Gupta, Mansha

e23274 Background: Treatment-related symptoms substantially impair quality of life (QoL) and drive unplanned acute care use in oncology. Although patient-reported digital symptom monitoring is widely studied, outcomes remain inconsistent. We examined how intervention design influences clinical impact. Methods: We conducted a scoping systematic review of prospective randomized trials and pragmatic real-world studies evaluating patient-reported digital symptom monitoring or management interventions in adult oncology populations. Eligible studies reported symptom burden, QoL and/or acute care utilization. Data were abstracted on cancer type, digital modality, triage approach, and direction of outcomes. Protocols and qualitative studies were reviewed for context but excluded from outcome synthesis. Results: Across studies involving solid tumors and hematologic malignancies, interventions used web-based portals, mobile applications or interactive voice response systems. A consistent pattern emerged across the evidence base: interventions that paired real-time symptom reporting with structured, rule-based triage and proactive clinician response demonstrated improvements in symptom burden and QoL across multiple settings. In contrast, passive monitoring or self-management alone showed limited or no benefit despite similar reporting intensity. Effects on acute care utilization were heterogeneous, with reductions most often seen in pragmatic, workflow-integrated implementations. Conclusions: The clinical value of patient-reported digital symptom monitoring lies not in symptom collection itself, but in how reported data are reviewed and acted upon by clinicians. Monitoring alone is insufficient. Implementation-ready models that embed actionable triage and clinician response workflows are essential to improve patient outcomes and reduce avoidable utilization. Intervention approaches and clinical outcomes of digital symptom monitoring in oncology. Intervention Approach Digital Modality Actionable Triage / Clinician Response Symptom Burden Quality of Life Acute Care Use Monitoring with proactive clinician response Portal / App / IVR Yes (rule-based, clinician-reviewed) Improved Improved Reduced / Mixed Monitoring with automated alerts only Portal / App Partial Mixed Mixed NR Passive monitoring or self-management App / Portal No No benefit No benefit NR Pragmatic, workflow-integrated models Mixed Yes + staffing workflows Improved Improved Reduced Heterogeneous or non-integrated implementations Mixed Variable Mixed Mixed Mixed Abbreviations: IVR = Interactive Voice Response; QoL = Quality of Life; NR = Not Reported.

e13683 Background: Immune checkpoint inhibitors (ICIs) have transformed cancer treatment but are frequently complicated by immune-related adverse events (irAEs), which may result in treatment interruption, hospitalization and mortality. Reliable pre-treatment tools to identify patients at higher risk for irAEs, particularly checkpoint inhibitor pneumonitis (CIP) remain limited. Artificial intelligence (AI)-based prediction models have emerged but their clinical performance and readiness for implementation are unclear. Methods: We systematically searched PubMed, Embase, Scopus and Web of Science (inception-January 2025) for studies developing AI-based models to predict irAEs or CIP in patients receiving ICIs. Eligible studies included radiomics, deep learning, natural language processing and clinical machine-learning approaches. Two reviewers independently extracted data on cancer type, toxicity endpoint, modality, algorithm, validation strategy and model performance. Study design and reporting characteristics were assessed. Due to substantial methodological heterogeneity, results were synthesized descriptively. Results: Fifteen studies met inclusion criteria, including mixed solid tumor cohorts and lung-predominant populations. Predictive targets included any-grade irAEs and organ-specific toxicities, most commonly pneumonitis. Model approaches ranged from clinical and electronic health record–based models to CT- and PET/CT-based radiomics and multimodal imaging-clinical frameworks. Reported discriminatory performance varied widely, with AUC values ranging from 0.64 to > 0.90 across model types. The most consistent performance was observed in deep learning and multimodal radiomics-clinical models for pneumonitis, with AUCs frequently between 0.75 and 0.92, whereas several clinical-only models demonstrated more modest performance. Most studies were retrospective and single-center, used heterogeneous irAE definitions and lacked standardized imaging protocols. External validation and calibration assessment were uncommon. Conclusions: AI-based models for predicting irAEs in ICI-treated patients demonstrate promising performance, particularly for pneumonitis using radiomics and multimodal approaches. However, current evidence is limited by heterogeneity, scarce external validation and uncertain generalizability. Prospective, multi-institutional validation and standardized outcome definitions are needed before AI-driven irAE risk prediction can be integrated into routine oncology practice to guide surveillance and patient counseling.

Abstract Background: Hematologic toxicity, including early and prolonged neutropenia, anemia, and thrombocytopenia, is among the most common and clinically significant complications after CD19, CD20, CD22 and BCMA-directed CAR-T therapy. Several clinical scoresmost notably CAR-HEMATOTOX (CAR-HT) and the refined ALL-HThave been developed to predict post-infusion cytopenias, but their comparative performance across diseases has not been systematically evaluated. No prior review has summarized existing models or assessed the need for AI-based predictive approaches. Methods: We conducted a systematic review of studies that developed or validated predictive models or risk scores for hematologic toxicity after CAR-T therapy in hematologic malignancies. Eligible studies included clinical, cytokine-based, or composite predictors and reported discrimination metrics such as area under the curve (AUC). Extracted variables included CAR-T product, target antigen, disease type, predictors used, model type, validation strategy, and performance statistics. Results were synthesized qualitatively due to heterogeneity in model structure and outcome definitions. Results: Studies on the CAR-HT score in B-cell lymphomas and multiple myelomas reported AUC values of 0.89 and 0.82, respectively, for predicting severe or long-term neutropenia after CD19 and BCMA-directed CAR-T therapy. Studies on the ALL-HT score (a modified/refined version of CAR-HT replacing ferritin with bone-marrow burden to improve discrimination in B-ALL) reported AUC 0.84-0.90 for predicting prolonged neutropenia and worse overall survivability (OS) after CD19/CD22 CAR-T. A multicenter study including aggressive/indolent NHL, MM/PCL, and ALL patients treated with CD19, CD20, or BCMA CAR-T reported AUC 0.87 (eIPM-Pre) and 0.88 (eIPM-Post) for predicting ICA-HT. A CD19 CAR-T study in B-ALL and LBCL reported AUC 0.85 for an early hematotoxicity nomogram using TNF- and CRP. Conclusion: Existing prediction tools for hematologic toxicity after CAR-T therapy demonstrate strong but disease-specific performance. Yet inconsistent endpoints and heterogeneous predictors limit clinical generalizability. No current model incorporates contemporary AI or machine-learning methods, underscoring a significant unmet need. Advanced predictive frameworks may allow earlier identification of patients at risk for severe or prolonged cytopenias and guide more individualized supportive care, with the potential to reduce downstream complications. Citation Format: Mansha Gupta, Abhijith Vemulapalli, Swathi Cherukuri, Rithish Nimmagadda, Akhil Jain. Predictive models for hematologic toxicity after CAR-T therapy: A systematic review of current risk scores and unmet AI needs [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 5202.

Abstract Background: Sickle cell crisis (SCC) remains a leading cause of emergency hospitalization and acute morbidity in patients with sickle cell disease (SCD). With improved survival in SCD, long-term complications such as cardiovascular disease are emerging as critical determinants of morbidity and mortality. While right-sided heart failure has been adequately explored in SCD, the specific contribution of the other cardiovascular conditions to in-hospital mortality during sickle cell crisis remains unclear. This study evaluates the independent impact of cardiovascular comorbidities on in-hospital mortality among adults hospitalized for SCC using a large, nationally representative database. Methods: This is a retrospective analysis involving adult hospitalizations (≥ 18 years old) for SCC in the United States (U.S) using 2016-2020 National Inpatient Sample (NIS). The study was exempt from institutional review board approval as the NIS database contains deidentified patient information. ICD-10 codes used for SCC were: D570, D5721, D5741, D5743, D5745, D5781. Regarding cardiovascular co-morbidities, the study considered age, race (African American, Hispanic and Caucasian), dyslipidemia, hypertension, diabetes mellitus (both type 1 and type 2), systolic and diastolic heart failure, rheumatic valvular heart disease, atrial fibrillation/flutter, pulmonary hypertension, chronic coronary artery disease and ischemic stroke. Univariable Cox regression was performed to identify independent predictors associated with in-hospital mortality among patients admitted for SCC. All factors from the univariable analysis with p<0.20 were included in a multivariable Cox regression model. All the outcomes were adjusted for age, sex, and Charlson Comorbidity Index. P-value of ≤0.05 was used to determine statistical significance. Results: The current study analyzed 424,540 SCD patients admitted for SCC (mean age 32.4 years, 55.4% females), from 2016 to 2020. Each additional year of age beyond 18 was associated with a linearly increasing in-hospital mortality among study patients hospitalized for SCC. This observation was confirmed in both univariate (HR: 1.06, 95% CI: 1.05–1.07, p<0.001) and multivariate (HR: 1.05, 95% CI: 1.04–1.06, p<0.001) analyses. On univariable analysis of cardiovascular co-morbidities in patients admitted for SCC, a higher in-hospital mortality was conferred by: hypertension (HR: 2.40; 1.95 – 2.94, p <0.01) dyslipidemia (HR: 2.38; 1.54 – 3.65, p <0.01), diabetes mellitus (HR: 2.15; 1.51 – 3.04, p <0.01, systolic (HR: 5.20; 3.70 – 7.31, p <0.01) and diastolic heart failure (HR: 2.11; 1.44 – 3.08, p <0.01), atrial fibrillation/flutter (HR: 5.43; 4.04 – 7.32, p <0.01), pulmonary hypertension (HR: 2.34; 1.75 – 3.13, p <0.01) and ischemic stroke (HR: 5.75; 3.30 – 10.03, <0.01); a lower in-hospital mortality was associated with the African American race (HR: 0.43; 0.20 – 0.91, p = 0.027). In multivariable analysis of cardiovascular comorbidities that demonstrated nominal significance (p < 0.20) in univariable analysis, only the following remained significantly associated with in-hospital mortality: systolic heart failure (HR: 2.65; 1.77 – 3.98, p <0.01), atrial fibrillation/flutter (HR: 2.01; 1.41 – 2.8), p <0.01) and ischemic stroke (HR: 3.25; 1.85 – 5.71, p <0.01). Although not statistically significant in univariable analysis (p = 0.065), chronic coronary artery disease (CAD) emerged as a significant predictor of reduced in-hospital mortality in the multivariable analysis (HR: 0.52; 0.29–0.91, p = 0.021). Conclusions: Among adults hospitalized for sickle cell crises, select cardiovascular comorbidities, including systolic heart failure, atrial fibrillation/flutter, and ischemic stroke, were independently associated with significantly increased in-hospital mortality. Interestingly, CAD was linked to reduced mortality in adjusted models. This may be attributed to closer medical surveillance and management in patients with established CAD, along with the potential protective mechanism(s) in SCD that limit atherosclerotic progression — warranting further investigation

Abstract Background: Chemotherapy-induced cardiomyopathy (CIC) is a serious, and often irreversible complication in patients treated for hematologic malignancies. While therapies for hematologic malignancies continue to evolve, data on trends in in-hospital mortality among patients with concurrent hematologic malignancies and CIC are limited, and disparities by race and sex remain poorly characterized. This study examines national trends and demographic disparities in in-hospital mortality among patients with hematologic malignancies who developed CIC. Methods: The United States (U.S) National Inpatient Sample (NIS) from 2016 to 2020 was queried for patients diagnosed with malignant hematologic and lymphoid neoplasms who also developed CIC. Hematologic malignancies were identified using ICD-10 codes C81–C96, which include: C81 for Hodgkin lymphoma; C82–C86 for non-Hodgkin lymphomas; C88 for malignant immunoproliferative diseases (e.g., Waldenström macroglobulinemia, heavy chain disease); C90–C95 for leukemias of lymphoid, myeloid, monocytic, mixed, and unspecified cell types; and C96 for other or unspecified malignant neoplasms of lymphoid, hematopoietic, and related tissue. CIC was identified using ICD-10 code I42.7. The study was exempt from institutional review board approval as the NIS database contains deidentified patient information. Multivariable regression analysis was performed to determine the odds of in-hospital mortality among patients with hematologic malignancies who developed CIC. Multivariable logistic regression followed by marginal effects was used to plot yearly trends in in-hospital mortality. All the outcomes were adjusted for gender, race, Charlson comorbidity index and hospital characteristics. P < 0.05 was used to determine statistical significance. The analysis was performed on STATA 16 software. Results: The study identified a total of 3,021,434 hospitalizations with hematologic malignancies, of which 11,115 (0.004%) had secondary diagnosis of CIC (mean age = 56.5 years, 52.5% males). Among 11,115 patients with hematologic malignancies who developed CIC, 740 (6.6%) patients died during the hospitalization. Multivariable logistic regression followed by marginal effects estimated a decrease in adjusted in-hospital mortality trend from 6.5% in 2016 to 4.3% in 2020. However, this decreasing mortality trend was not statistically significant after adjusting for gender, race, Charlson comorbidity index, and hospital characteristics (OR: 0.93; 95% CI: 0.83 – 1.05; trend p = 0.260). In-hospital mortality did not differ significantly between male vs. female patients with hematologic malignancies who developed CIC (OR: 1.02; 95% CI: 0.72 – 1.43; p = 0.921). There was no difference in overall in-hospital mortality of patients of White (reference) vs. Black (OR: 1.12; 95% CI: 0.72 – 1.76, p = 0.61) or Hispanic (OR: 0.63; 95% CI: 0.32 – 1.25, p = 0.18) race. However, males of the Black race demonstrated a lower in-hospital mortality vs. males of the White race (OR: 0.26; 95% CI: 0.09 – 0.77, p = 0.01). In addition, females of the Black race showed a higher in-hospital mortality vs. females of the White race (OR: 2.43; 95% CI: 1.40 – 4.25, p <0.01). Conclusions: In patients with hematologic malignancies who developed CIC, overall in-hospital mortality did not differ significantly by race alone. However, on sex-based stratification, Black males had lower in-hospital mortality compared to White males, while Black females had higher mortality than White females. These differences highlight potential race–sex disparities in outcomes that warrant further investigation.