bluuebunny/crossref_metadata_2025_split huggingface dataset:
The association of social and demographic factors on the time interval between a suspected diagnosis of cancer, diagnosis, treatment, and outcomes in a lung cancer patient population.
Authors: Ajrouch Ali , Jennewein Laura , Li Yang , Pike Francis , Hanna Nasser H. 
Published: June 2022

e18598 Background: Delays in lung cancer (LC) diagnosis and treatment adversely affect patient outcomes. Prior studies report mixed results on the association between delays in diagnosis and various demographic factors. Methods: A retrospective cohort analysis was conducted utilizing a randomly selected subset of LC patients treated at the IU Simon Comprehensive Cancer Center between 1/1/18 and 12/31/20. Chart search identified the dates of first suspected cancer finding (SCF; imaging or high-risk presentation), diagnosis, first treating physician contact, and treatment initiation. Patient characteristics (sex, age, race, marital status, insurance status/type, employment status, smoking status, LC stage, LC type) and healthcare utilization (setting of SCF, modality used to discover SCF) were also collected. Asymptomatic patients with SCF at routine screening were excluded. Multivariable Cox proportional hazards and logistic regression models were utilized with significance defined as p < 0.05. Results: Patient characteristics (n = 203): white 86.7%; ever smokers 86.0%; Medicare 61.3%; NSCLC 87.7%; stage IV 44.3%. SCF was primarily identified in an ambulatory setting (57.4%) by CT imaging (62.1%). Median time from SCF to diagnosis, treating physician contact, and treatment were 24d (IQR: 8-67), 24.5d (IQR: 9-54), and 57d (IQR: 28-98), respectively. Median time from diagnosis to treatment was 22d (IQR: 7-41). Time from SCF to LC care was significantly associated with cancer type (median times - diagnosis: NSCLC = 27d, SCLC = 9d, p = 0.001; treating physician contact: NSCLC = 27d, SCLC = 6d, p = 0.002; treatment: NSCLC = 66.5d, SCLC = 23d, p < 0.001), stage (median times - diagnosis: stage I = 52d, stage IV = 9d, p < 0.001; treating physician contact: stage I = 32.5d, stage IV = 13.5d, p = 0.008; treatment: stage I = 97d, stage IV = 23d, p < 0.001), and setting of SCF (median times - diagnosis: ambulatory = 30d, emergency = 7d, p < 0.001; treating physician contact: ambulatory = 31d, emergency = 9d, p < 0.001; treatment: ambulatory = 74.5d, emergency = 28d, p < 0.001). Risk of delays were highest among those with Stage I NSCLC and when SCF occurred in the ambulatory setting. Adjusted by cancer types and modality, patients presenting to the ER were 2.15 times more likely to be diagnosed with stage IV disease than those presenting in the ambulatory setting (p < 0.001). Conclusions: Streamlined transitions of care are needed in the ambulatory setting to reduce delays in LC diagnosis and treatment to optimize patient outcomes. The extended delays experienced by early vs late-stage LC patients may be explained by access to care. However, no demographic factors (insurance, age, race) were significantly associated with delays in this dataset. Further studies are needed to assess the timeliness of cancer diagnosis and treatment in large, diverse populations.
Assessing feasibility and utility of an implicit bias training program for addressing disparities in cancer clinical trial participation.
Authors: Barrett Nadine , Boehmer Leigh , Schrag Janelle , Benson Al Bowen , Green Sybil , Hamroun Leila , Howson Alexandra , Matin Khalid , Oyer Randall A. , Pierce Lori J. , Jeames Sanford Earl , Winkfield Karen Marie , Yang Eddy Shih-Hsin , Zwicker Victoria , Bruinooge Suanna Steeby , Hurley Patricia A. , Hanley Williams Jen Hanley , Guerra Carmen 
Published: June 2022

e18599 Background: Low participation of Black, Hispanic, Latinx and other underrepresented racial/ethnic groups in clinical research remains a problem across the U.S. Recent studies have highlighted that stereotypes, assumptions, and bias play a role in lack of diversity in cancer trial participation. To help address this, the Association of Community Cancer Centers (ACCC) and American Society of Clinical Oncology (ASCO) piloted an implicit bias training program for clinical research teams. Methods: Adapted from the Duke University Just Ask™ program, the pilot program is comprised of eLearning modules which can be completed in about 60 minutes. Features include education on diversity, equity, and bias in clinical trial participation, case vignettes, and strategies to mitigate disparities. A call was issued to members of both organizations. After completing the training, all individual participants were asked to complete a retrospective pre/post survey to assess change in knowledge and attitude. Focus groups explored participants’ experience with the training. Another survey was administered 6 weeks later to assess sustainability of changes. Results: Research teams from 50 programs were selected for the pilot. 129 individuals consented, and 126 completed the training and evaluations (98% response rate). 48% of participants reported that they had completed training on implicit bias and/or related topics prior to the pilot. Increased levels of knowledge were reported across all key training concepts, with an average % increase from 19% to 45%. Similar increases were observed for strategies for addressing implicit bias, with an average % increase from 10% to 31%. At 6 weeks post-training, there was a slight decrease in knowledge across most items, from -1% to -8%. Most (92%) participants reported satisfaction with the course, and most (92%) indicated they would recommend it to a colleague and would recommend implementing it at their program. Suggestions to improve the course included streamlining content and providing additional tools and resources. Conclusions: Pilot findings support the feasibility and utility of the training, which can help cancer programs to address disparities in clinical research. Next steps include modifying the course based on participant feedback, disseminating the training and supplementary resources, and exploring options for assessing the impact on upstream outcomes such as diversity in trial participation.[Table: see text]
Predictive factors for cancer treatment delay in an underserved urban population.
Authors: Sheni Risha , Qin Jiyue , Viswanathan Shankar , Castellucci Enrico , Mehta Vikas 
Published: June 2022

e18608 Background: Incremental delays in cancer time to treatment initiation (TTI) have been shown to cause a proportional, independent increased risk of disease specific mortality for breast cancer, colorectal cancer (CRC), head and neck (HNC), non-small cell lung cancer (NSCLC) and pancreatic cancer. A portion of delays can be attributed to increasingly complex workup with modern imaging, genomic, and multidisciplinary/multimodality treatment paradigms. However, studies suggest delays are associated with racial and socioeconomic disparities, implicating a target for addressing inequitable care. Given Montefiore Medical Center (MMC) serves a racially diverse, socioeconomically challenged population, we sought to evaluate the association between patient factors and TTI to identify factors associated with TTI delay. Methods: Retrospective cohort study at an urban community-based academic center of patients diagnosed with or referred for curative intent treatment of breast cancer, CRC, HNC, NSCLC, and pancreatic cancer at MMC from January 2019 to December 2021. Variables of interest included tumor staging, primary treatment modality, marital status, Charlson Comorbidity Index (CCI) score, tobacco use, zip code, insurance type, language preference, primary care provider status, and inpatient (IP) admission or emergency room visit 30 days prior to diagnosis. Results: A total of 3430 patients (F=2376, M=1054) were identified (mean age 64.2 ± 13.3). Demographics are summarized in Table. The Median TTI was 21 days (7, 41 IQR). In assessing factors associated with TTI delay, patients who were IP 30 days before diagnosis were significantly more likely to have timely TTI for breast cancer (p<0.003), CRC (p<0.001), HNC (p<0.006), and NSCLC (p<0.001). For CRC, a higher CCI score was associated with TTI greater than 30 days (p<0.019). For lung cancer, older age (p<0.015) and having public insurance (p<0.001) were associated with TTI delay. Conclusions: IP admission 30 days before diagnosis was associated with timely TTI for breast cancer, CRC, HNC, and NSCLC. Other factors predictive of delay included CCI score in CRC, and advanced age at diagnosis and insurance type for NSCLC. This study identifies factors predicting delay and opportunities for addressing delay, thereby improving survival outcomes in our population.[Table: see text]
Using a clinical trial schedule of assessments (SOA) as a quality improvement tool.
Authors: Paul Varun , Ferranti Lori , Dilts David M. 
Published: June 2022

e18638 Background: The SOA is a key part of any clinical trial as it highlights activities to be performed during trial execution. Such a schedule provides a roadmap and calendar of tests, measurements, and evaluations. Normally, such a schedule is created after a protocol has been mostly created. Our aim is to demonstrate that the SOA can have a broader usage: that of a quality improvement (QI) tool used during protocol creation, thus helping to ensure a high-quality protocol at the design. When creating a protocol conflicting uses of the Protocol contribute to operational tension. For example, Trial Operations, which wishes to minimize the complexity of the trial, may debate with Clinical Science, who wish to collect the greatest amount of scientific tests and data. The more complex a set of requirements in the protocol, there is a corresponding drop in available sites able to meet those needs. Additionally potential sites may comment about being burdened with tests that are unnecessary to the core protocol aims, thus creating unnecessary delays in the trial. All these issues may be addressed by using the SOA as a QI tool. Methods: This research was completed at a major, international pharmaceutical organization for a Phase III oncology clinical trial. A team was created composed of: Clinical Scientist Lead , Stats/Data Lead, Trial Operations Led, Medical Monitor, Medical Leader, and Lean Expert. Methodologically, this was led by the Lean Expert and each team member was sent an electronic version of a draft SOA for review. They inserted questions and comments that were visible to the entire team. After this, an SOA issues log was created in an SOA tracking system. This system categorized and ranked issues for discussion by the entire team during the creation process of both the SOA and the protocol. The process used a “House of Quality” framework, allowing for the aggregation of protocol requirements so that they could be addressed in total, rather than individually. Results: The process was successful in 3 critical dimensions. From a time metric, the SOA was produced 3 to 6 months faster, enabling numerous efficiencies such as providing the development team with information both faster and at the time the team was creating the final protocol. Second, from an economic standpoint, the labor efficiency of the trial was improved as it required 33% less individuals to produce the SOA and protocol resulting in a protocol at record speed. Finally, protocol quality was increased as in this method the team worked jointly together, nearly simultaneously, to complete both the SOA and the protocol. This reduced the number of Protocol Amendments by 50%. Conclusions: Using this method, there was an increase in quality, a reduction in cost, and a faster time to the sites. This reduced the commercialization timeline by 3-6 months which is a significant cost savings. But most importantly, this allows for better treatment faster to be in the hands of oncologists to treat patients.
Regional block pilot for microvascular free-flap breast reconstruction.
Authors: Kim Leah , Long Anne , Miller Jared , Zurich Holly , Golshan Mehra , Li Jinlei 
Published: June 2022

e18646 Background: With increasing focus on improving patient outcomes and decreasing narcotic use, healthcare has prioritized the use of multidisciplinary, multimodal, and evidence-based approaches to perioperative management through enhanced recovery after surgery (ERAS) pathways. Currently, microsurgical breast reconstruction with or without concurrent mastectomy commonly may employ regional anesthesia in the preoperative and intraoperative techniques. To optimize perioperative analgesia, an ERAS protocol was piloted at a quaternary care academic center implementing a pre-operative anesthesia placed regional block with preference for anterior quadratum lumborum blocks. Methods: A total of 161 patients were enrolled in this single center Continuous Quality Improvement (CQI) project. The project consisted of 72 baseline, 20 pilot, and 69 after pilot patients who underwent unilateral (n = 65) or bilateral (n = 96) deep inferior epigastric perforator (DIEP) flap reconstruction either immediately (n = 87) or delayed (n = 74) after mastectomy. In the 20 CQI pilot group, anesthesiologists pre-operatively placed bilateral anterior quadratus lumborum blocks (aQLB) under direct ultrasound guidance. After positively observed metrics, the official ERAS pathway was implemented into clinical practice, allowing for subsequent accrual of 69 additional patients. We compared this after pilot group to 72 baseline patients who underwent a surgeon-placed intraoperative transversus abdominus plane (TAP) block using liposomal bupivacaine. Statistics data was tracked using a dedicated clinical EMR dashboard. Baseline patients were matched to the after pilot cohort based on laterality and timing of DIEP flap reconstruction. Results: The ERAS pathway with anesthesia-placed aQLB was associated with lower post-operative daily opioid consumption from 27.2 MME to 10.2 MME. Average pain scores, post-anesthesia care unit (PACU) length of stay, hospital length of stay, as well as re-admissions and complications were not statistically different before and after the implementation of the ERAS pathway. Conclusions: Anesthesia placed pre-operative aQLB is a safe and effective opioid-reducing pain control modality for DIEP flap reconstruction with or without mastectomy. Prospective and controlled studies will be important for the precise impacts of aQLB in DIEP procedures in an ERAS protocol.[Table: see text]
LUNG cancer: Supportive oncology program development for patients and care partners: A qualitative study.
Authors: Beeler Dori Michelle , Mileham Kathryn Finch , York Beth , Szafranski Michele , Walsh Declan 
Published: June 2022

e18652 Background: Lung cancer patients (LCP) have various unmet needs. Evidence supports tailored services to provide individuals information and interaction with healthcare professionals (HCP). We conducted a qualitative study of the ideal supportive care services and structure for LCP and care partners (CP) within Levine Cancer Institute, a regional, multi-site cancer system. Methods: Methods included Focus Groups (FG) and a paper-based survey (PBS) in the Section of Thoracic Oncology. FG included HCP, local Lung Cancer Support Group leadership, LCP, and their CP. FG identified important topics for LCP, CP, and HCP. FG recordings were transcribed, analyzed, and incorporated with the evidence base to inform PBS development. LCP and CP participated in the PBS and responses analyzed with REDCap (v11.1 11, ©2022 Vanderbilt). FG and PBS data were compared for overlap and discordance. Results: FG (total N = 18) identified four Themes: 1) unmet needs 2) program structure 3) barriers to care 4) program content; each analyzed further into three codes (Table). FG Themes are presented in order of frequency (FQ) of unique comments. Unmet needs (FQ=25) included: loneliness, care for the CP, onboarding burden. Structure (FQ=24) included: innovation, variability, virtual. Barriers to care (FQ=20) included: seeking/needing knowledge, transportation, psychosocial. Content (FQ=18) included: combined content and services, financial, educational. Among the responses (total n = 44), strong overlap existed in six PBS areas. Most agreed on seeking/needing knowledge and psychosocial barriers. The majority agreed with combined content and services and the importance of financial and educational content. Variability was specified by agreement on printed and patient portal digital materials with a moderate preference for virtual meetings. Discordance was found across loneliness and transportation while care for the CP, onboarding burden, and innovation were neutral or irrelevant. Conclusions: FG findings confirmed unmet needs, program structure, barriers to care, and program content as integral LCP and CP program concerns. Identified overlap between these Themes and PBS responses are addressed in the ideal LCP and CP support program. This methodology can be generalized to guide future program development to serve other populations and cancer institutes. [Table: see text]
Visualizing meaningful change in small sample sizes.
Authors: Iaconangelo Charles , Serrano Daniel , McManus Shauna 
Published: June 2022

e18656 Background: Patient Reported Outcomes (PROs) are included in trials to incorporate patient voice into the drug development process. In oncology, PROs typically evaluate patient physical function and/or symptom burden. Clinicians need to be able to describe expected patient outcomes in terms of meaningful change in conversations with patients. FDA guidances outline analysis methods for measuring meaningful change, and the results are increasingly used to inform regulatory decisions and in drug packaging. Improving best practices in measuring meaningful change is crucial for regulatory review and to help clinicians communicate effectively with patients. Current best practice under FDA guidance is to compute meaningful within-patient change (MWPC) using an anchor-based approach. The relationship between change scores and anchor groups is visualized via empirical cumulative distribution functions (eCDFs). However, when sample sizes within anchor groups are small, it may be difficult to compute the distribution function. In contrast, a scatterplot approach does not require any computation. The objective was to evaluate under what study conditions (i.e., sample sizes and magnitude of meaningful change) would it be advantageous to interpret meaningful change with a scatterplot approach. Methods: Data simulated to emulate real PRO data from a trial, including attrition due to death, were used to highlight shortcomings in current practice and propose a robust alternative. A simulation study permitted a variety sample sizes and magnitudes of meaningful change to be evaluated. The eCDF was implemented in accordance with current guidance and compared to scatterplots. The scatterplot approach was operationalized as follows: the subject scores were plotted with the baseline scores on the x-axis and the follow-up scores on the y-axis. The scatterplot was stratified by color to represent anchor group-specific scores. Subjects who did not respond at follow-up due to progression or death were plotted in a separate color, using the baseline response and an assigned follow-up score reflecting maximum severity. A 45-degree line represented No Change. The vertical distance between the subject score and the 45-degree line represented the observed change. Results: When sample size was below n = 10 subjects per anchor group, the eCDF yielded uninterpretable visualizations. At sample sizes n = 1-20 per anchor group, the scatterplot approach consistently yielded interpretable visualizations of meaningful change. Conclusions: The scatterplot approach avoids methodological complications and thus ensures transparency in the evaluation of meaningful change. Recommendations to supplement current best practice with the scatterplot approach described above should be considered.
Development of a predictive model for emergency room utilization and unanticipated hospital admission in patients receiving cancer treatment for solid tumor malignancies.
Authors: Watson Catherine , Alhanti Brooke , Zhao Congwen , Havrilesky Laura J , Davidson Brittany Anne 
Published: June 2022

e18674 Background: Unanticipated healthcare resource utilization, in the form of either emergency department utilization (EDUs) or hospital admission (HA), is an indicator of lower quality of cancer care. The objective of this study was to develop a comprehensive predictive model for emergency department utilization and hospital admission (HA) for patients with solid tumors who received chemotherapy within 30 days. Methods: We abstracted electronic health data on oncology encounters from all patients receiving systemic therapy chemotherapy for solid tumors from March 01, 2015 to August 21, 2020. Patients included in the cohort met the following criteria:1) Ages > = 18 years old 2) Diagnosed with any solid tumor (3) Had any oncology treatment plan related to their solid tumor diagnosis; 4) Received at least one chemotherapy during March 01, 2015 through August 21, 2020. We defined a primary composite outcome of emergency visit or unanticipated hospitalization within 30 days after the encounter. Patient information was abstracted. We developed a predictive model for the primary outcome using LASSO logistic regression modeling. To evaluate the model, we calculated the Area Under the Receiver Operator Characteristic (AUROC) and the calibration slope. Results: 12,917 unique patients with 134,641 encounters were included. Of these, 9100 (6.7%) of encounters were ED visits or hospitalizations. 714 patients (5.5%) had at least one ED visit or hospitalization within 30 days of treatment; patients at highest risk for this outcome included those with pancreatic cancer, lung cancer and ovarian/endometrial cancer (9.1%, 7.4% and 6.8% respectively). 38 variables were identified as contributing to the LASSO model. The top predictors, in order of importance, were fever, hypernatremia, hypotension and low albumin. The model’s AUC was 0.83, indicating good model sensitivity to outcome. Conclusions: Approximately 7% of healthcare encounters in patients undergoing treatment for solid malignancies were ED visits or hospitalizations. Identifying patients at highest risk for this outcome may prevent unnecessary resource utilization and optimize patient care during cancer treatment. The model developed in this study demonstrated good sensitivity and could be implemented in clinical practice to allow for preventive outpatient interventions. Work is ongoing to integrate this model into clinical care at our institution.[Table: see text]