bluuebunny/crossref_metadata_2025_split huggingface dataset:
A real-world longitudinal assessment of the treatment-related symptoms and associated burden among patients receiving chemotherapy.
Authors: Trask Peter , Rusli Emelly , Holt Tracy , Kramer Mordecai , Galaznik Aaron 
Published: June 2024

e23158 Background: For patients (pts) with cancer, treatment with cytotoxic chemotherapy is associated with a variety of treatment-related symptoms including nausea, vomiting, diarrhea and constipation. Often the ability to manage these symptoms when they occur is the difference between completion of chemotherapy and early discontinuation. The first weeks of treatment are critical for understanding the pts’ acute experience with chemotherapy. The objective of this study is to longitudinally evaluate treatment-related symptoms and associated treatment burden among chemotherapy pts through a real-world electronic remote symptom monitoring system. Methods: Pts treated with chemotherapy in U.S. academic cancer centers were enrolled in Carevive’s Patient Reported Outcomes Mobile Platform (Carevive PROmpt®) between September 2020 and Dec 2023, with 3 month median follow-up. A total of 476 pts were enrolled, across early (81%) and late (19%) stage disease. Weekly PRO-CTCAE-derived surveys assessed severity, frequency, and interference for 16 symptoms often associated with cancer treatment: anxiety, constipation, cough, decreased appetite, diarrhea, fatigue, general pain, insomnia, mouth/throat sores, muscle pain, nausea, numbness and tingling, rash, sadness, shortness of breath, and vomiting. The GP5 assessed weekly overall bother with the side effects of treatment. Questionnaires were administered weekly starting at treatment initiation. Results: The mean age of chemotherapy pts was 67.3 (SD = 13.2) years, with 75% female, and 20% African American. At treatment initiation, 302 pts reported symptoms by PRO-CTCAE, and 100 completed the GP5. At week 1, constipation, general pain (GP), nausea, numbness/tingling (N/T), and shortness of breath were experienced by 20-25% of pts. Over the first 4 weeks of treatment, pts reported an increase in constipation, decreased appetite, diarrhea, and nausea at week 2, with a return to week 1 frequency over the remaining weeks. Diarrhea, however, remained elevated. While week 1 capture rates were lower, patterns for treatment bother were similar. At week 1, 34% of pts reported being bothered at least “Somewhat” by the side effects of treatment. This increased to 48% at week 2, but returned to near the week 1 frequency over the remaining weeks. Conclusions: For remote-monitored pts in this real-world data set treated with chemotherapy both alone and in combination, the most frequent symptoms at the start of treatment were nausea, GP, and N/T. The frequency of pts with GP and N/T did not change over the course of treatment suggesting their possible presence prior to the observed regimen. Increases in chemotherapy-related symptoms show the importance of ongoing assessment over the first 4 weeks of treatment, ideally with feedback to treating physicians and concomitant symptom management to optimize successful chemotherapy completion.
Preliminary validation of a novel patient-reported outcomes (PRO) questionnaire for patients with cutaneous T-cell lymphoma: The CTCL-PRO-18.
Authors: Akkad Neha , Jeffe Donna , Frank Anastasia , Al-Hammadi Noor , Watkins Marcus , Musiek Amy , Mehta-Shah Neha 
Published: June 2024

e23161 Background: Improving quality of life (QoL) is key to clinical decision-making for patients with cutaneous T-cell lymphoma (CTCL). Currently there is no measure of QoL that captures concerns of patients with advanced-stage CTCL. A thematic analysis of interviews with early- and late-stage patients with CTCL identified novel QoL domains. Using these data, we developed questionnaire items for a new QoL measure (CTCL-PRO-18). Methods: Consented patients completed our new survey and previously validated metrics: SF-36, MF/SS-CTCL QoL, VAS-Itch (past 24 hours), Skindex-29, CES-D, and PROMIS™ Sleep Disturbance. Principal component analysis (PCA) with varimax rotation reduced the number of items on our measure; Cronbach’s alpha evaluated the internal-consistency reliability of items that loaded on each component and the overall measure. We examined the construct and convergent validity of the CTCL-PRO-18 using Pearson correlations, and differences in QoL outcomes by patient demographics and disease status using analyses of variance (ANOVA). Results: 82 patients (47 male; 69 white, 11 Black, 2 Hispanic) completed the surveys. The PCA yielded an 18-item, 5-component solution: 6-item self-consciousness; 3-item burden of treatment; 3-item concern about sores; 3-item sleep/fatigue; and 3-item lack of available CTCL resources. The 5 subscales and overall CTCL-PRO-18 measure had acceptably high reliabilities; Cronbach’s alphas ranged from .74 to .93. Correlations between the CTCL-PRO-18 and each of the previously validated measures were statistically significant (P < .005). Correlations between CTCL-PRO-18 and SF-36 (subscales ranged r =-.320 to -.624), CESD ( r= .645), PROMIS™ Sleep Disturbance ( r= .4780), and VAS-Itch ( r= .665) were moderate. Correlations between CTCL-PRO-18 and MF/SS-CTCL QoL ( r= .720) and Skindex-29 ( r= .814) were high. Correlations between the overall CTCL-PRO-18 and each of its subscales were significant (P < .001) and ranged from .571 (Burden of Treatment) to .882 (Self-consciousness). ANOVAs showed no statistically significant gender or racial/ethnic (white vs. Black/Hispanic race/ethnicity) differences in the CTCL-PRO-18 or the 5 component scores. Burden of Treatment scores were significantly lower for patients with no active disease compared with patients with reduced symptoms and active disease (4.9 [SD 3.9], 6.4 [3.2], and 8.2 [3.8], respectively; P = .018). Conclusions: The CTCL-PRO-18 and its 5 subscales had high internal-consistency reliability. Correlations were higher between the CTCL-PRO-18 and other CTCL-relevant measures (Skindex-29, MF/SS-CTCL QoL) than they were between the CTCL-PRO-18 and less CTCL-specific measures, providing preliminary evidence of the construct and convergent validity of the CTCL-PRO-18. Examination of the discriminant validity of this metric is warranted.
Correlation of stress, depression, sleep, and pain with race and body mass index (BMI): Secondary analysis from Alliance A011502.
Authors: Gandhi Shipra , Ballman Karla V. , Holmes Michelle D. , Visvanathan Kala , Symington Banu , Carvan Margaret , Matyka Carol , Weiss Anna , Winer Eric P. , Razaq Wajeeha , Carey Lisa A. , Partridge Ann H. , Chen Wendy Y. 
Published: June 2024

e23171 Background: A011502 is a phase 3 trial that randomized high-risk nonmetastatic breast cancer (BC) patients (pts) to aspirin 300 mg daily vs. placebo. Obesity and Black race have been shown to be associated with worse clinical outcomes. We studied the association of stress, depression, sleep and pain with each other, and with BMI and race. Methods: At baseline, 2735, 2610, 2720, and 2422 pts completed Perceived Stress Scale (PSS), Center for Epidemiologic Studies Depression Scale Revised (CESD-R), Brief Pain Inventory (BPI), and Pittsburgh Sleep Quality Index (PSQI) respectively. Stress was categorized with PSS scores: low (0-13), moderate (14-26) or high (27-40). Depression was categorized with CESD-R scores: no depression (0-15) or depression (≥16). Pain was categorized with BPI scores: none/mild (0-3) or moderate/severe (≥4). Sleep quality was categorized with PSQI scores: good (0-5) or poor ( > 5). Associations of measures by race and BMI were analyzed using chi-square test. Results: There was a statistically significant association among stress, depression, pain, and sleep quality (p < 0.01 for all). Compared to pts with BMI < 25, overweight and obese women reported higher stress (2.9%, 2.3% vs. 1.7%, p = 0.04), depression (13.4%, 18% vs. 9.6%, p < 0.01), moderate/severe pain (16.5%, 20.3% vs. 12%, p < 0.01), and poor sleep (64.9%, 71.5% vs. 60.4%, p < 0.01). Black women reported more moderate/severe pain compared to White women or other races (32.9% vs. 14.2% vs. 27.8%, p < 0.01). There was no association of other symptoms with race (Table 1). Conclusions: Obesity was associated with higher stress, depression, poor sleep and pain among BC pts from A011502. Black women reported higher pain compared to other races. Future studies are warranted that determine causal associations, how symptoms may relate to outcome disparities, and develop approaches to address them. U10CA180821, U10CA180882, UG1CA233196; https://acknowledgments.alliancefound.org . Clinical trial information: NCT02927249 . [Table: see text]
Trends and landscape of clinical outcome assessment use to support oncology product approvals.
Authors: Dumais Kelly , Davis-Aoki Reina 
Published: June 2024

e23186 Background: The regulatory landscape in oncology trials has evolved with growing emphasis on patient-focused drug development. Further to tumor and survival outcomes, measuring how patients feel and function via clinical outcome assessments (COAs) provides critical information on symptoms and treatment burden and may help to inform risk/benefit evaluation for new treatments. This study aimed to examine trends in the use of COAs to support efficacy or safety in oncology product labeling. Methods: Product labeling information was obtained from PROLABELS (Mapi Research Trust), a database containing COA-related data sourced from the efficacy or safety sections of FDA Product labels and EMA SmPC where at least one COA is mentioned. Oncology product labels were screened based on therapeutic indications and study information. The clinical study details published in the label/SPC between 1990 and 2022 were used for further analysis. Results: A total of 180 EMA and FDA approved drugs and devices for oncology indications where COAs were mentioned in the product label were identified. 288 studies with COAs supported these drug approvals. These studies were in 52 different indications, with most in multiple myeloma (31), followed by non-small-cell lung cancer (24), prostate cancer (22), and breast cancer (22). Of the COAs that were used, 47% were patient-reported outcomes, 18% were clinician-reported outcomes, 30% were composites, and 5% were unspecified. The number of studies conducted with COAs supporting a product label increased over time, with 3x more studies in the past 10 years (189 from 2013-2022) compared to the previous 10 years (60 from 2003-2012). For studies listing specific COA names, 63 unique COAs were used. Excluding response criteria assessments, the most common COAs were the EORTC-QLQ-C30 (57 studies), EORTC-QLQ-LC13 (18 studies), and EQ-5D-3L (17 studies). The two indications with the largest variety of COAs used were prostate cancer (10 unique COAs) and non-small-cell lung cancer (9 unique COAs). Conclusions: In line with regulators highlighting the utility of COAs in evaluating clinical benefit, results show an increase in studies using COAs to support efficacy and safety in oncology product approvals. With COAs providing critical information on symptoms, side effect impact, and quality of life, including this information on labels may help patients and providers make more informed healthcare decisions, and for market differentiation.
Longitudinal race/ethnicity (R/E) specific trends in patient-reported outcomes (PRO) in elderly women with breast cancer (BC) in the SEER-MHOS database.
Authors: Dookeran Keith A. , Meyer Nicolette A. , Landeta Vidal Jennifer , Champion Kristen E. 
Published: June 2024

e23189 Background: PRO can be used to characterize health-related quality of life in cancer survivors; however, few studies have reported on longitudinal trends. Hence, we examined R/E specific (White/Black/Hispanic) longitudinal trends in physical and mental PRO for elderly women with BC. Methods: Data is from the Surveillance, Epidemiology, and End Results- Medicare Health Outcomes Survey (SEER-MHOS). The analytic sample (n = 33,386) included 25,267 White, 4,553 Black, and 3,566 Hispanic women newly diagnosed between 2010-2017 with 10 years of follow-up survey data. Generalized linear models with age/time-stratified predictive margins [means with 95% confidence intervals (CI)] were used to examine age/body mass index-adjusted physical and mental health PRO by R/E as physical and mental component summary t-scores [PCS/MCS: are linear transformation of the 0-100 possible range scoring for 8 Veterans RAND 12 Item Health Survey (VR-12) sub-scales, with mean of 50 and standard deviation (SD) of 10, normed to the US population]. Results: Overall PRO trends for all R/E groups were observed to decrease (i.e., worsen) over the 10 year period, however, trends remained highest for Whites and lowest for Hispanics [average Black/White Risk Difference (RD) (PCS: -3.2; -3.7, -2.8), (MCS: -3.4; -3.8, -2.9); average Hispanic/White RD: (PCS -3.3; -3.8, -2.8), (MCS: -5.4; -5.8, -4.9)]. Compared with White, Black and Hispanic women aged 45 years at diagnosis had significantly worse PRO at 10 years of follow-up [PCS: (White: 39.7; 38.2, 41.2), (Black: 33.7; 29.6, 37.9), (Hispanic: 31.4; 26.7, 36.1); MCS: (White: 47.1; 45.6, 48.5), (Black: 41.2; 37.2, 45.2), (Hispanic: 35.4; 30.8, 38.9)]. Similar disparity in R/E trends was also observed at older age strata (i.e., 55, 65, 75 and 85). For PCS the R/E disparity was no longer significant after 8 years follow-up for age strata 55, 65, and 75, and after 4 years for age strata 85. But for MCS the R/E disparity remained significant up to 8 years follow-up for age strata 55, 65, 75, and 85. With regards to VR-12 sub-scale examination, most of the PCS R/E disparity was likely due to differences in the general-health score; whereas for MCS disparity, it was likely due to differences in social-functioning, role-limitation, and emotional-well-being. Conclusions: Our findings show durable R/E specific disparities in longitudinal physical and mental PRO trends in elderly women with BC; this should inform strategies to optimize healthy aging for BC survivors.
Acupuncture for cancer symptoms: Clinical application and longitudinal impact.
Authors: Lasheen Wael , Walsh Declan , Yaguda Susan , Polsky Jonathan 
Published: June 2024

e23191 Background: Evidence for acupuncture (ACUP) in cancer care is inconsistent. ACUP is variously used for multiple symptoms, e.g. anxiety, chemotherapy-related peripheral neuropathy, dyspnea, hot flashes, insomnia, and pain. Approximately 500 unique patients are treated for ACUP annually in our Section of Integrative Oncology. Our objective was to evaluate ACUP’s clinical application and longitudinal impact on symptoms severity. Methods: This was a retrospective analysis of those treated in a group ACUP session. Symptoms were rated before each session on a severity numeric rating scale (0-10). Clinical improvement was defined as a 2-point decrease from baseline (T1). We report symptoms from baseline to the fourth sessions (T1-T4). Counts are presented as percentages and numbers summarized by mean (standard deviation; SD) or median (interquartile range; IQR). Groups were compared statistically as appropriate. Results: 2239 consecutive patients were treated between 2015-2022. Mean (SD) age was 57 (±12); 83% were female; 57% breast primary site, 9% hematologic, 9% gastrointestinal, 8% gynecologic, 4% lung, 3% prostate, and 9% other malignancies. Symptoms treated were pain (61%), insomnia (48%), fatigue (44%), neuropathy (41%), anxiety (37%), hot flashes (37%), and other symptoms (17%). 68% presented with multiple symptoms. They received a median (IQR) of 4 (2-6) sessions and 7 (7-14) days apart. At T1 symptom severity scores, median (IQR), were anxiety 5(2-7), fatigue 5(3-7), hot flashes 6(4-8), insomnia 6(4-8), neuropathy 5(3-8), and pain 5(3-7); Table1. By T2, improvement was observed in patients, N (%), with hot flashes 476 (66%), anxiety 434 (62%), fatigue 464 (56%), insomnia 407 (55%), neuropathy 441 (55%), and pain 563 (50%). Symptom severity improvement was statistically significant by T2 and by T3 clinically relevant; Table 1. By T4, about 60% were still on ACUP treatment. Multiple ACUP sessions ( > 1) were associated with older age and higher symptom burden, and fewer sessions with more severe T1 anxiety and fatigue. Fewer males were treated, yet symptoms improved like in females. Conclusions: Symptoms were of moderate severity at referral. In this large consecutive sample (N = 2239) of mostly younger breast cancer women, group ACUP was associated with broad based symptom benefit. Repeated ACUP treatments were important for clinical improvement. Males and females benefited equally however fewer males were referred. Several confounders need to be considered interpreting these results. [Table: see text]
Characterization of remote symptom alerts generated from patient-reported outcome (PRO) symptom monitoring in a real-world breast cancer population.
Authors: Hurtado Castillo Marisabel , Rusli Emelly , Wujcik Debra , Galaznik Aaron , Kaufmann Tara L. 
Published: June 2024

e23193 Background: Women with breast cancer experience debilitating symptoms, yet the patient-reported symptom burden among patients on treatment remains largely uncharacterized. This study characterized electronic PRO (ePRO) symptom monitoring data of patients with breast cancer in real-world settings to assess differences in remote symptom alerts by clinical characteristics. Methods: We conducted a retrospective descriptive study of complete case ePRO data reported by patients at twelve US sites using Carevive PROmPT, a remote symptom monitoring platform. Participants consent to share de-identified, aggregate ePRO symptom data and sociodemographic and clinical characteristics from the electronic health record. We characterized PRO-CTCAE symptom data reported by breast cancer patients on active cancer treatment enrolled between 9/2020 and 11/2023. The PRO platform generates an algorithm-based alert if the combination of symptom severity, frequency, and interference is moderate or severe. We used descriptive statistics to characterize symptom severity and number of alerts by baseline patient-report ECOG status, age, cancer stage (Stage I-III vs. IV), and breast cancer subtype. Results: Of 646 patients, 519 (80%) generated alerts during treatment. Population characteristics were: median age 56 years-old (range 26-84), 72% White, 20% Black/African American, 62% Stage I-III, 15% HER2 positive, 47% ER and/or PR positive, HER2 negative,18% triple negative. Patients reported a total of 19,425 symptoms over a median of 12 weeks with 80% compliance rate, of which 39% resulted in alerts. Participants generated a median of 1 alert per week. About 50% of alerts occurred in the first 12 weeks of ePRO completion, with a higher proportion in Week 1-4 across all subgroups (range, 15-38%). On average, ECOG 2 or greater patients generated more alerts per week than ECOG 0-1, (mean 2.1, (SD 1.3) vs. 1.7 (SD 1.0). Younger patients ( < 50 years) generated more alerts compared to those older than 50 years (87% vs. 77%). Patients with early-stage disease reported more alerts than the Stage IV group (81% vs. 78%). In the metastatic setting, triple negative patients reported a median of 3 (range 1-8) moderate/severe symptoms per week; the other subtypes reported a median of 1 (range 1-8). Conclusions: Women on breast cancer treatment reported their symptom experience with high compliance rates in the real-world setting. Nearly 40% of symptom reports generated an alert, with a higher proportion in the first month of treatment. Younger, early-stage, and metastatic triple negative patients with breast cancer reported a higher symptom burden compared to other groups. Future work will investigate symptom experience by sociodemographic and clinical characteristics to further identify patients who may benefit from more intensive symptom reporting and management.
Screening oncology articles in a qualitative literature review using large language models: A comparison of GPT4 versus fine-tuned open source models using expert-annotated data.
Authors: Thorlund Kristian , Lloyd-Price Lucy , Jafar Reza , Nourizade Milad , Burbridge Claire , Hudgens Stacie 
Published: June 2024

e23196 Background: Clinical Outcome Assessment (COA) conceptual gap analyses for oncology are complex and time consuming. Artificial Intelligence may efficiently reduce time to completion of such analyses. We aimed to assess two AI models’ performance for literature screening to identify relevant qualitative oncology research. We also compared accuracy and run-time for both AI models. Methods: We manually curated a dataset of title/abstract screening (n = 1,700 study references) across 17 landscape reviews. Among these, 11 landscape reviews (n = 951 study references) spanning over were in oncology, including 8 solid cancers (breast, lung, urothelial, colorectal, esophageal, head and neck, pancreatic, and stomach) and 3 non-solid cancers (lymphoma, acute myeloid leukemia, and multiple myeloma). Each citation was annotated for eligibility (Y/N) by population, study design (qualitative), and reporting of concepts (how patients feel or function). We then compared the accuracy of two AI models at predicting the screening decisions of expert researchers. The two AI models were Generative Pre-trained Transformers 4 (GPT4, OpenAI) prompts and a fine-tuned SciFive biomedical large language model (LLM). We used 70% of the data for training and 30% for test. Accuracy estimates were obtained only for the models’ ability to label eligibility within the 11 oncology datasets. Results: Both LLMs performed well for assessing relevance by oncology population, with F1-scores for the GPT4 and SciFive models being 0.92 and 0.83 respectively (precision was 0.92 and 0.93 respectively). For concept reporting the fine-tuned SciFive model outperformed GPT4 with an F1-score and precision 0.88 and 0.92 versus 0.81 and 0.79. The same was true but less pronounced for eligibility by study design, with an F1-score and precision 0.81 and 0.90 versus 0.86 and 0.76. For overall eligibility, the customized SciFive model outperformed the GPT4 model with an F1-score and precision of 0.84 and 0.92 versus 0.85 and 0.82. Lastly, it took the GPT4 prompts between 10-30 minutes to screen 100 abstracts. By contrast, the customized SciFive model took 1-2 minutes on a computer with a Quadro RTX 8000 GPU. Conclusions: In conclusion, both AI models are promising. The fine-tuned SciFive model appears slightly more accurate and performs substantially faster than the GPT4 model.