bluuebunny/crossref_metadata_2025_split huggingface dataset:
Cost-utility of geriatric assessment (GA) in older adults with cancer: A model-based economic evaluation of four randomized controlled trials (RCTs).
Authors: Akseer Selai , Yeretzian Shant , Sahakyan Yeva , Abrahamyan Lusine , Puts Martine , Alibhai Shabbir M.H. 
Published: June 2024

1509 Background: Geriatric assessment (GA) is a guideline-recommended approach to optimize cancer management in older adults undergoing chemotherapy. Our recent cost-utility analysis of the published 5C (Clinical and Cost-effectiveness of a Comprehensive geriatric assessment and management for Canadian elders with Cancer) RCT comparing GA and management (GAM) with usual care in older adults with cancer did not demonstrate cost-effectiveness overall. However, the trial was limited by <5% of study participants receiving GA prior to starting treatment. Three other GAM RCTs (GAIN, GAP-70, and INTEGERATE) have recently been published with evidence of efficacy on clinically relevant endpoints. Whether these are more cost effective than 5C is unclear. We evaluated the cost-effectiveness of GAM versus usual care in older adults with cancer using a decision model under a range of plausible scenarios representing the 4 trials. Methods: We performed cost-effectiveness analyses using the healthcare payer perspective and a 12-month time horizon. We incorporated Canadian costs and utility data from 5C, and used intervention details and effectiveness data from the three RCTs. We reported healthcare costs per quality-adjusted life year (QALY) and the incremental net monetary benefit (INMB) using a $50,000 per QALY threshold. In scenario analyses we examined the main cost drivers. Results: Across trials, the average QALY per patient ranged 0.577–0.662 for GA and 0.606–0.665 for UC, and the average total costs $31,234–$39,432 for GA and $29,261–$41,756 for UC. Chemotherapy expenses accounted for 46%-66% of total costs across trials. The INTEGERATE trial had a positive INMB of $6,074. The GAIN and GAP-70 trials had negative INMB value of -$2,123 and -$1,172, respectively. In comparison, in 5C, the total costs were $39,812 and $37,450 for GAM and UC, respectively, and QALYs were 0.728 and 0.751, respectively; the INMB was $-2,713. Conclusions: Trial results and the associated model of care from INTEGERATE suggested a positive net monetary benefit, primarily driven by reduced hospitalization. Evaluation of cost-effectiveness under a range of plausible scenarios from RCTs can provide important insights about GAM. Our results add to the growing data supporting the need to implement GAM in older adults with cancer starting chemotherapy and argue for its cost effectiveness under specific scenarios. Future trials should include hospitalization outcomes. Future economic analyses need to accurately capture chemotherapy costs.
Quality of life for older patients with metastatic cancer in Brazil: A telehealth-based geriatric assessment and supportive care intervention (GAIN-S).
Authors: Bergerot Cristiane Decat , Bergerot Paulo Gustavo , Razavi Marianne , Silva Jonas Ribeiro Gomes , Cerveira Jose Adolfo , Franca Marcos V S , Fuzita William Hiromi , Vasconcellos Vitor Fiorin de , Matos Neto Joao Nunes , Patriota Carolina Bezerra , Hada Aline Lury , Anjos Gabriel dos , Buso Murilo , Laloni Mariana , Ferreira Carlos G. M. , Nipp Ryan David , El-Jawahri Areej , Soto Pérez de Celis Enrique , Dale William 
Published: June 2024

1514 Background: Older adults with cancer often present unique geriatric and supportive care needs. We sought to evaluate the impact of a telehealth-based multimodal geriatric assessment (GA)-guided supportive care interventions (GAIN-S) program among older patients with metastatic cancer in a Brazilian cancer center. Methods: This is a randomized trial. Patients aged 65+ years with metastatic cancer, undergoing treatment, regardless of ECOG status, were enrolled. Following the baseline assessment, patients were randomized 1:1 to receive usual care or GAIN-S. The GAIN-S intervention provided remote, telehealth-based consultations with a nutritionist, psychologist, psychiatrist, and certified fitness trainer. At baseline and 3-months, patients were assessed for emotional symptoms (Geriatric Depression Scale), functional status (Instrumental Activities of Daily Living), coping (BriefCope), and quality of life (Functional Assessment of Cancer Therapy-General). Independent t-tests and linear mixed model were used. Results: Out of 86 approached patients, 80 were enrolled (40 per arm), with 3 deceased during treatment (usual care: 2, GAIN-S: 1). Most patients were female (56%), with a median age of 73 years (range, 65-68). Most common cancers were genitourinary (28%) and breast (23%). At baseline, there were no differences in the outcome measures between arms. However, from baseline to 3-months, patients assigned to GAIN-S had improvements in emotional symptoms, functional status, and quality of life compared to usual care (Table). Additionally, those assigned to GAIN-S reported increased utilization of active coping, acceptance, and positive reframing at 3-months. Conclusions: GAIN-S demonstrated significant improvements in emotional well-being, functional status, and quality of life among older patients with metastatic cancer. These findings underscore the potential of such telehealth-driven approaches to enhance the supportive care experience in diverse healthcare settings. [Table: see text]
Medicaid expansion and cancer stage at diagnoses during the COVID-19 pandemic in the United States.
Authors: Han Xuesong , Yang Nuo Nova , Fan Qinjin , Nogueira Leticia M , Yabroff Robin 
Published: June 2024

1526 Background: Substantial cancer underdiagnosis, especially for early-stage diseases, occurred during the COVID-19 pandemic in the US in 2020. The Affordable Care Act expanded Medicaid to nonelderly adults with income up to 138% Federal Poverty Level in states that opted in, potentially facilitating residents’ access to care during pandemic-related financial and employment instability, thus aiding in timely detection of cancer. This study examines the association of Medicaid expansion with changes in cancer stage at diagnosis during the COVID-19 pandemic. Methods: Adults aged 18-64 years newly diagnosed with cancer in 2019 and 2021 were identified from the National Cancer Database. Year 2020 was excluded to avoid confounding from distinct geographic patterns of COVID-19 infections. A difference-in-differences (DD) approach was used to compare the changes in proportions of patients diagnosed at stage I/II in 2021 vs 2019 between Medicaid expansion states and non-expansion states. Adjusted DD estimates were calculated with linear probability models and stratified by key sociodemographic factors and cancer type. Results: A total of 427,368 adults newly diagnosed with cancer in 2019 and 393,961 in 2021 were identified. Patients diagnosed with stage I/II decreased nationwide, however, decreases were larger in non-expansion states (from 58.3% in 2019 to 56.9% in 2021) compared to expansion states (61.4% in 2019 to 60.7% in 2021), leading to a DD of 0.92 percentage points in early-stage cancer diagnosis associated with Medicaid expansion (95% CI =0.46-1.38). The association was strongest among individuals aged 18-44 years (DD=1.46, 95% CI=0.42-2.49), men (DD=1.46, 95% CI=0.73-2.19), those living in most socioeconomically deprived areas (DD=1.40, 95% CI=0.41-2.40) and nonmetropolitan areas (DD=1.82, 95% CI=0.56-3.09). When stratified by cancer type, the largest associations were seen for esophageal cancer (DD=3.86, 95% CI=0.26-7.45) and prostate cancer (DD=2.81, 95% CI=1.43-4.18). Conclusions: Substantial cancer underdiagnosis and declines in proportion of early-stage diagnoses continued nationwide in 2021, the second year of the COVID-19 pandemic. However, the pattern was worse in non-expansion states, suggesting a protective effect of Medicaid expansion on cancer early diagnosis during the public health emergency. Findings may inform policy makers and the public in the 10 states that have yet to expand Medicaid eligibility.
Impact of age on clinical trial availability for AYAs with cancer: A time-trend analysis.
Authors: Sheth Bhutada Jessica , Mittal Nupur , Freyer David R. , Roth Michael 
Published: June 2024

1534 Background: Low participation of adolescents and young adults (AYAs, age 15-39 yrs at diagnosis) in cancer clinical trials (CCT) limits their access to novel therapies and hinders the ability to study disease biology and age-specific toxicities. Patient age may be an important barrier as younger and older AYAs may be excluded depending on the trial’s focus. In 2017, the American Society of Clinical Oncology and Friends of Cancer Research issued a joint statement encouraging removal of age as a CCT eligibility criterion unless there is specific biological rationale. To evaluate the impact of this statement, we undertook this analysis of CCT availability for AYAs. Our primary objective was to compare the number of CCT available for AYAs, overall and by tumor types, from 2019-2023 vs previously published data from 2007-2018. The availability of early phase trials and study sponsor was also assessed. Methods: We identified CCT registered on ClinicalTrials.gov from January 2019 to July 2023 enrolling patients with 10 malignancies relevant for AYAs (Hodgkin lymphoma, anaplastic large cell lymphoma, melanoma, extracranial germ cell tumors [GCT], medulloblastoma, thyroid cancer, Ewing sarcoma, osteosarcoma, rhabdomyosarcoma [RMS], and synovial sarcoma). Trials were categorized as adult (≥18 yrs), transitional (patients < or ≥18 yrs), or pediatric (<18 yrs). Transitional trials with an age range 12-18 to <40 yrs were defined as AYA specific trials. Early phase included phase 1 and 1/2 trials; late phase included phase 2/3 and 3 trials. Trial availability from 2007-2018 was identified from a prior analysis by deRojas et al in 2019 (PMID:32337483). The proportion of trials in each category was compared between 2007-2018 and 2019-2023 using z-tests. Results: There were 1071 eligible trials registered on Clinicaltrials.gov: 840 (78%) adult, 226 (21%) transitional, and 5 (0.5%) pediatric. Four trials were AYA-specific. Compared to the prior 10 years, the proportion of transitional trials did not change overall (19% vs 21%, p=0.24) or within any disease. Whereas the proportion of trials available at age 17 has not significantly changed overall (20% vs 21%; p=0.48) or within any disease, the proportion of trials available at age 18 significantly increased (95% vs 98%; p<0.001) in aggregate and specifically for Ewing sarcoma (93% vs 100%; p=0.003), GCT (96% vs 99%; p=0.002), medulloblastoma (86% vs 98%; p=0.006), RMS (87% vs 98%; p=0.007) and thyroid cancer (92% vs 98%; p=0.01). Time trends for transitional trials by phase and sponsor are summarized (Table). Conclusions: Availability of transitional trials for AYAs has not increased, particularly for those <18 yrs. The 18-yr-old age limit continues to be an obstacle in CCT availability and enrollment. [Table: see text]
TriHealth Cancer Institute’s collaboration with the Tempus AI TIME program impact on clinical trial operations and enrollment.
Authors: Maher James F. , Mallahan Samantha , Claes Jason Joseph , Thompson Wayne , Kuritzky Benjamin , Neff Robert , Shatavi Seerin Viviane , Rice Courtney , Huelsman Karen M. , Budde Leanne S. , Adhami Faisal , Shulman Megan , Degele Meghan , Donovan Blathnaid , Skelly Danielle , Patnaude Emily , Clifford Christian , Darmofal Annie , Osterman Chelsea Kendall , Cooney Matthew M. 
Published: June 2024

1553 Background: TriHealth Cancer Institute has the largest personalized medicine program serving Cincinnati. TriHealth’s EMR integration with Tempus AI TIME allows for patient screening using an AI powered software TApp that matches to a broad clinical trial portfolio. Each patient is followed using LINK portal and trials can be activated rapidly via TIME’s operational model. The goal of this collaboration is to increase TriHealth’s patients' access for clinical trials. Methods: The TApp is software utilizing subject data, trial eligibility criteria, and NLP models for AI matching to TIME studies. Unique TApp searches occur whenever there is new patient data or changes to eligibility criteria. All TApp matches are reviewed by Tempus AI nurses followed by confirmatory screening by TriHealth’s staff. Trials were activated using the TIME program’s standardized operational methods including a pre-negotiated rate card, trials agreement, and a central IRB. Data included number of patients screened, unique TApp searches, trial activations, consents, operational metrics, and financial impact. Results: From January 2022 - March 2023, TApp performed 8,653,397 unique searches for 135 TIME trials on TriHealth’s population of 18,823 patients. During this period, 985 trial eligibility criteria were modified and 200,776 patient clinical updates occurred. There were 2,032 potential matches (1,944 interventional, 88 observational) for 45 different trials. Tempus AI nurses spent 389 hours reviewing all 1,944 interventional matches and of these sent 284 to TriHealth for evaluation. For TIME studies, 28 patients consented (18 interventional, 10 observational) and 7 trials were activated (4 interventional, 3 observational). Total trial billable revenue for TIME studies was approximately $241,500 ($34,500 average per trial). Regarding non-TIME trials, 3 patients consented on 3 interventional studies. Activation time for non-TIME trials compared to TIME trials was 186 days versus 37 days. Conclusions: TriHealth’s partnership with TIME evaluated 18,800+ patients for 135 trials. Over 8.6M unique TApp searches resulted in 2,000+ patients screened by nurses. Operationally, TIME trials resulted in a 2.3X increase in the number of trial activations, an 80% reduction in days to activation, and 9.3 fold increase in enrollment compared to non-TIME studies. The use of AI enabled patient screening, combined with a structured trial activation process, improved patient enrollment at a community health system. [Table: see text]
A machine learning algorithm based on multi-omics biomarkers for the detection of tumor microsatellite instability.
Authors: Strickland Kyle C , Pabla Sarabjot , Wallen Zachary D , Nesline Mary K , Ko Heidi Chwan , Previs Rebecca A. , Conroy Jeffrey M , Sathyan Pratheesh , Saini Kamal S. , Jensen Taylor J. , Caveney Brian , Eisenberg Marcia , Ramkissoon Shakti H , Severson Eric A 
Published: June 2024

1554 Background: Microsatellite instability (MSI) occurs via defects in mismatch repair (MMR) and is characterized by high tumor mutational burden (TMB) and a robust inflammatory response. Accurate MSI-high classification of immune-evasive tumors is vital due to the effectiveness of immune checkpoint inhibitor (ICI) therapy, such as pembrolizumab, which is FDA approved for all MSI-high tumors irrespective of anatomic origin. Detection methods of MSI-status vary widely, including immunohistochemistry (IHC), polymerase chain reaction (PCR), and next-generation sequencing (NGS). We developed a machine learning (ML) model to predict MSI status in patients with solid tumors using comprehensive genomic and immune profiling (CGIP), independent of direct sequencing data from microsatellite sites. Methods: We analyzed samples from 1,838 patients with colorectal cancer (CRC) by CGIP, which included DNA panel testing (523 genes) for pathogenic single nucleotide variants (pSNV) and determination of TMB (mut/Mb), and RNA sequencing (397 genes) for gene expression (GEX). The Boruta algorithm was used to select key genomic and GEX changes associated with MSI status. A distributed gradient boosting algorithm created a predictive model that was trained on 70% of the cohort. The model was tested on the remaining CRC cohort (Test), and in the PanCancer Atlas CRCs (TCGA), endometrial adenocarcinomas (EMCA), and CRC with indeterminate MSI testing by CGIP. Performance of the trained model was assessed using sensitivity, specificity, and positive (PPV) and negative (NPV) predictive value. Results: Feature selection identified 79 genes with pSNVs, 63 GEX changes, and TMB as informative for MSI prediction. The model showed high predictive accuracy in differentiating MSI-H tumors. Of the 39 cases that failed MSI component of CGIP (Indeterminate), 17 had MMR IHC results available, of which 1 demonstrated loss of MLH1/PMS2 and the other 16 showed intact (normal) expression. Testing of this cohort identified 3 cases as MSI-high, at least one of which was confirmed by IHC. No cases with intact MMR protein IHC were identified as MSI-high by the algorithm. Conclusions: Our ML-driven approach accurately assessed MSI status of CRC and EMCA using CGIP data. This approach also identified potential cases with MSI-high status where direct sequencing of microsatellites failed. This study highlights a method to identify patients with potential MSI-high status for orthogonal screening with the MSI component of a test fails. [Table: see text]
Efficient site selection for clinical trials using simulated annealing.
Authors: Ruchlin Ian , Govil Vibhor , Samar Deepak , Chawla Jitesh , Gurha Pyeush 
Published: June 2024

1558 Background: Identifying sites with eligible patients for multi-site oncology clinical trials is challenging given increasingly complex inclusion/exclusion criteria, limited site resources, and the need to ensure diversity in trials. This is a complex systems problem with many factors that present nonlinear behaviors. Given manifold combinations of sites, research capabilities, and study characteristics, artificial intelligence solutions may help research sites and sponsors optimize site selection to achieve enrollment, speed, and diversity of goals. We report on one of the first deployments of a multi-AI model solution to optimize site selection for an ongoing clinical trial. Methods: The clinical trials were translated into a standardized digital form expressed as rules fully encompassing the inclusion and exclusion criteria. A patient cohort was created from the criteria using the ConcertAI oncology research database, including site affiliations. The sites had numerical features specific to the protocol indication, such as physician count and projected patient count. Each site was scored by its features with the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to create higher alignment to the best matched sites. Feature weights allowed TOPSIS to emphasize desirable attributes in an interpretable way. A simulated annealing algorithm, adept with nonlinear objectives, explored site combinations, yielding a high-scoring, constraint satisfying solution. Independent simulations measured convergence and provided a distribution of solutions. Shapley analysis clarified site contributions to score and constraints. The implementation was compiled and parallelized. Results: Utilizing a Multiple Myeloma clinical trial protocol with 7 criteria, we formed a cohort of 15,881 patients across 1,246 US clinical trial sites. Each site had 12 features. The weights optimized patient diversity while enrolling at least 10 patients per month across all sites to achieve the targeted study accrual timeline. The result was a set of 3 sites enrolling 12 patients per month with 51% diversity, in agreement with the optimal solution obtained through detailed, multi-week, manual analysis. As constraints increased, and exact verification became impractical, convergence was gauged by decreasing variance in independent solutions. Conclusions: Our study introduced a robust, efficient, and interpretable approach to clinical trial site selection. It addressed the challenges posed by increasingly complicated protocols, requirements for study participant diversity, and heterogeneous site features. The multi-AI model scalability underscored its practical utility in streamlining the site selection process for clinical research networks, academic centers, industry sponsored trials, cooperative group studies, and investigator-initiated studies.
Large language models to evaluate racial discrepancies in performance status assignment.
Authors: Demirci Arda , Hong Julian C. , Zack Travis 
Published: June 2024

1561 Background: Oncologic treatment eligibility relies heavily on “Performance Status” (PS), a subjective gauge of a patient's overall health. Despite frameworks aimed at quantifying PS, these assessments remain prone to potential bias. A systematic evaluation of discrepancies in language between ECOG PS and race has yet to be conducted. Large language models (LLMs) have the ability to synthesize text and may enable an assessment of the relationship between race, note text, and physician-documented PS. We hypothesize that LLMs can quantify these relationships to understand potential inconsistencies in ECOG PS. Methods: In our single-institution cohort study, we examined patients from medical or radiation oncology clinics between January 2012 to December 2023 with documented ECOG PS. PS was extracted from clinical notes and redacted from downstream analyses. Notes were categorized by patient-reported race (Asian, Black, White, Other). 1,500 matched subjective assessments (from oncologist clinical notes) and PS were randomly sampled for each race, except for Black patients, where only 640 entries were available. We divided training and test sets using an 80-20 ratio across each race. Using the training cohort, we trained race-specific models to assign ECOG PS based on subjective assessment, using two LLMs: UCSFBERT (BERT pretrained on UCSF data) and RoBERTa. In the hold-out test sets, we applied each model across all races to assign ECOG PS based on subjective assessments. These were evaluated by micro-F1 (model accuracy) and Multiway ANOVA to compute model-specific p-values. Results: 13.6% of patients had a documented ECOG 2+, with variation across races (Asian: 16.1%, Black: 17.7%, White: 7.7%, Other: 15.3%). Models tended to assign Black patients with ECOG 0-1 with worse performance status (ECOG 2+). Other than RoBERTa models trained on Asian and White cohorts, all models trained on a specific race showed disparate classification results when applied to other races (out-of-domain), highlighting potential biases in LLM models trained where a specific race predominates (Table; ANOVA). Conclusions: This study demonstrates a novel way to use LLM to assess discordance within physician documentation and performance status assignment. It underscores the need for incorporating diverse demographic data when using LLM in medical contexts. This research has potential to dissect how bias may be propagated within physician practice and provide insight into known disparities in clinical trial enrollment and standard of care implementation.[Table: see text]