bluuebunny/crossref_metadata_2025_split huggingface dataset:
Impact of pathogenic germline variants in HRR genes on gastroesophageal cancer in a large real-world Chinese population: A comprehensive study.
Authors: Li Jian , Zhang Zhe , Zhu Liangjun , Shi Jun , Hou Ting , Su Lan , Lv Xinze , Zhang Zhou , Shen Lin 
Published: June 2024

10588 Background: Genetic testing in gastroesophageal cancer has been limited to those with hereditary cancer syndromes, restricting understanding of the impact of germline variants in sporadic cancers. Our study expands this by exploring germline and somatic characteristics in a broad Chinese cohort, enhancing risk assessment and guiding treatment strategies in a wider population. Methods: From October 2017 to April 2023, we retrospectively analyzed 2596 Chinese gastroesophageal cancer (GEC) patients, including 2003 with gastric cancer (GC), 196 with gastroesophageal junction cancer (GEJC), and 397 with esophageal cancer (EC). A 520-gene panel including 62 genes associated with cancer predisposition, was employed for genomic profiling of paired tumor and white blood cell samples. The pathogenicity of germline variants was determined according to ACMG guidelines. Results: In this retrospective cohort, the proportion of patients carrying pathogenic/likely pathogenic (P/LP) germline variants was 5.3% in GC, 5.6% in GEJC, and 5.0% in EC, respectively. Notably, genes associated with HRR exhibited the highest rates of P/LP germline mutations in GEC (94, 3.7%), with the majority occurring in non-BRCA HRR genes (77, 3.0%). The top five genes with the highest mutation rates were ATM, PALB2, FANCA, CHEK2, and RAD54L. Compared to the East Asian population without cancer in the genomAD database, a higher proportion of P/LP germline mutations were found in the CDH1(Inf [6.46-Inf], p < 0.001), PALB2 (5.60 [1.61-13.09], p = 0.002), SDHA (8.99 [1.29-99.42], p = 0.012), ATM (2.15 [1.08-4.09], p = 0.02), and CHEK2 (3.83 [1.06-13.32], p = 0.02) genes in GC. Similarly, significant increases in PALB2 (10.36 [2.32-37.28], p = 0.002) and ATM (4.42 [0.86-14.33], p = 0.036) were also observed in EC and GEJC. The HRR P/LP variant group exhibited a higher incidence of biallelic inactivation (loss of wildtype allele) compared to the B/LB variant group (24.7% vs 6.6%, p<0.001). Moreover, the similar trend was observed for the moderately/uncertainly penetrant gene ATM and FANCA (38.5% vs 0.6%, p < 0.001; 42.9% vs 9.8%, p = 0.025), which suggests that ATM and FANCA may be associated with a high risk. The HRR genes ATM(56 vs 61, p = 0.052), CDH1(33 vs 61, p = 0.003), and FANCI(48 vs 61, p = 0.041) with P/LP variants were associated with a younger age of onset. Carriers of HRR P/LP variants had less prevalent somatic TP53 variants in GC and GEC and higher HRD score compared to non-carriers (p=0.005). Conclusions: Our research delineates the germline P/LP variant landscape in Chinese GEC, revealing the significant role of HRR P/LP variants. The presence of P/LP variants in HRR genes in GEC is linked to distinct disease characteristics and progression, underscoring their importance in guiding personalized treatment approaches and risk evaluation.
Impact of boarding time on in-hospital mortality in patients with cancer presenting to the emergency department.
Authors: Brock Patricia A. , Langabeer Denise Manon , Page Valda D , Qdaisat Aiham 
Published: June 2024

11036 Background: Longer emergency department (ED) boarding times for admitted patients is associated with increased morbidity and mortality.1 While research over the years has shown an association between boarding time to mortality in a variety of patient demographics, no specific research has been explored in a cancer population. Patients with cancer visit the emergency department frequently and have a high rate of admission compared to other patients.2 This study evaluates boarding times and mortality rates in patients presenting to an oncologic ED. Methods: A retrospective analysis was performed on 183,524 ED visits located at a comprehensive cancer center between March 2016 and December 2022. Eligibility criteria included patients 19 years and older admitted to the hospital excluding direct intensive care unit admission. Boarding time was defined from the time the physician made an order to admit to the hospital to the time of ED departure. Demographics and clinical characteristics were examined using descriptive statistics. Categorical data was analyzed using chi-square test and continuous variables employed the nonparametric Mann–Whitney U test. Boarding time was grouped into quartiles. Univariate and multivariable logistic regression analyses examined clinical factors, including boarding time and in-hospital mortality. Results: The eligible number of visits supporting the study equaled 83,629. Overall median boarding time was 2.7 hours (interquartile range: 1.5–5.1 hours) and in-hospital mortality was 5.5% (n=4607). Longer boarding time was significantly associated with in-hospital mortality. Patients with boarding time ≥5.1 hours had 1.19 (95% confidence interval [95%CI]: 1.09-1.30; P<0.001) odds of in-hospital mortality when compared to the reference group (boarding time <1.5 hours) when controlling for other risk factors including comorbidities and emergency severity index (ESI) between 1 through 5. Similar results were observed for patients with boarding times of ≥1.5- <2.7 hours and ≥2.7- <5.1 hours with odds ratio of 1.12 (95%CI: 1.02-1.22; P=0.013) and 1.11 (95%CI: 1.02-1.21; P=0.020), respectively. Higher Charlson comorbidity index and levels 1 and 2 ESI were also associated with poor survival outcome (P<0.001 for both). Conclusions: This study found an association between boarding time and in-hospital mortality in cancer patients presenting to the ED. Patients with longer boarding times were likely to experience mortality during their hospital admission. High comorbidity score and high-risk ESI were also identified as risk factors for poor survival outcome. These results emphasize the need for improved strategies to expedite the admission process and optimize care for cancer patients in the ED to reduce mortality rates. Further research and interventions targeting reduction in boarding times are warranted to improve outcomes for this vulnerable population.
Geographic distribution and accessibility of clinic trials for advanced-stage pancreatic cancer in the United States: A focus on rural and minority health disparities.
Authors: Swenson Wade T. , Westergard Emily , Swenson Abigail P , Schroeder Zachary 
Published: June 2024

11048 Background: Our research delves into the geographical distribution and accessibility of clinical trials for metastatic pancreatic cancer patients in the U.S., spotlighting the challenges faced by rural communities and minority groups. Prior research indicated nearly half of these patients would have to commute over an hour to reach a trial site. [1] Our study provides a refreshed perspective on this issue, offering a more comprehensive analysis. Methods: Utilizing the ClinicalTrials.gov portal, we identified active interventional clinical trials for metastatic pancreatic cancer patients as of November 25, 2022. We obtained distinct zip codes linked to these trials and, using 2020 census data, gauged the proportion of the U.S. populace residing within specific distances from these sites. Our analysis encompassed factors like urbanity, ethnicity, and other socio-economic parameters. We also developed illustrative maps to visually represent the U.S. clinical trial accessibility landscape. Results: The majority of Americans diagnosed with metastatic pancreatic cancer have clinical trials within their reach. A significant 65.7% of the U.S. populace resides within a 30-mile radius of a relevant clinical trial site. However, a closer look reveals pronounced disparities based on urban-rural demarcations and racial lines, with American Indians facing the most pronounced accessibility challenges. Conclusions: Our data underscores the uneven geographical spread of these trials. This skewed distribution poses accessibility hurdles for specific demographics, especially those in rural areas and certain racial groups. Our findings underscore the pressing need for strategic initiatives to rectify these disparities, ensuring clinical trials are within reach for every individual, irrespective of their location or background.
The annual cost of cancer screening in the U.S.
Authors: Halpern Michael T. , Liu Benmei , Lowy Douglas R. , Gupta Samir , Croswell Jennifer M. , Doria-Rose Paul 
Published: June 2024

11051 Background: Cancer screening can decrease cancer incidence, mortality, and treatment costs. However, the annual cost of screening to the U.S. healthcare system is unknown. Methods: We used data from national healthcare surveys and standard costing sources to model the healthcare system cost of breast, cervical, colorectal, lung, and prostate cancer screening in the U.S. in 2021. Models projected the number of individuals in the U.S. eligible for each screening test based on current guidelines/recommendations; the number of eligible individuals screened by insurance status; and the costs associated with screening (in 2021 US dollars). Multiple sensitivity analyses were performed to examine the effects of changing model population parameters and costs on projected outcomes. Results: Total 2021 cancer screening costs to the U.S. healthcare system were estimated to be $57 billion; approximately 88% of the costs were attributable to private insurance, 9% to Medicare, and 3% to Medicaid, other government programs, and uninsured individuals. Most screened individuals had private insurance except for lung cancer screening, where a majority of individuals screened had Medicare coverage. Individuals with private insurance accounted for the majority of costs for each screening modality. Screening for colorectal cancer represented approximately two-thirds of the total cost; screening colonoscopy was approximately 60% of the total cost of cancer screening. Breast and cervical cancer screening represented the second and third largest screening costs, 15.4% and 14.7% (respectively) of total screening costs. Facility costs (amounts paid to facilities where testing occurred) were generally larger components of the total estimated costs of screening than were physician costs. Cost estimates were robust to a range of variations in eligible populations and screening costs. Conclusions: The $57 billion estimated annual cost for cancer screening in the U.S. in 2021 is similar to the estimated annual cost of cancer treatment in the U.S. in the first 12 months following diagnosis. Screening may have been suboptimal in 2021, with higher screening rates and costs in subsequent years. Identification of cancer screening costs and their drivers is critical to help inform policy and develop programmatic priorities. The model can be used to estimate the increased costs of enhanced access to recommended cancer screening services among underscreened populations, which would provide population-wide benefits.
Opioid prescribing trends and pain scores among adult patients with cancer in a large health system.
Authors: Baum Laura Van Metre , Soulos Pamela R. , KC Madhav , Jeffery Molly , Ruddy Kathryn Jean , Lerro Catherine C. , Lee Hana , Graham David J. , Liberatore Mark , Rivera Donna , Leapman Michael , Jairam Vikram , Dinan Michaela Ann , Gross Cary Philip , Park Henry Soo-Min 
Published: June 2024

11059 Background: Some opioid stewardship policies could negatively affect patients with cancer pain. However, patients with cancer are also at risk for opioid related harms, especially with persistent use. We assessed opioid prescribing trends in the context of cancer treatment and patient reported pain in a large health system in Connecticut. Methods: We conducted a retrospective cohort study of opioid-naïve adult patients with solid tumor malignancies diagnosed from 2016 through 2020 within the Yale New Haven Health System. We identified new (≥1 opioid prescription in 0-6 months following diagnosis) and additional (0-6 and 6-9 months) opioid prescriptions. The analysis evaluated all eligible patients as well as two clinical cohorts: patients treated surgically and patients with metastatic cancer. For patients with metastatic cancer and a documented pain score in the electronic health record flowsheet data, we further stratified by any (any score≥1) or no (all scores=0) pain in the 6 months since diagnosis and prior to first opioid, if given. For these patients, we noted therapeutic class of the first opioid. We used a logistic model adjusted for patient demographics to calculate predicted probability of opioid prescription and change over time. Results: A total of 10,868 patients met study criteria. Overall, we observed a decline in new opioid prescribing from 69.0% to 62.7% (p<.001) (Table). Additional opioid use also declined from 23.4% to 20.2% (p=.02). In the surgery cohort, new opioid prescribing fell from 95.7% to 88.2% (p<.001), while additional opioid use was stable over time (approximately 12%). For patients with metastatic cancer with any documented pain, new opioid prescribing was stable over time (approximately 56%). For those with documented pain scores=0, new opioid prescribing declined from 59.7% to 34.2% (p<.001). In these patients, the class listed on the first opioid prescription was analgesia in 86.6% and cough in 13.4%. Conclusions: Overall, and in patients treated with oncological surgeries, our study in a single large US health system suggests a modest, gradual decline in opioid prescribing for patients with cancer over time. On the other hand, among patients with metastatic cancer, opioid prescribing remained stable for those with documented pain and declined steeply for those without documented pain. [Table: see text]
Risk prediction model for acute care use among patients with advanced cancer on clinical trials.
Authors: Hershman Dawn L. , Till Cathee , Vaidya Riha , LeBlanc Michael Leo , Unger Joseph M. 
Published: June 2024

11074 Background: Reducing acute care use is an important strategy for improving value. Patients with advanced cancer are at high risk for unplanned Emergency Room visits (ER_visit) and hospital stays (HS). Identifying patients at highest risk could inform personalized risk-based intervention strategies and resource allocation. Methods: The data were from the SWOG Cancer Research Network. We linked data from trials in advanced cancer to Medicare claims data. The primary endpoint was the occurrence of any HS or ER_visit within 1 year after enrollment. Patients were required to have had continuous Medicare parts A and B claims from registration to 12 months after registration or death to detect all utilization outcomes. We examined sociodemographic (age, sex, race, ethnicity, insurance status), geographic (rural or urban, area-level deprivation), clinical (prognostic risk, performance status), treatment characteristics (line of therapy, therapy type), and individual comorbidity factors to establish a predictive risk model. Twenty-six factors were evaluated. A training/test approach was used. First, a 60% random sample training set of patients was generated. Best subset selection was used to identify candidate variables that minimized mean squared error using 5-fold cross validation repeated 10 times. Candidate variables were examined in multivariable logistic regression, with factors with p<.05 retained. A risk model was built by summing adverse factors and creating high vs low- risk groups by splitting at the median and into quartiles. The derived model is reported. Results: Among N=1397 total patients from 6 trials (lung, 3; prostate, 2; pancreas, 1), 839 (60%) comprised the training set. In these, 32.9% were 75+ years, 21.3% were female, and 7.7% were Black. The overall proportion of patients with >1 hospitalization/ER visit was 67.5%. In the training set, adverse risk factors were first line treatment (vs. subsequent), high (vs. low) prognostic risk, coronary artery disease (yes vs. no), hypertension (yes vs. no), and liver disease (yes vs. no). Patients with >2 factors (high risk; n=487, 58.0%) vs. 0/1 risk factor (low risk; n=352, 42.0%), were more likely to experience hospitalization/ER visit (80.3% vs. 49.7%, p<.001), corresponding to a >4-fold increase in risk (OR=4.12, 95% CI, 3.03-5.59, p<.001). Quartile-level proportions were 38.8%, 55.4%, 76.8%, and 84.0%, respectively, with an eightfold increased risk for those in the highest vs. lowest quartiles (Q4 vs. Q1, OR=8.25, 95% CI, 4.98-13.65, p<.001). Validation of the derived risk model in an independent set of 558 patients (40%) is planned. Conclusions: A limited set of 5 comorbid conditions, clinical and treatment variables predicted a 4-fold increased risk of HS and ER_visits in cancer patients with advanced disease. Personalized targeted interventions aimed at preventing acute care use could decrease the cost and improve the quality of cancer care.
Associations of symptom burden with patient-reported outcomes (PROs) and clinical outcomes among early-phase cancer clinical trial (EP-CT) participants.
Authors: Lam Anh B. , Lundquist Debra , Pelletier Andrea , Durbin Sienna , Petrillo Laura A , Jimenez Rachel , Turbini Victoria , Bame Viola , Lynch Kaitlyn , Yalala Vaishnavi Reddy , Ollila Nicholas , Malowitz Benjamin , McIntyre Casandra , Juric Dejan , Nipp Ryan David 
Published: June 2024

11101 Background: Individuals with cancer often endure a substantial physical and psychological symptom burden. However, little is known about the baseline symptom burden of patients with cancer participating in EP-CTs or the associations of this symptom burden with other PROs and clinical outcomes. Methods: We prospectively enrolled adults with cancer participating in EP-CTs at Massachusetts General Hospital from 4/2021-1/2023. Participants completed baseline surveys prior to EP-CT initiation that assessed symptoms (Edmonton Symptom Assessment System [ESAS]), quality of life (QOL; Functional Assessment of Cancer Therapy-General), hope (Herth Hope Index), depression/anxiety symptoms (Patient Health Questionnaire-4 [PHQ-4]), and financial wellbeing (COST tool, higher scores indicate greater financial wellbeing). We used regression models to explore associations of baseline symptom burden with other PROs (QOL, hope, depression/anxiety, financial wellbeing) and clinical outcomes (time on trial, hospitalizations, overall survival). Results: Among 205 participants (median age=63.3 [range: 31.8-88.6], 57.1% female), the most common cancer types were gastrointestinal (34.6%), breast (20.0%), and head and neck (10.2%). Based on the ESAS, approximately half of participants reported moderate/severe fatigue (50.2%) and poor wellbeing (49.5%). Under half reported moderate/severe drowsiness (35.7%), pain (25.9%), lack of appetite (25.4%), shortness of breath (20.0%), anxiety (18.0%), depression (15.4%), and nausea (9.2%). Higher baseline ESAS total, physical, and psychological scores were associated with worse QOL, lower hope, greater PHQ-4 depression/anxiety symptoms, and worse financial wellbeing (see Table). Higher baseline ESAS scores were associated with decreased time on trial and worse overall survival. Conclusions: In this study of EP-CT participants, we found associations of baseline symptom burden with other important PROs and clinical outcomes. Specifically, higher baseline symptom burden was associated with decreased QOL, lower hope, increased depression/anxiety symptoms, and diminished financial wellbeing as well as greater risk for shorter time on trial and worse survival. Interventions seeking to enhance care delivery and outcomes for EP-CT participants should strive to address the symptom burden of this population. [Table: see text]
Estimating adult cancer cachexia prevalence and impact on survival in the US: Real-world data analysis.
Authors: Roeland Eric , Crawford Jeffrey , Dunne Richard Francis , Jacobs Ira Allen , Groarke John , Rossulek Michelle I , Cater Nilo B. , Bonomi Philip D. 
Published: June 2024

11141 Background: Cancer cachexia is defined by weight loss that nutritional support cannot fully reverse, leading to worse pt outcomes. Cachexia prevalence across cancer types relies on outdated studies. This study assesses the frequency of cachexia diagnosis using ICD9/10 codes or observed weight loss consistent with real-world cachexia diagnosis and its impact on survival. Methods: Retrospective, observational, real-world analysis using deidentified adult pt data from Clinformatics Data Mart database (Aug 1, 2016-Jul 31, 2021). Pts with at least 1 of 23 cancer types were identified using diagnostic and procedure codes and classified in 1 of 3 cachexia categories: (1) diagnosed (diagnostic code identified); (2) observed (BMI decrease: >10% within 12 mo, >5% within 6 mo, or >2% with a BMI <20 kg/m2), (3) none (with no cachexia diagnostic code/observed). For observed, pts required ≥10 BMI records. Summary statistics presented as mean, median, or n (%). Survival rates compared using Cox proportional hazards model. Results: 672,665 pts were identified; 278,923 met inclusion criteria. Diagnosed + observed (subgroup) cachexia rates are shown for the 5 most prevalent cancer types (Table). Over 5 y, diagnosed cachexia rates ranged from 0.9-6.3%; observed from 60-83% (in subgroup with ≥10 BMI records). After cancer diagnosis, observed cachexia was identified on average 0-11 mo, while formal diagnosis occurred on average 11-24 mo. At least 16% of diagnosed and 31% observed pts received diagnosis or met observed criteria at or before cancer diagnosis. Average percent BMI decrease over 5 y was 22.0% ± 12.1, 17.8% ± 9.5, and 6.8% ± 5.5 for diagnosed, observed, and none, respectively. Compared with no cachexia, diagnosed and observed showed worse survival (HR 10.72 [95% CI 10.56-10.88]; HR 2.35 [95% CI 2.32-2.38]), respectively. Conclusions: Across all cancer subtypes, prevalence of observed cachexia cases documented by weight loss far exceeded cachexia prevalence based on diagnostic coding. After cancer diagnosis, pts met diagnostic criteria of cachexia within 1 y, but formal diagnosis took up to 2 y. The presence of cancer cachexia (diagnosed or observed), with or without metastatic disease, was associated with lower survival.[Table: see text]