler J. Taylor, Antonious Hazim, Anastasios Dimou, Kaushal Parikh, Mohammed Shanshal, Ailsa Luce, Anna Schwecke, Julian Molina, Aaron Mansfield, Katherine Smith, Lucy Holmes, Haidong Dong, Yi Lin, Konstantinos Leventakos. Immune cell profile changes in patients treated with tarlatamab for extensive stage small cell lung cancer in real world practice [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 3931.

Abstract Introduction: CAR-T therapy has transformed cancer therapy. with its first Food and Drug Administration (FDA) approved product in August 2017. Given the expanding indications and growing experience, particularly with management of immediate toxicities requiring specialized expertise such as Cytokine Release Syndrome (CRS) and Immune Effector Cell-Associated Neurotoxicity syndrome (ICANS), FDA recently removed the Risk Evaluation and Mitigation Strategies (REMS) for CD19 and BCMA targeting CAR-T on June 26th2025. In particular, given that CRS and ICANS typically occur in the first 2 weeks post CAR-T infusion, the minimum required stay near CAR-T treatment center is reduced from one month to 2 weeks. While this could reduce treatment burden for patients (pts) who need to travel away from home to receive CAR-T, there is a need to update the transition of care from CAR-T center to pts' local oncology practice. Pts carry in their rate of recovery and have varying supportive care needs in the second half of the month post CAR-T infusion. To assist with care transition and planning, we examine the use of AI multimodal large language model (LLM) to predict patients' supportive care needs 2 weeks after CAR-T infusion based on the clinical data at the time of CAR-T evaluation, prior to leukapheresis. This timepoint was selected to also maximize travel, lodging and caregiver planning for pts and family as well. Methodology: Pts who were treated with FDA-approved CAR-T at Mayo Clinic Rochester between December 2022 and June 2025 were examined. Based on experiences with referral practices in the Midwest region, we defined patients with high monitoring needs as those who between day 14-30 post CAR-T had either hospital admission due to recurrent CRS or ICANS or required transfusion of more than 1 blood product per week (red blood cell or platelets). Others were classified as low monitoring needs. Clinical notes, labs, vital signs and electrocardiogram at the time of CAR-T evaluation were abstracted and summarized by Gemini LLM for association with patients with high versus low monitoring needs post CAR-T. Model was developed and tested for predicted needs and concordance with actual outcome of these pts. Results: Among the 362 pts who received FDA approved CAR-T in the timeframe, 84 pts had high monitoring needs. An additional 84 pts with low monitoring needs were randomly selected from 278 pts. These were divided into a training cohort and test cohort of 84 pts each, with 42 pts with high and 42 pts with low monitoring needs. Demographics of the two cohorts displayed no statistical difference. LLM analysis of the clinical data at the time of CAR-T eval identified 5 categories associated with risk for high monitoring needs: 1) Disease status (clinical notes about bulky, progressive disease or CNS involvement); 2) inflammatory and tumor burden markers (CRP, LDH); 3) hematologic status (platelet, plt; neutrophil count ANC); 4) renal function; 5) performance status. Using common clinical lab parameter convention (platelets <100, ANC< 1, CrCl< 30ml/min), and prioritizing for not missing pts with high monitoring needs, LLM model was defined to predict patients for high monitoring needs if high risk feature was identified in category 1 or any category combinations; whereas patients with no high risk feature or only 1 category that is not disease status was predicted as low monitoring needs. In this model, the sensitivity was 85.7%, specificity 23.8%, F1 score 65.5%. Applying this model to test cohort 2, the sensitivity was 83.3%, specificity 28.6%, and F1 score 65.4%. Discussion: We demonstrate proof-of-concept that LLM can be used to analyze clinical data at the time of evaluation for CAR-T eligibility to predict for supportive care needs post treatment. Not surprisingly, features identified by LLM to be associated with high monitoring needs are those previously reported by us and others at the time of CAR-T evaluation and or pre-lymphodepletion to be associated with risk for cytopenia, infection, hospitalization and non-relapse mortality. Based on the summary output generated by LLM, additional opportunities for classification methods were identified to improve specificity and balanced accuracy of the LLM model. This is a promising tool for further development to assist clinicians and patients to plan their treatment journey.

1652 Background: Tarlatamab is a bispecific T-cell engager targeting DLL3 that has shown promise as a therapy in small cell lung cancer. Administering tarlatamab requires careful consideration of immune-related adverse events (AE), including cytokine release syndrome (CRS) and immune effector cell-associated neurotoxicity syndrome (ICANS). Prescribing information recommends patients to be monitored for 22-24 hours in an appropriate healthcare setting for cycle 1 (C1), days 1 and 8. At Mayo Clinic in Rochester MN (MCR), tarlatamab is initially administered in a hospital-based outpatient (HBO) setting utilizing an innovative remote patient monitoring system (RPM). In this study, we describe our RPM process and patient outcomes thus far. Methods: We retrospectively reviewed records of patients treated with tarlatamab from August through December 2024. All patients received tarlatamab in the HBO setting and were enrolled into RPM for C1, days 1–10. Patients attended daily HBO visits on days 1–3 and 8–10 of C1 and were required to stay within 30 minutes of the hospital with a 24-hour caregiver for 48 hours following infusion days in C1-2. Data on patient characteristics, frequency of RPM alerts, escalations, rate of CRS & ICANS, and need for hospitalizations were reported using descriptive metrics. Results: As part of RPM, patients are provided with a kit containing preconnected Bluetooth-enabled devices to measure vital signs (VS), including blood pressure (BP), heart rate (HR), temperature (T), and pulse oximetry (SpO2). The kit also includes a cellular-enabled tablet for electronic ICANS questionnaires, which upload directly into our electronic medical record system. Patients are required to log VS four times daily, monitored in real-time by a centralized virtual RPM nursing (RN) team. Embedded decision trees trigger alerts, prompting the RN team to contact patients and follow care pathways for escalations as needed. Among the 16 patients treated, the median age was 68 years, and the median ECOG PS was 1. During the 10-day RPM period, a total of 2,233 VS entries were recorded. Of these, 88% (14/16) of patients had alerts triggered, with a median of 2 alerts per patient. Alerts were mostly for out-of-range systolic BP (56%), HR (56%), and T (25%), with none for SpO2. The RN team escalated alerts in 50% (8/16) of the patients. Overall, 38% (6/16) of patients were managed entirely in the HBO setting. Among the 63% hospitalized, 56% had CRS limited to grade (G) 2, and 31% had ICANS limited to G3. Two patients were hospitalized for other indications. The mean length of stay was 2.6 days, with no ICU admissions. Conclusions: Our experience shows that frequent monitoring required with tarlatamab can be safely and effectively executed in an outpatient setting with the utilization of an RPM system. It can potentially minimize the burden of hospitalizations for patients and the healthcare system.

PURPOSE Cytokine release syndrome (CRS) of any grade occurs in 56%-80% of bispecific antibodies (BsAbs) used in multiple myeloma (MM). Risk mitigation strategies are required to expedite escalation of care if toxicities develop. The rarity of grade 3 or 4 CRS compels protocolized outpatient management of BsAbs as outpatient practice may avoid hospital admissions and costly resource utilization without compromising safety. MATERIALS AND METHODS Patients with MM who received BsAb step-up dosing (SUD) from August 23, 2023, to March 29, 2024, were enrolled. Baseline demographics, patient outcomes, and CRS management were reviewed to assess the safety of outpatient practice. Resource utilization was also analyzed. RESULTS In this study, 34 patients received outpatient SUD of BsAbs (teclistamab n = 17, talequetamab n = 17) with 16 remaining outpatient throughout the entire SUD period. CRS occurred in seven patients who did not require hospitalization, demonstrating the safety of outpatient management of BsAb toxicity, regardless of low-grade CRS. CRS was observed in 24 patients (maximum grade 2) and immune effector cell–associated neurotoxicity syndrome in four patients (maximum grade 3). All patients with CRS received steroids, including 12 patients at home when instructed. Tocilizumab was given to 13 patients, accounting for 18 doses. If all doses of BsAbs and tocilizumab were given outpatient, the medication margin would be $115,004 in US dollars (USD). CONCLUSION An outpatient-based practice for BsAb administration demonstrated safety and cost savings. The description of our practice and results of this study provide valuable insights into the safety, feasibility, and resource stewardship of outpatient management of BsAbs. Future research should attempt to predict and stratify CRS risk to deliver a tailored supportive strategy and continued increase of outpatient management.

Cytokine release syndrome (CRS) commonly occurs during initiation of bispecific T-cell engaging therapy (BsAbs) for multiple myeloma (MM) (NEJM 2022;387:495, NEJM 2022;387:2232, Nat Med 2023;29:2259). Pre-medications, step-up dosing (SUD), and monitoring minimize high-grade toxicity risk, while protocolized outpatient (OP) management of BsAbs and toxicities could reduce resource utilization and improve patient satisfaction. At Mayo Clinic, SUD, clinical assessment and grade 1 CRS management occurs primarily as an OP for patients with MM receiving BsAbs. This study aims to assess the current OP management practices of BsAbs and related toxicities including patient outcomes, admission rates, and resource utilization. Patients with MM who received SUD from August 23, 2023, to March 29, 2024, were included. Baseline demographics, patient outcomes, and CRS management details are summarized. Resource utilization outcomes included level of care, length of hospital stay (LOS), and high-cost drug administration. 38 patients received MM BsAbs during the study timeframe. 34 patients initiated OP SUD (teclistamab n=17 and talquetamab n= 17). The median age was 71 years, 59% were male, 82% were white, and 88% had Medicare coverage. Median number of prior lines of therapy was 6. There were 317 OP encounters with a median of 10 encounters per patient. Of these, 72% were planned in-person assessments, 5% were unplanned clinic visits for potential toxicity-related symptom assessment, and 20% were phone visits that obviated in-person assessment. 66 of 68 doses of talquetamab (total of 1,628 mg) and 48 of 50 doses of teclistamab (total of 2,048 mg) were administered and billed in the OP setting. 24 patients (71%) that initiated OP SUD developed CRS (grade 1 n=16, grade 2 n=7). Management of the initial CRS event included steroids and acetaminophen, with or without tocilizumab. 2,600 mg of tocilizumab administered in the OP setting. Recurrent CRS was seen in 5 patients and also treated entirely OP. 4 patients developed ICANS (grade 1 n=3, grade 3 n=1). Hospitalization following OP SUD initiation occurred in 18 (53%) patients at some point during treatment. Indications included grade 2 CRS (n=6), ICANS (grade 1 n=3), refractory CRS (n=7), infection (n=1), GI bleed (n=1), planned observation for comorbidities or prior CRS (n=2), loss of caregiver (n=1), atrial fibrillation (n=1), heart failure (n=1), and pain crisis (n=1). Median LOS was 2 days. Recurrent admission occurred in 41% and median LOS was also 2 days for subsequent admissions. Two patients were admitted to the intensive care unit for hypotension or arrhythmia. 1 patient required dialysis. In total for SUD during the study time frame, 51 mg of teclistamab, 176 mg of talquetamab, and 8,240 mg of tocilizumab were billed under an IP encounter, which likely was due to inpatient administration or hospital admission within 72 hours of receiving the OP SUD. This descriptive study of our OP BsAb management practice demonstrated that 47% of patients remained OP, even with a CRS event. The CRS rate did not surpass those in landmark clinical trials and aligns with real-world data previously reported (Blood Cancer J 2024;14:35.). Availability and use of patient monitoring capabilities along with lack of sentinel outcomes emphasizes the safety of an OP BsAb practice. Drivers of financial burdens and savings include location and reimbursement of high-cost drugs and admission rates. Median LOS was less than half of what's anticipated with inpatient SUD. Inpatient administration of high-cost drugs can be more costly compared to OP due to associated medical costs and insufficient reimbursement in a population primarily insured by Medicare which reimburses inpatient mediations based on the Diagnostic Related Group (DRG) unless the medication has New Technology Add on Payment (NTAP). Using expected Medicare reimbursement rates and wholesale acquisition costs while excluding variable DRG reimbursement and NTAP impacts for teclistamab, the total net revenue improvement for shifting use of all teclistamab, talquetamab, and tocilizumab OP is $115,004 for the period analyzed (CMS.gov 2024). In conclusion, OP management of patients undergoing SUD for MM BsAbs is safe and has substantial reductions in resource utilization and costs.

Background: Management of cytokine release syndrome (CRS) with chimeric antigen receptor T cell therapy (CAR-T) has evolved in the last decade. First line treatment tocilizumab (toci) was initially given for grade 2 or higher CRS in clinical trials (Toci/CRS≥2). Earlier use of toci in grade 1 CRS was found to be safe without negative impact on clinical response in cohort 4 of ZUMA-1 study for axicabtagene ciloleucel (axi-cel) for B-cell non-Hodgkin lymphoma (NHL; Toci/CRS1). This practice was also adopted for other CAR-T therapies for other disease indications. Subsequently, FDA also updated the package insert for axi-cel in 2021 to include the use of prophylactic dexamethasone (ppx dex) to be given on days 0, 1, 2 of CAR-T infusion based on the results from cohort 6 of ZUMA-1. We examined CRS and ICANS (immune effector cell associated neurologic syndrome) outcome of patients who received FDA approved axi-cel in standard-of-care (SOC) practice with these evolving practices. Of note, adoption of ppx dex has been variable across CAR-T treatment centers in the US (Khurana et al, Blood 2022). At Mayo Clinic Rochester, patients considered high-risk for severe CRS/ICANS (those who received bridging therapy and had LDH above upper limit of normal and/or with secondary CNS involvement or other CNS pathologies) would be considered for ppx dex. Methods: Medical records of patients who received axi-cel in SOC between 2018 and 2023 at Mayo Clinic Rochester were reviewed. ASTCT guideline was used for CRS and ICANS grading. Categorical variables were compared using Chi-squared test and continuous variables were compared using Kruskal-Wallis test. Results: Among 187 pts receiving SOC axi-cel, the median age was 60 years and 121 (65%) were males, 152 (81%) had CRS, of which 73 (39%) were grade ≥2; 97 (52%) had ICANS, of which 65 (35%) were grade ≥2. Of the 152 CRS pts, 54/152 (36%) pts never received toci or ppx/dex. Of the pts who received Toci, 35/152 (23%) received Toci when CRS is at least grade 2 (Toci/CRS≥2), 49/152 (32%) received Toci/CRS1 without ppx dex (Toci/CRS1), and 14 (9%) received ppx dex with Toci given in CRS1 when pts developed CRS (PPX DEX). Baseline demographics were comparable except age (Toci/CRS≥2: 58y; Toci/ CRS1: 63y; ppx dex: 50y; p=0.001) and pre-LD CRP (Toci/CRS≥2: 34mg/L; Toci/ CRS1: 6mg/L; ppx dex: 16mg/L; p=0.002). Pre-LD LDH and ferritin were also comparable. PPX DEX or Toci/CRS1 had significantly lower rate of maximum grade CRS≥2 (PPX DEX: 4 (29%); Toci/ CRS1: 20 (41%); 35 (100%); p<0.001) and duration of CRS (PPX DEX: 2.5d; Toci/ CRS1: 3d; Toci/CRS≥2: 5d; p<0.001). Time to onset of CRS was not statistically different between the 3 groups. Not surprisingly, Toci/CRS1 group had higher overall rate of toci use, compared to . Interestingly, use rate of ≥2 doses of Toci or siltuximab was significantly higher in Toci/CRS1 or PPX DEX (PPX DEX: 8 (57%); Toci/ CRS1: 26 (53%); 9 (26%); p=0.02). Pt who received PPX DEX or Toci/CRS1 had a significantly lower rate of ICANS (PPX DEX: 6 (43%); Toci/ CRS1: 33 (67%); 32 (91%); p=0.001) and rate of max grade ICANS ≥2 (PPX DEX: 3 (21%); Toci/ CRS1: 23 (47%); 27 (77%); p<0.001). The time to onset and duration of ICANS was not different among the 3 groups. Conclusion: Our real-world study shows that earlier use of Toci in grade 1, compared to its first use in grade 2, can result in lower overall incidence of more severe CRS, duration of CRS, incidence of ICANS and more severe ICANS. While we selected patients at higher risk for having more severe CRS in our PPX DEX cohort, experience to date suggests that these patients, compared to toci/CRS>=2 cohort, could also have reduced severity of CRS and ICANS with this updated management.