bluuebunny/crossref_metadata_2025_split huggingface dataset:
Effect of mail-in sperm cryopreservation solution on access to cryopreservation: A national demographic analysis.
Authors: Passarelli Rachel , Kenfield Stacey A , Nolte Daniel , Belarmino Andre , Civello Daniel , Smith James F , Velez Leitner Danielle 
Published: May 2024

e13524 Background: There are many circumstances under which someone may choose to bank their sperm, but forthcoming cancer treatment is among the most common. The advent of services offering mail-in semen analysis and cryopreservation has expanded access to sperm banking, especially for those living in rural areas, far away from traditional andrology labs. This study summarized demographic characteristics and reasons for cryopreservation among those using one such US-based mail-in service, with a focus on patients banking sperm prior to cancer treatment. Methods: Patients >18 years who banked sperm using a mail-in cryopreservation solution developed by Fellow Health, Inc. from March 2021 to January 2024 were included. Test kits sold for research purposes were excluded. To categorize each patient by reason for cryopreservation, we made use of a patient-provided reason, supplied during test kit registration, where available. For the remaining patients, reason was inferred using several data sources including medical history, medications, and clinic and physician information. Patients who could not be reliably classified were omitted from this portion of the analysis. Descriptive statistics of key demographics and reason for cryopreservation were reported under an IRB-approved protocol. Results: 2,154 patients met our inclusion criteria and 725 (33.7%) provided demographic information. Average age was 34.7 (SD=9.2) years and 11.9% identified as a trans female. The majority of the population identified as white (79.5%) and non-Hispanic (84.8%). Over half of the population had a college degree or greater (59.3%), was married/partnered (57.1%), and had private insurance (84.9%). Among the 756 patients whose reason for cryopreservation was discernible, the majority, 450 (59.5%), were doing so in the context of fertility treatment, with only 46 (6.1%) indicating cancer treatment. Other reasons included pre-vasectomy storage (n=137, 18.4%) and gender affirming hormone therapy (n=90, 11.9%). Of the patients who indicated cancer treatment as their reason for cryopreservation, 50.0% lived greater than a 25-mile drive of the nearest National Cancer Institute (NCI)-designated Cancer Center. Conclusions: Surprisingly, few patients utilizing a mail-in service for cryopreservation did so prior to beginning cancer treatment, with most originating from the fertility or pre-vasectomy context. Use was disproportionately high among trans women initiating hormone replacement therapy (HRT), perhaps demonstrating their preference for at-home sample collection to avoid the unique challenges they encounter in a clinical setting. Especially for oncology patients who are admitted to a hospital and cannot leave for fertility preservation, there is an opportunity to further integrate mail-in cryopreservation services within oncology to improve access and reduce time costs.
Factors associated with delay in breast biopsy in a vulnerable population.
Authors: Sharma Shivani , Carrera Camila , Vasudevan Srivatsa Surya , Walker Nakoma , Martinez Shidestini Chanta , Chennapragada Suma Sri , Beedupalli Kavitha 
Published: May 2024

e13526 Background: Prompt diagnosis and treatment of breast cancer can improve outcomes and reduce stress and anxiety in patients. Race, socioeconomic status, and education level are associated with delays in diagnosis in breast cancer patients. 63% of Louisiana’s parishes are classified as rural, and 18.8% of the population is below the poverty line (the second highest in the country). The above factors make access to care challenging. Our study aimed to identify factors associated with delay in breast biopsy after abnormal diagnostic imaging in our patient population. Methods: A retrospective cohort study including female patients >18 years undergoing breast biopsy for abnormal diagnostic imaging findings from 1/1/2022 to 12/31/2022 at LSU Health, Shreveport, was done. Patients with a prior history of breast cancer, pregnant females, and patients with cognitive defects were excluded. Patients were followed from abnormal diagnostic imaging to biopsy. Descriptive analysis was done for baseline cohort characteristics. Univariate and multivariate logistic regression models were used to find 95% Confidence Interval and Odds ratio (ORs) for independent factors associated with time to biopsy (TTB). The study was approved by IRB. Results: 148 women (mean age [SD]: 54 [12] years) were included in the analysis. 67.5% were African American, and 25.6% lived in rural areas. The mean distance from the patient’s home to our center was 26.7 miles (Range: 3-219). The mean TTB from abnormal diagnostic imaging was 13.79 days (Range: 0-84), and 67.5% of patients had TTB of =<14 days. A TTB of more than 14 days was considered a delay in biopsy. After adjusting for age, multivariate logistic regression showed rural residence was associated with TTB of >14 days. (OR: 2.5, 95% CI: 1.15-5.67, p=0.02) (Table). A distance more than 40 miles from the hospital was associated with a higher likelihood of TTB of >14 days on a multivariate model adjusted for age and race revealed (OR: 2.2, 95% CI: 1.004-5.10, p= 0.04). Conclusions: Patients residing in rural parishes and at a distance more than 40 miles from our center have a higher likelihood of delay in time to biopsy from the diagnostic imaging. Same-day breast biopsy should be prioritized in such patients to ensure timely diagnosis and treatment. Cancer Centers serving the safety net population can conduct similar studies and implement changes to ensure equitable access to care. [Table: see text]
Impact of a care delivery intervention for patients with socioeconomic disadvantages, in their preferred language of English or Spanish.
Authors: Khan Sarah , Weldon Christine B. , Trosman Julia R. , Valentine Tierra , Hernandez Jorge , PADURI SWETHA , Reddy Vijay Ketan , Sakhuja Anuradha , Czarnowski Daniel , Patel Twinkle , Oliveras Margarita , Ramirez Sandra , Williams Emilia , Valecha Gautam , Kassem Mohammed Azher , Sharma Jaya , Subramanian Seshan , Ravelo Arliene , Khosla Paramjeet Grewal 
Published: May 2024

e13550 Background: Sinai Health System’s oncology program serves Chicago’s inner city patients with substantial needs for social and primary care, many of whom prefer communication in Spanish. Sinai has implemented a care delivery intervention to address these needs, using the 4R Oncology Model for patient-facing care planning and patient self-management. 4R (Right Info/Care/Patient/Time) has shown significant improvements in the timeliness of care cancer delivery and referrals to cancer care (1). Herein, we implemented the 4R intervention to improve the timeliness of care delivery and referrals to care, focusing on the social and primary care needs of Sinai patients. Methods: Evaluation was conducted by surveying historical control and intervention (4R) cohorts of patients in their preferred language of Spanish or English. The 4R cohort received care at a Sinai medical oncology clinic Apr 2023 - Jan 2024. Control cohort received care Jan 2023 – March 2023. Analyses were conducted using Fisher’s exact test. Results: Survey response: 76% (38/50) in control and 75% (33/44) in 4R cohorts. Participant characteristics were comparable between control and 4R cohorts, respectively: 55%, 58% Hispanic, 42%, 39% Black; 76%, 82% high school education or less; 87%, 97% < $30k annual income; 50%, 55% preferred Spanish; 47%, 45% preferred English. For the total cohorts, all 5 metrics improved between control and 4R, and 4 of the 5 metrics improved significantly (Table). Among Spanish-preferred patients, 3 of the 5 metrics improved significantly, while improvements among English-preferred patients were not statistically significant. Both language groups saw numerical, but not statistically significant improvement in receiving referrals to primary care and meeting all their practical and family needs. Conclusions: The 4R Oncology intervention resulted in significant improvement in time to treatment and referrals to social and primary care in our population of socioeconomically disadvantaged patients with high needs for these services. Patients preferring Spanish benefited at a higher rate than English-preferring patients, indicating that 4R may be an effective method to address the needs of this important patient group. Future efforts will further optimize 4R to address remaining gaps for this group, as well as to improve the delivery of this care for the English-preferred patients. 1. Trosman JCOOP 2021. [Table: see text]
Predicting persistent fever in patients with cancer using machine learning.
Authors: Sauer Christopher Martin , Brehmer Alexander , Rostalski Till , Schuler Martin H. , Nensa Felix , Reinhardt Hans Christian , Kleesiek Jens , Pucher Gernot 
Published: May 2024

e13552 Background: Sepsis is the leading cause of hospital readmission and death in the developed world. Cancer patients are at high risk due to their underlying disease and therapies. Meanwhile, antimicrobial resistance increases steadily, warranting a more careful administration of antibiotics. Predicting fever persistence 48 hours after initiation of antibiotics could help reduce antibiotic escalation and chest CT scans in patients where fever is predicted to subside within the next 24 hours. Methods: All cancer patients of the University Hospital Essen (UHE) between 1/1/2020-7/1/2023, receiving broad-spectrum i.v. antibiotics, and having persistent fever after 48 hours of first administration were extracted from UHE’s Fast Health Interoperability Resources clinical database. Vital signs, clinically important covariates, as well as lag features of vital signs and laboratory values were included. The binary target to predict was prevalence of body temperature ≥38 °C after 48-72 hours. Gradient boosting machines, a deep learning neural network, logistic regression (LR), random forest (RF) and a voting ensemble classifier were implemented. Hyperparameter tuning and model validation was performed through five-fold stratified cross validation (80/20% train/test split). Results: Analyzing 2.9 million patient records of UHE we obtained approx. 27,000 hospitalized adults with cancer. Restriction to hematology/oncology wards and intravenous broad-spectrum antibiotics resulted in 1,267 cancer patients. Of these, 264 patients had persistent fever after 48 hours, of which 111 patients had solid cancers, 66 lymphomas, and 87 other hematologic malignancies. 72 hours after antibiotic therapy 160 patients (60.2%) had persistent fever. Based on the area under the precision-recall curve (AUPRC) of 81.7%, the voting classifier model had the highest predictive performance. The model shows a sensitivity of 85%, a specificity of 63%, a Negative Predictive Value (NPV) of 75% and a Positive Predictive Value (PPV) of 73% on a cross-validated test set (Table). Conclusions: Machine learning-based algorithms can predict fever evolution and assist in clinical decision-making, potentially contributing to better patient care by preventing unnecessary antibiotics escalations and CT scans. Based on current model performance, 25% of chest CT scans could be obsolete as patients will not have persistent fever after 48 hours. Additional results will be presented at the meeting. [Table: see text]
Treatment rates and reasons for non-treatment in a real-world cohort of patients with pancreatic cancer.
Authors: Sundaresan Tilak Kumar , Evans Nicole S , Gupta Suraj , Leung Lawrence , Lee Jeffrey , Wong Maily , VanDenEeden Stephen K. 
Published: May 2024

e13554 Background: The proportion of patients who receive treatment for pancreatic ductal adenocarcinoma (PDAC) and reasons for non-treatment in real-world patient populations are poorly understood. Our study aims to determine treatment rates, predictors for non-treatment, and the rationale for declining treatment in a well-defined, diverse, community-based patient population. Methods: We identified all adult patients with PDAC (n = 3852) between 2010 and 2020 through the Kaiser Permanente Northern California Cancer Registry. Patients were stratified by age and tumor characteristics to determine rates of oncologic therapy. Multivariable logistic regression was used to assess how covariates were associated with receiving treatment. A detailed chart review of all patients ≤ 65 years and a random sample of 100 patients > 65 years with non-metastatic disease who did not receive treatment was performed to better understand the reasons for non-treatment. A random sample of 20 patients with metastatic disease who did not receive treatment were also reviewed. Results: Of patients with PDAC, 77.7% of patients with non-metastatic and 56.2% with metastatic disease received any cancer-directed treatment. A multivariate analysis showed no statistically significant differences by race/ethnicity or gender. Increasing age was a significant negative predictor of treatment. Charlson comorbidity index (CCI) did not influence treatment rates in patients with non-metastatic PDAC, but in patients with metastatic disease those with CCI 3+ had a decreased odds of receiving treatment. Of patients ≤ 65 years with non-metastatic disease, 90.3% were seen by an oncologist, and of those patients 89.3% were offered treatment. The primary reason for declining treatment was patient/family preference in 80.8%, followed by PDAC-related frailty in 11.5%, and medical comorbidity/other malignancy in 7.69%. Similarly, of patients > 65 years with non-metastatic disease, the majority were also seen by an oncologist, offered treatment, and declined due to personal preference. The same trends were noted in patients with metastatic PDAC. Conclusions: A significant proportion of patients with PDAC did not receive cancer-directed treatment in a real-world, community-based patient population, and this was more likely for patients with metastatic disease. Race and gender did not influence whether patients received treatment in this fully insured patient population and comorbidity had a small effect. Patients of increasing age, however, were less likely to receive treatment. Most patients who did not initiate treatment did so primarily due to patient/family preference despite being offered cancer-directed therapy by their treatment team. These findings may help guide the development and utilization of patient decision-aids in PDAC to improve goal concordant care that accounts for patients’ preferences and values.
Delays in receipt of neoadjuvant chemotherapy compared to upfront surgery in patients with operable breast cancer.
Authors: Wooster Margaux , DeStephano David , Beauchemin Melissa , Wang Shikun , Hur Chin , Wright Jason Dennis , Hershman Dawn L. , Accordino Melissa Kate 
Published: May 2024

e13561 Background: Delays in time from breast cancer (BC) diagnosis to BC treatment initiation (BCTI) including upfront breast surgery (UBS) or neoadjuvant chemotherapy (NACT), can negatively impact BC survival. We evaluated odds of delay in BCTI among patients (pts) who received NACT compared to pts who received UBS. Methods: We conducted a retrospective cohort study using the National Cancer Database. Eligibility included age ≥18 years, diagnosis of non-metastatic BC between 1/1/2010 – 12/31/2020, who were treated with NACT followed by surgery or UBS followed by adjuvant chemotherapy (AC). Pts who did not receive NACT or UBS within 180 days of diagnosis were excluded. The cohort was stratified by first BC treatment (NACT or UBS), and time to NACT (TTNACT) or time to surgery (TTS) was recorded for each patient in days. Pts were categorized into sub-cohorts by receptor status and clinical stage: 1) Hormone Receptor positive/HER-2 negative (HR+/HER2-) lymph node (LN)+; 2) HER-2 positive (HER2+) cT2+ or LN+; and 3) triple negative BC (TNBC) cT2+ or LN+. Logistic regression adjusted for demographic, socioeconomic, and tumor characteristics was used to identify delays in BCTI > 60 days. Multiple imputation was used for missing data. Inverse probability treatment weighting (IPTW) was done as a sensitivity analysis. Results: We identified 218,049 eligible pts, 75% were White, 18% were Black, and 8.6% were Hispanic. Median age was 53 years (range 18-90). There were 145,697 (67%) pts who received NACT with median TTNACT of 32 days (range 0-180), and 9.8% had a delay > 60 days (Table). There were 72,353 (33.0%) pts who received UBS followed by AC, with a median TTS of 33 days (range 0-180), and 13% with a delay > 60 days. In multivariable analysis, pts who received UBS had higher odds of BCTI delays compared to NACT, with OR 1.50 (95%CI 1.42-1.57) [HR+/HER2-], OR 1.49 (95%CI 1.41-1.58) [HER2+], and OR 1.26 (95%CI 1.18-1.34) [TNBC]. Results from IPTW analysis were similar, groups were well balanced, and pts who received UBS had higher odds of BCTI OR 1.50 (95%CI 1.44-1.57) [HR+/HER2-], OR 1.75 (95%CI 1.67-1.83) [HER2+], and OR 1.46 (95%CI 1.38-1.53) [TNBC]. Conclusions: Among high clinical risk BC patients who received surgery and chemotherapy, those who underwent NACT were less likely to experience delays in BCTI compared to pts who received UBS first. Among eligible pts, use of NACT may reduce delays in BCTI. [Table: see text]
Retrospective study on the correlation between MASCC score and the evolution of febrile neutropenia for patients with solid cancer.
Authors: Rivest Charles-Éric , Ben Abderrazik Fatim Ezzahra , Côté-Marcoux Marc-Antoine , Malick Mandy , Hanel Robert , Pavic Michel 
Published: May 2024

e13570 Retrospective study on the correlation between MASCC score and the evolution of febrile neutropenia for patients with solid cancer. Background: The Multinational Association for Supportive Care in Cancer (MASCC) score is used to risk-stratify outpatients with febrile neutropenia. Currently, data on the use of the MASCC score are based on studies with a small number of patients. Our primary aim was to determine whether a MASCC score ≥ 21 identifies patients with solid tumors who would evolve without complications. Methods: We conducted a retrospective cohort study of patients admitted with febrile neutropenia and solid cancer at Sherbrooke University Hospital from 2011 to 2022. We collected patients' demographics, type of cancer, current chemotherapy regimen, whether patients were candidates for outpatient treatment, MASCC score, duration of IV antibiotic therapy, complications (intensive care unit admission, hypotension, hypoxemia, acute kidney failure, bacteriemia (persistent or developing after 48h of IV antibiotics) and escalation in antimicrobial regimen after hospital admission), and inpatient deaths. We used chi-square analysis and multivariate analysis to determine factors other than MASCC that predict complications. Results: A total of 290 febrile neutropenia patients with solid tumors were identified. 93 patients had a MASCC score <21 and 196 were identified as low risk (MASCC score ≥ 21). The preliminary results showed that the MASCC score had a specificity of 58,3% [95% CI 48,4%-67,8%], a sensibility of 83,0% [95% CI 76,7%-88,1%], a positive predictive value of 77,0% [95% CI 72,7%-80,9%] and a negative predictive value of 67,0 [95% CI 58,7%-74,4%] to identify complications. A low risk MASCC score showed statistically significative reduction in duration of neutropenia, IV antibiotics, fever and length of hospitalisation. Conclusions: The MASCC score did not demonstrate high enough accuracy to precisely identify patients which would have evolved without complications. However, a low-risk score identified by using the MASCC score was found to have strong association with reduction of duration of hospitalisation burden. Furter data will be collected to increase the strength of the study.
Optimizing the oncology ecosystem: Addressing barriers between community and academic oncologists.
Authors: Khalil Ahmed , Kango Ghazal , Graff Stephanie L. 
Published: May 2024

e13580 Background: Oncology practice is becoming more complex and collaboration between academic and community practices is necessary to optimize care for patients. This qualitative analysis explores perceived gaps between academic and community practices and proposed solutions to a more cohesive oncology ecosystem for optimal quality and care delivery. Methods: A 10-question mixed-methods quantitative and qualitative survey was developed (SurveyMonkey) to identify practice setting, methods of collaboration, perceived barriers, and strategies for improvement. Both open-ended and multiple-choice questions were used. Survey was distributed via social media outreach on Facebook, X, and email. Individual responses were coded by AK, then compared using two sample z-test for proportions. Results: 68 responses were collected, comprising 45.5% community and 44.1% academic physicians. Non-clinician respondents (2.9%) were excluded. Respondents had been in practice: >20 years (16.42%), 10-20 years (40.3%), 5-9 years (19.4%) and < 5 years (23.9%). Collaboration commonly occurred via conferences (73.68%), shared Electronic Medical Records (EMRs, 59.65%) or research (38.6%). The main barriers perceived are summarized (Table). Community oncologists describe lack of appreciation from academics, time constraints, difficulty communicating. Main barriers to collaboration perceived by academic oncologists include delayed referral, time constraints, difficulty communicating, lack of shared EMRs, fear of patient displacement from the primary referring practice, and administrative red tape. Proposed solutions from both groups include engaging community oncologists in academic tumor boards (n=8/68,11.8%) and regional education events (n=2/68, 2.9%). Improved communication. Additionally, both groups were interested in expanding clinical trials in the community. Both groups noted the need for financial incentives and institutional support for successful cooperation (n=3/68, 4.4%). Conclusions: Our data unveiled the nuances of collaboration. Barriers mainly consist of lack of acknowledgement, poor communication, and barriers in EMR utilization. Both groups struggle with time constraints. This data can inform multi-shareholder groups working for systems-based process improvement, expanding collaboration between community and academic oncology. [Table: see text]