bluuebunny/crossref_metadata_2025_split huggingface dataset:
Associations between preexisting nociplastic pain and early discontinuation of aromatase inhibitor therapy in breast cancer.
Authors: Joyce Elizabeth , Wang Sidi , Motamed Mehrdad , Kidwell Kelley M. , Henry Norah Lynn 
Published: June 2021

12068 Background: Aromatase inhibitors (AI) are recommended for at least five years to reduce risk of breast cancer recurrence in postmenopausal women with hormone receptor-positive disease. Despite the potential benefit, up to 50% of patients discontinue AI early, primarily because of musculoskeletal side effects. Some patients, including those with fibromyalgia and other chronic pain conditions, have preexisting pain that arises from altered nociception without clear evidence of tissue or peripheral nerve damage, called nociplastic or centralized pain. We hypothesized that preexisting nociplastic pain is associated with early discontinuation of AI therapy due to toxicity. Methods: Patients diagnosed with breast cancer between 2012-19 and who enrolled in the Michigan Genomics Initiative (MGI) were identified. Patients who were female, had hormone receptor-positive breast cancer, had not received neoadjuvant therapy, and who initiated adjuvant AI therapy were included in the analysis. Prior to breast cancer surgery patients completed surveys about pain in the affected breast and overall worst pain (Brief Pain Inventory [BPI]), nociplastic pain (2011 Fibromyalgia Survey [FS], which assesses widespread pain plus somatic symptom severity), and anxiety (PROMIS Anxiety 4a v.1). Demographics, cancer characteristics, and treatment history, including date and reason for discontinuation of AI therapy, were abstracted from the medical record. Patients were censored at date of last oncology follow-up. Univariate and multivariable analyses were conducted to assess relationship between age, BMI, surgical site pain, overall worst pain, FS score, anxiety, and time to discontinuation of initial AI medication. Results: Of 207 analyzed patients, the average age was 61 years, average BMI was 30.3 kg/m2, 181 (87%) were postmenopausal at the time of breast cancer diagnosis, and 31% received chemotherapy. The majority were prescribed anastrozole (n=196); 6 took letrozole and 5 took exemestane. Average scores prior to surgery were 1.5/10 for worst pain, 0.2/10 for surgical site pain, 4.2/31 for FS score, and 54.1/100 for anxiety. On univariate analysis, only use of exemestane (HR 5.55, p<.001) and use of letrozole (HR 3.11, p<.001) were associated with shorter time to ET discontinuation; age, BMI, surgical site pain, worst pain, FS score, and anxiety were not statistically significantly associated with time to AI discontinuation. On multivariable analysis, only higher FS score was statistically significantly associated with time to AI discontinuation (HR 1.11, p=.021). Conclusions: Greater pre-existing nociplastic pain was associated with early discontinuation of AI therapy. For these patients, early interventions that target centralized pain, or treatment with tamoxifen instead of AI therapy, may be appropriate in order to reduce risk of nonpersistence with AI therapy.
Marijuana use among US adults with cancer: Findings from the 2018-2019 Behavioral Risk Factor Surveillance System.
Authors: Lee Min Jee , Salloum Ramzi , Sharma Arun 
Published: June 2021

12088 Background: Cancer survivors experiencing adverse effects from their cancer and treatment report decreased symptom burden with marijuana use. An increasing number of U.S. states have legalized marijuana use for both medical and recreational purposes. This study aimed to assess the prevalence of current marijuana use and to identify the factors associated with its use among US adults with cancer living in 17 U.S. states and territories. Methods: Data from the 2018-2019 Behavioral Risk Factor Surveillance System Marijuana Use module were analyzed. In 2018, 13 states (California, Florida, Idaho, Maryland, Minnesota, Montana, New Hampshire, North Dakota, Ohio, South Carolina, Tennessee, West Virginia, and Wyoming) and 2 territories (Guam and Puerto Rico) participated in the optional marijuana use module. In 2019, 12 states (California, Idaho, Illinois, Maryland, Minnesota, New Hampshire, North Dakota, South Carolina, Tennessee, Utah, West Virginia, and Wyoming) and 1 territory (Guam) participated in the optional marijuana use module. The analytic sample included 13,174 adults with cancer. The analysis was weighted to account for BRFSS’s complex survey design. The primary outcome was current marijuana use (in the past 30-days). Multivariable logistic regression was used to identify demographic, socioeconomic, clinical, and behavioral factors associated with marijuana use among US adults with cancer. Results: Overall, 9.2% of adult cancer survivors (n = 13,174; weighted 5.7 million; 37.9% men) reported marijuana recently current use, 51.3% of whom used it for medical reasons only, with 65.2% reporting smoking as the main method of administration. Adult cancer survivors were significantly more like to use marijuana if they were younger (odds ratio [OR] for 55-64 versus 18-44 years old: 0.60; 95% CI: 0.38-0.93; P < 0.01); male (OR for female versus male: 0.65; 95% CI: 0.48-0.87; P < 0.01); non-Hispanic Black race/ethnicity (OR: 2.00; 95% CI: 1.21-3.33; P < 0.01); having depression (OR: 1.58; 95% CI: 1.17-2.14; P < 0.01) and current (OR: 3.23; 95% CI: 2.20-4.74; P < 0.01) or former tobacco smoker (OR: 2.40; 95% CI: 1.70-3.38; P < 0.01) and binge drinker (OR: 2.25; 95% CI: 1.53-3.29; P < 0.01). Conclusions: Among a large cohort of US adults with cancer, marijuana use was commonly reported and certain subgroups were at higher risk for marijuana use. Health professionals should identify the risk factors for elevated marijuana use, especially as more states legalize medical and recreational marijuana use despite uncertain health risks.
A 10-hour time-restricted eating intervention to address cancer-related fatigue among cancer survivors.
Authors: Kleckner Amber , Reschke Jennifer E , Altman Brian James , Belcher Elizabeth , Dunne Richard Francis , Fleming Fergal J. , Gilmore Nikesha , Jensen-Battaglia Marielle , Kleckner Ian , Lin Po-Ju , Mustian Karen Michelle , Peppone Luke Joseph 
Published: June 2021

12109 Background: Cancer-related fatigue is a common, debilitating condition that can persist for months or years after cancer treatment. Time-restricted eating has been shown to improve circadian rhythm and strengthen rest and activity patterns, and therefore could help reduce persistent fatigue. Herein, we evaluated the feasibility of recruiting cancer survivors to a two-week, single-arm, time-restricted eating intervention with a 10-h eating window, assessed safety of the intervention, monitored adherence, and obtained initial estimates of within-group change in patient-reported fatigue. Methods: We recruited adults 4-60 months post-cancer treatment who had a fatigue level ≥3 on a scale from 0-10 and who did not already consume food within a 10-h window. Participants were asked to consume all food and beverages within a self-selected 10-h eating window for 14 days; water was allowed at all times. Participants completed a daily diary indicating when they began and stopped eating each day. To assess fatigue, participants completed the Functional Assessment of Chronic Illness Therapy-Fatigue (FACIT-F) and the Brief Fatigue Inventory (BFI) at pre- and post-intervention. We report mean±standard deviation and used a t-test to assess differences in pre- vs. post-intervention scores. Results: A total of 21 participants consented (20 breast cancer, 1 prostate cancer; 20 female; age 57.7±11.4 years; 1.6±1.1 years post-primary treatment). The study was feasible: 19/21 (90.5%) completed pre- and post-intervention assessments and daily diaries. It was also safe: there were two mild adverse events—one unlikely (insomnia) and one possibly related to the intervention (acute headache) and no severe adverse events. Most of the participants adhered to the intervention; 14/19 (73.7%) ate all of their food within a 10-h window at least 80% of the days, and 16/19 (84.2%) reported an average eating window ≤10 h. Fatigue scores improved a clinically meaningful degree for FACIT-F total score, FACIT-F fatigue subscale, and the BFI total score from pre- to post-intervention per established cutoffs (Table). Conclusions: Cancer survivors were willing and able to adhere to a two-week time-restricted eating intervention, and the intervention was safe. Also, fatigue was reduced with moderate to large effect sizes after two weeks of time-restricted eating. Based on our results, a follow-up randomized controlled trial to investigate time-restricted eating to alleviate cancer-related fatigue among cancer survivors is indicated. Funding: NIH/NCI UG1CA189961, T32CA102618. Clinical trial information: NCT04243512. [Table: see text]
Improving cancer pain management in the emergency department: An EMR-based solution.
Authors: Coyne Christopher John , Kettler Ellen , Dong Kelly , Killeen James 
Published: June 2021

12110 Background: Pain is common reason for patients with cancer to seek care in the emergency department (ED). Unfortunately, these patients frequently receive inadequate doses of pain medication, partially due to opioid reduction efforts in the ED, as well as opioid tolerance among those with chronic cancer pain. The purpose of this study was to investigate the effectiveness of an electronic medical record (EMR) based best practice advisory (BPA) at improving analgesic dosing for cancer patients in the ED. Methods: We performed a retrospective cohort study on cancer pain at two academic medical centers from 05/18/20 to 10/27/20. The BPA algorithm identified ED patients with cancer that were taking prescription opioids with a morphine equivalent daily dose (MEDD) of at least 100, as calculated by the EMR. If the ED provider ordered opioids for these patients, a BPA alert appeared with a recommended opioid dose based on the patient’s individual MEDD. This alert also included pre-set safety orders for O2 and end tidal CO2 monitoring as well as naloxone. We compared outcomes based on whether an ED provider accepted or cancelled the BPA recommendation. These outcomes included the change in opioid dose and ED disposition. Continuous variables were compared using the students t-test, while categorical variables were compared with the chi-squared test with an alpha of 0.05. Results: Our BPA identified 92 patients that met our criteria, representing 143 BPA alerts. The mean age was 52, 43.5% were female, 54.3% had metastatic disease, and 56.5% presented with a painful chief complaint. Of the ED providers that accepted the BPA, 57.5% increased their dose of opioid medication. BPA usage led to a 33.3% mean increase in medication dosage (p <.001). Patients that presented with a painful chief complaint, whose providers utilized the BPA were admitted at a rate of 60.5%, verses a 77.8% admission rate among those whose providers did not utilize the BPA (p <.01). No patients required an opioid reversal agent. Conclusions: Among cancer patients on chronic opioids presenting to the ED, use of an EMR-based BPA led to more appropriate opioid dosing without the need for opioid reversal agents, and was associated with an overall decrease in hospital admissions.
CORONET; COVID-19 in Oncology evaluatiON Tool: Use of machine learning to inform management of COVID-19 in patients with cancer.
Authors: Lee Rebecca , Wysocki Oskar , Zhou Cong , Calles Antonio , Eastlake Leonie , Ganatra Sarju , Harrison Michelle , Horsley Laura , Huddar Prerana , Khan Khurum , Mckenzie Hayley , Palmieri Carlo , Rogado Revuelta Jacobo , Thomas Anne , Wilson Caroline , Cooksley Tim , Dive Caroline , Freitas Andre , Armstrong Anne Caroline ,   CORONET Consortium
Published: June 2021

1505 Background: Patients (pts) with cancer are at increased risk of severe COVID-19 infection and death. Due to COVID-19 outcome heterogeneity, accurate assessment of pts is crucial. Early identification of pts who are likely to deteriorate allows timely discussions regarding escalation of care. Likewise, safe home management will reduce risk of nosocomial infection. To aid clinical decision-making, we developed a model to help determine which pts should be admitted vs. managed as an outpatient and which pts are likely to have severe COVID-19. Methods: Pts with active solid or haematological cancer presenting with symptoms/asymptomatic and testing positive for SARS-CoV-2 in Europe and USA were identified following institutional board approval. Clinical and laboratory data were extracted from pt records. Clinical outcome measures were discharge within 24 hours, requirement for oxygen at any stage during admission and death. Random Forest (RF) algorithm was used for model derivation as it compared favourably vs. lasso regression. Relevant clinical features were identified using recursive feature elimination based on SHAP. Internal validation (bootstrapping) with multiple imputations for missing data (maximum ≤2) were used for performance evaluation. Cost function determined cut-offs were defined for admission/death. The final CORONET model was trained on the entire cohort. Results: Model derivation set comprised 672 pts (393 male, 279 female, median age 71). 83% had solid cancers, 17% haematological. Predictive features were selected based on clinical relevance and data availability, supported by recursive feature elimination based on SHAP. RF model using haematological cancer, solid cancer stage, no of comorbidities, National Early Warning Score 2 (NEWS2), neutrophil:lymphocyte ratio, platelets, CRP and albumin achieved AUROC for admission 0.79 (+/-0.03) and death 0.75 (+/-0.02). RF explanation using SHAP revealed NEWS2 and C-reactive protein as the most important features predicting COVID-19 severity. In the entire cohort, CORONET recommended admission of 96% of patients requiring oxygen and 99% of patients who died. We then built a decision support tool using the model, which aids clinical decisions by presenting model predictions and explaining key contributing features. Conclusions: We have developed a model and tool available at https://coronet.manchester.ac.uk/ to predict which pts with cancer and COVID-19 require hospital admission and are likely to have a severe disease course. CORONET is being continuously refined and validated over time.
Association of the COVID-19 pandemic with patterns of cancer services.
Authors: Yabroff Robin Robin , Wu Xiao-Cheng , Negoita Serban , Stevens Jennifer , Coyle Linda , Zhao Jingxuan , Mumphrey Brent , Jemal Ahmedin , Ward Kevin C. 
Published: June 2021

1514 Background: The COVID-19 pandemic led to delays in medical care in the United States. We examined changes in patterns of cancer diagnosis and surgical treatment in 2020 using real-time electronic pathology report data from population-based SEER cancer registries in Georgia and Louisiana. Methods: Bi-weekly numbers, distributions, and patterns of pathology reports were compared between January 1st and December 31st in 2020 and the same period in 2019 by age group and cancer site. Results: During 2020, there were 29,905 fewer pathology reports than in 2019, representing a 10.2% decline. Absolute declines were greatest among adults aged ≥50 years (N=23,065); percentage declines were greatest among children and young adults ≤18 years (38.3%). By cancer site, percentage declines were greatest for lung cancer (17.4%), followed by colorectal (12.0%), breast (9.0%) and prostate (5.8%) cancers. Biweekly reports were statistically significantly lower in 2020 than in 2019 from late March through the end of December in most biweekly periods. The nadir was the month of April 2020 – the number of reports was at least 40% lower than in April 2019. The number of reports in 2020 compared with 2019 also declined sharply in early November (26.8%) and late December (32.0%). Numbers of reports in 2020 never consistently exceeded those in 2019 after the first decline. Patterns were similar by cancer site, with variation in magnitude and duration of declines. Conclusions: Significant declines in cancer pathology reports from population-based registries during 2020 suggest substantial delays in screening, evaluation of signs and symptoms, diagnosis, and treatment services for cancers with effective screening tests as well as in cancer sites and age groups without effective screening tests as an indirect result of the COVID-19 pandemic. Ongoing evaluation will be critical for informing public health efforts to minimize any lasting adverse effects of the pandemic on cancer screening, diagnosis, treatment, and survival.[Table: see text]
The influence of race on financial toxicity among cancer patients.
Authors: Panzone John , Welch Christopher , Pinkhasov Ruben , Jacob Joseph M , Shapiro Oleg , Basnet Alina , Bratslavsky Gennady , Goldberg Hanan 
Published: June 2021

1525 Background: Studies show that cancer patients and survivors are likely to endure financial toxicity long after being diagnosed. Methods: To examine the influence of race on financial toxicity among individuals with a history of cancer, a US based cross sectional study was conducted using data on 1,328 cancer patients collected from the Health Information National Trends Survey. Multivariable logistic regression analyses were used to analyze the relationship between race and financial toxicity, adjusting for known confounders. Results: Blacks, Hispanics and other races were shown to have a lower rate of insurance compared to Whites. Whites were also more likely to receive cancer treatment than other races (6.1% received no treatment vs 15.0% of Blacks, 17.8% of Hispanics, and 9.7% of other races, p<0.001). Considerably more Whites underwent surgical treatment of their cancer (77%) vs. 60% of Blacks, 55% of Hispanics and 74.2% of other races, p<0.001. Blacks were found to be over 5 times more likely to be denied insurance (OR 5.003, 95% CI 2.451-10.213, p<0.001) and more than twice as likely to be hurt financially than Whites (OR 2.448, 95% CI 1.520-3.941, p<0.001). Other racial minorities were also more than twice as likely to be hurt financially than Whites (OR 2.421, 95% CI 1.248-4.698, p=0.009) (Table). Conclusions: These data suggest that race is significantly associated with increased rates of being hurt financially and being denied insurance due to cancer. Awareness of race inequality should be raised so that equal cancer treatment can be provided, irrespective of race, gender or socioeconomic status.[Table: see text]
Impact of a deep learning model to reduce variation and costs of federally mandated breast density legislation.
Authors: Cavallo Hom Katherine , Dontchos Brian Nicholas , Mercaldo Sarah , Dang Pragya , Lamb Leslie , Lehman Constance Dobbins 
Published: June 2021

1550 Background: Dense breast tissue is an independent risk factor for malignancy and can mask cancers on mammography. Yet, radiologist-assessed mammographic breast density is subjective and varies widely between and within radiologists. Our deep learning (DL) model was implemented into routine clinical practice at an academic breast imaging center and was externally validated at a separate community practice, with both sites demonstrating high clinical acceptance of the model’s density predictions. The aim of this study is to demonstrate the influence our DL model has on prospective radiologist density assessments in routine clinical practice. Methods: This IRB-approved, HIPAA-compliant retrospective study identified consecutive screening mammograms without exclusion performed across three clinical sites, over two time periods: pre-DL model implementation (January 1, 2017 through September 30, 2017) and post-DL model implementation (January 1, 2019 through September 30, 2019). Clinical sites were as follows: Site A (the academic practice where the DL model was developed and was implemented in late 2017); Site B (an affiliated community practice which implemented the DL model in late 2017 and was used for external validation); and Site C (an affiliated community practice which was never exposed to the DL model). Patient demographics and radiologist-assessed mammographic breast densities were compared over time and across sites. Patient characteristics were evaluated using Wilcoxon test and Pearson’s chi-squared test. Multivariable logistic regression models evaluated the odds of a dense breast classification as a function of time period (pre-DL vs post-DL), race (White vs non-White) and site. Results: A total of 85,865 consecutive screening mammograms across the three clinical sites were identified. After controlling for age and race, adjusted odds ratios (aOR) of a mammogram being classified as dense at Site C compared to Site B before the DL model was implemented was 2.01 (95% CI 1.873, 2.157, p<0.001). This increased to 2.827 (95% CI 2.636, 3.032, p< 0.001) after DL implementation. The aOR of a mammogram being classified as dense at Site A after implementation compared to before implementation was 0.924 (95% CI 0.885, 0.964, p<0.001). Conclusions: Our findings suggest implementation of the DL model influences radiologist’s prospective density assessments in routine clinical practice by reducing the odds of a screening exam being categorized as dense. As a result, clinical use of our model could reduce downstream costs of supplemental screening tests and limit unnecessary high-risk clinic evaluations.[Table: see text]
Disparities in government and nonprofit organization funding may hinder clinical trial development for underfunded cancers.
Authors: Kamath Suneel Deepak 
Published: June 2021

1573 Background: National Cancer Institute (NCI) and nonprofit organization (NPO) funding is critical for research and advocacy, but may not be equitable across cancers. This could negatively impact clinical trial development for underfunded cancers. Methods: This study evaluated funding from the NCI and NPOs with > $5 million in annual revenue supporting leukemia, lymphoma, melanoma, lung, breast, colorectal, pancreatic, hepatobiliary, prostate, ovarian, cervical and endometrial cancers from 2015-2018 based on publically available reports and tax records. The primary objectives were to assess for disparities in NCI and NPO funding across different cancers compared to their median incidence and mortality from 2015-2018, and to determine if underfunding correlates with fewer clinical trials found in clinicaltrials.gov. Correlations between combined NCI and NPO funding for each cancer and its incidence, mortality and number of clinical trials were evaluated using descriptive statistics and Pearson correlation coefficients. Results: Diseases with the largest combined NCI+NPO funding were breast ($3.75 billion), leukemia ($1.99 billion) and lung cancer ($1.56 billion). Those with the least funding were endometrial ($94 million), cervical ($292 million), and hepatobiliary cancers ($348 million). These data are summarized in the Table. Disease-specific NCI+NPO funding correlated well with incidence, but less so with mortality (Pearson correlation coefficients: 0.74 and 0.63, respectively). Disease-specific NPO funding correlated moderately well with incidence, but was poorly correlated with mortality (Pearson correlation coefficients: 0.54 and 0.39, respectively). Breast cancer, leukemia and lymphoma were consistently well-funded compared to their incidence and mortality, while colorectal, lung, hepatobiliary and uterine cancers were consistently underfunded. The amount of NCI funding, NPO funding and combined NCI+NPO funding for a particular cancer each correlated strongly with the number of clinical trials for that disease (Pearson correlation coefficients: 0.88, 0.87 and 0.91, respectively). Conclusions: Many cancers with high incidence and mortality are underfunded. Cancers with higher mortality rates receive less funding, particularly from NPOs. Underfunding strongly correlates with fewer clinical trials, which could impede future advances in underfunded cancers.[Table: see text]