bluuebunny/crossref_metadata_2025_split huggingface dataset:
Comorbid opioid use disorder and resource utilization outcomes in patients with neoplasm-related pain: A population analysis.
Authors: Akhiwu Ted , Freeman Jincong Q. , Atarere Joseph , Ndakotsu Andrew , Omoike Eugene , Edigin Ehizogie , Kanemo Philip Onyekaoso , Gangireddy Mounika 
Published: June 2024

11040 Background: Neoplasm-related pain (NRP) is one of the most debilitating sequalae of cancer, affecting patients’ (pts) quality of life. Its intersection with opioid use disorder (OUD) adds a layer of complexity to patient care in the United States (US). As the prevalence of both conditions continues to increase, understanding the association between OUD and NRP becomes important. This study aimed to shed light on this intricate relationship by examining outcomes in resource utilization among OUD pts with NRP. Methods: We analyzed weighted data from the 2016-2020 iterations of the National Inpatient Sample database. We included pts 18 years or older with a principal discharge diagnosis of NRP with or without a secondary diagnosis of OUD. The main outcomes for the study were length of stay (LOS) and total hospitalization charges (THC) adjusted for inflation using the Medicare Hospital Consumer Price Index. Baseline sociodemographic and hospital characteristics as well as comorbidities of patients with and without OUD were compared using χ2 for categorical variables and Student’s t-test for continuous variables. Multivariable Linear regression was used to assess the relationship between comorbid OUD and resource utilization among NRP hospitalizations. Statistical significance was set at p<0.05. Results: There were 120,359 hospitalizations for NRP between 2016 and 2020. Among patients hospitalized for NRP, 3335 (2.78%) had a secondary diagnosis of OUD. The mean age of NRP pts was 60 years. Compared to patients without OUD, OUD pts were male (50.67% vs 45.47%; p = 0.0086), younger (mean age 56 vs 60 years; p<0.0001), more likely to be on Medicaid (37.48% vs 21.00%; p <0.0001) and hospitalized in urban hospitals (94.6% vs 91.37%; p = 0.0040). On multivariable linear regression, OUD was associated with an increased LOS by 0.93 days compared to non-OUD patients (95% CI: 0.265 – 1.600; p = 0.006), as well as increased THC by $9248 (95% CI: 1676.00 – 16820.41; p = 0.017) after adjusting for sociodemographic and hospital covariates. Conclusions: Our study highlights an increased resource utilization among pts with NRP and comorbid OUD. We surmise this could be due to increased tolerance to and dependence on the effects of opioids requiring more intricate multidisciplinary care in managing their pain. Recognizing this association is important for medical oncologists and other health care providers when managing NRP pts with OUD. Incorporating multimodality pain management early on admission could improve patient outcomes while optimizing healthcare resources.
Electronic patient-reported outcome measures (E-Proms): Results of the H2020 CAPABLE project for patients with metastatic renal cell cancer (mRCC).
Authors: Locati Laura D. , Rizzo Mimma , Gabanelli Paola , Quaglini Silvana , Tibollo Valentina , Malovini Alberto , Panzarasa Silvia , Terzaghi Matteo , Lanzola Giordano , Premoli Andrea , Demurtas Sara , Sacchi Lucia 
Published: June 2024

11045 Background: E-Proms were shown to benefit both survival and quality of life (QoL) of cancer patients. So far, E-Proms have been mostly implemented as the electronic version of validated, paper-based questionnaires typically administered at follow-up visits. Benefits of telemedicine systems that put symptom reporting within a wider framework embracing a holistic view of a patient's wellbeing are still under-investigated. Methods: Thirty mRCC patients were enrolled in the CAPABLE project intervention cohort during 2023. They were provided with the CAPABLE smartphone app, and with a smartwatch able to monitor vital parameters, physical activity, and sleep. The app acts as a virtual coach, allowing patients to enter any symptom they experience through an interface inspired by both CTACE and proCTCAE. Immediate feedback on whether to contact or not a doctor is generated according to the symptom severity. Patient’s data are matched with the ESMO guidelines for toxicity management, a selection of which was computerized within CAPABLE, and any generated recommendation is shown to the patient. The app manages the symptom follow up, periodically prompting the patient to report any change, and it also promotes activities aimed at improving patients’ education, mental wellbeing and lifestyle. All the entered data may be visualized by doctors at the hospital. The CAPABLE cohort was compared with a control cohort of 77 mRCC patients, enrolled in 2021/2022, who underwent standard care. All patients filled in a set of questionnaires measuring QoL, satisfaction, nutritional and psychological status at enrollment (T0), after 3 months (T1) and after 6 months (T2). A focus group with the CAPABLE cohort was held close to the end of the study. In this report the primary outcome, i.e. EORTC QLQ-C30 score for QoL, is discussed, together with qualitative results from the focus group. Results: The analysis shows an increase in the average QoL for the CAPABLE cohort (80.7 vs 84.9), and a slight decrease in the controls (80 vs 77), with a statistically significant interaction between time and cohort (p < 0.05). Considering QLQ-C30 subscales, CAPABLE patients showed less fatigue and improved social and role functioning. As from the focus group, the app was perceived as effective and useful. More connection with the patient’s clinical pathway and more personalization according to specific treatments were highlighted as needed improvements. Conclusions: This study shows that E-Proms may be effectively included in a comprehensive and engaging system for remote management of mRCC patients. Clinical trial information: NCT06161233 .
Administrative burden of establishing health care coverage for the uninsured and its impact on timely access to care.
Authors: Wang Laura , Hong Arthur S , Narayan Aman , Sadeghi Navid , Halm Ethan A 
Published: June 2024

11050 Background: Although safety net coverage programs like Medicaid exist to fund health care for low-income patients, the administrative burden of the lengthy application limits uptake by eligible individuals. Therefore, many patients do not attempt to enroll until they become ill, which is common among uninsured adults diagnosed with cancer. The literature reports worse cancer outcomes for those with Medicaid, but reasons for this are unclear. The delays in enrollment for patients with uncertain coverage at the time of diagnosis – those in limbo – are understudied and may explain some of this persisting disparity. We estimated the patient and health system burdens for those with uncertain coverage status at the time of cancer diagnosis at Parkland Health, the main provider of cancer treatment for the uninsured in Dallas, Texas. Methods: We assembled the electronic health record financial encounter data of patients with an incident cancer diagnosis in the Parkland tumor registry, diagnosed from 2010 – 2022. We defined patients as being “in limbo” if they had self-pay, pending, or missing insurance plans at the date of diagnosis. We also included patients whose coverage verification date occurred after diagnosis date, since coverage programs can apply retroactively for 90 days. We used plan verification dates to identify when limbo status resolved. We used coarsened exact matching to balance across age, race, Spanish-language preference, Charlson, and screenable cancer type, then multivariate regression to compare patients that were in limbo to those that had established coverage before cancer diagnosis. Results: We identified 17,468 adults newly diagnosed with cancer, 80% of whom were < age 65, 55% female, 73% Hispanic and/or Black, and 30% Spanish-language preferring. The most common cancer types were GI/colorectal (19%), breast (17%), and lung (9%); 23% were advanced stage at diagnosis and 13% died within 180 days of diagnosis. 52.1% were already enrolled in Medicaid/charity care, but one-third (35%) were in limbo at diagnosis, which lasted for a median 31 days (IQR 13 – 126). Over half (55%) of patients in limbo resolved to Medicaid or charity care. Patients who experienced limbo had a median of 596 financial account notes in the first year after diagnosis, 121 more than the comparison group (95% CI 86.7 - 156.1) and began treatment 10 days later (95% CI 7.7 - 12.7). They also had a median of 1 ED visit in the year after diagnosis, 0.2 more than patients never in limbo (95% CI 0.11 - 0.26). Conclusions: The administrative burden of enrolling into coverage programs for the uninsured may impose direct and indirect costs on patients and safety-net health systems and may contribute to worse cancer care outcomes. A more comprehensive accounting of the effects of administrative burdens might suggest ways to redesign coverage enrollment for the uninsured to improve care and reduce costs.
Disrupting how we recruit to cancer clinical trials.
Authors: Bickell Nina A. , May Benjamin , Havrylchuk Ihor , John Jimmy , Lin Sylvia , Yagnik Radhi , Tao Ariana , Hillyer Grace C. , Rapkin Bruce , Tatonetti Nicholas 
Published: June 2024

11072 Background: Traditional methods to identify eligible patients for cancer clinical trials rely heavily on manual review, a time-consuming bottle neck limiting access to novel treatments, particularly for underserved populations. This study presents an automated informatics platform designed to simplify and streamline patient identification and trial matching. Methods: Our platform leverages three tools: 1. Parser:Extracts and categorizes trial data based on cancer type, stage, and receptor status from the National Cancer Institute's ClinicalTrials.gov database, storing it in a structured JSON format; 2. Screener: Utilizes Epic Clarity electronic health record data to identify upcoming patients approaching treatment decision points, times when clinical trials are more relevant. Patients are classified as "newly diagnosed" or "potentially progressed" based on progress notes and imaging reports. Eligibility criteria, including stage and receptor status, are assessed using regular expressions; 3. Matcher: Pairs eligible patients with relevant trials based on compatible cancer stage and receptor status. One Cancer Center (CC) is prospectively using the platform to identify patients. The 2nd CC is refining the code using retrospective data. Accuracy was measured comparing automated to manual review by experienced trained abstractors. Results: Implementation across two cancer centers yielded: 1. Reduced manual review burden: Between June 2023 – Jan 2024, of 674 breast cancer patients seen at CC #1, 371 were new patients (N=371) and thus, at a treatment decision point; 3 did not undergo imaging. Of the 300 established patients undergoing imaging studies, the algorithm identified 50/300 (17%) scans as “positive,” and 61 (20%) with mixed disease progression/ regression, requiring manual review and trial matching. 189 scans were read as “negative”: 96% True Negatives; 4% were False Negatives. Automated screening reduced manual review from 300 to 111 patients, significantly alleviating workload on healthcare personnel. 2. Variable staging accuracy: While receptor status classification proved effective, stage accuracy varied between centers, highlighting a need for further refinement in this area. 3. Accurate receptor status classification: The system achieved high accuracy in demonstrating reliable assessment of this vital factor. Conclusions: This automated informatics platform demonstrates significant potential to streamline case identification for cancer clinical trials and thus, foster broader access to novel therapies. Its adaptability makes it suitable for implementation across diverse healthcare settings, empowering wider participation in advancements in cancer care. Ongoing efforts to refine the staging module are underway. Clinical trial information: NCT05146297 . [Table: see text]
Effect of multi-cancer early detection screening on late-stage cancers: A modeling study.
Authors: Chhatwal Jagpreet , Xiao Jade , Merdan Selin , ElHabr Andrew , Tyson Christopher , Cao Xiting , Raoof Sana , Fendrick A. Mark , Ozbay Burak Burak , Limburg Paul J. , Beer Tomasz M. , Deshmukh Ashish , Briggs Andrew 
Published: June 2024

11076 Background: Emerging blood-based multi-cancer early detection (MCED) tests can revolutionize early cancer detection. We evaluated the potential impact of MCED screening in reducing risk of late-stage diagnosis of 12 specific cancers which represent 70% of all cancer incidence in the US. Methods: We developed Simulation Model for MCED (SiMCED), a microsimulation model of 12 solid tumor cancer types: breast, colorectal, endometrial, esophageal, gastric, kidney, liver, lung, ovarian, pancreatic, prostate, and urinary bladder. Transitions between cancer stages (I-IV) were driven by cancer type- and stage-specific dwell times, which were synthesized from published literature and empirical estimates. MCED test sensitivity was derived from the PRE-ASCEND study on average-risk American adults. The model was calibrated to reproduce yearly observed cancer incidence diagnosed symptomatically or through screening as captured in the Surveillance, Epidemiology, and End Results (SEER) database, while accounting for unobserved cancer incidence by age, sex, and cancer type and stage. Using a 50-year time horizon, we simulated 100,000 individuals aged 50 to 84 years. Diagnosis of cancer could arise from usual care (UC) or annual MCED screening. Results: Over the 50-year horizon, MCED screening of 100,000 individuals resulted in 1,323 fewer Stage IV (24%) diagnoses relative to UC, with 38% of Stage IV reductions attributed to non-screening-detectable cancer types (i.e., those without recommended screening guidelines). The table displays absolute and percentage reductions in Stage IV diagnoses for the 8 cancer types with the highest absolute reduction in Stage IV diagnoses. The percentage of Stage IV reduction was 21% for screening-detectable cancers and 29% for non-screening-detectable cancers. Conclusions: Our study suggests that MCED screening could be effective for reducing the incidence of Stage IV cancer, which is associated with the worst survival and quality of life. Of note, MCED has the potential to reduce Stage IV cancer incidence for cancers without recommended screening guidelines. However, the real-world impact of MCED tests and their cost-effectiveness require further investigation. [Table: see text]
Adopting combination machine learning models could reduce hospital length of stay for oncology patients.
Authors: Achuthan Srisairam , Salamatullah Shaikh , Scott Jeffrey A. , Sheth Karna , Lantrip Logan , Lilienthal Drew , Ricca Joseph , Pepper Scott C. , Chatterjee Rishov 
Published: June 2024

11084 Background: Integra Connect previously created in-patient (IP) admission prediction model based on OCM data. One of the challenges for practices in a value-based care (VBC) program is to provide continuous care-coordination during and after an IP admission. Our objective is to show how adopting combination machine learning (ML) models can predict IP length of stay (LOS). Potential benefits include an overall reduction in LOS which could minimize the risk of hospital acquired conditions. Methods: ML models were trained on 5 major cancer types (Lung Cancer, Multiple Myeloma, Lymphoma, Small Intestine / Colorectal Cancer, High-risk Breast Cancer') from 7 OCM practices of PP4-PP9 data, excluding surgery IP admission. The top 5 cancer types accounted for ~50% of total in-patient admissions and ~50% of total LOS in days. The IP admissions were divided into 4 major cohorts in terms of LOS in days (1-3: class 1, 4-8: class 2, 9-15: class 3, and more than 15: class 4). To reduce the overall LOS, we adopted two ML models: 1) To classify LOS, a multi-classification model (Model1, an eXtreme Gradient Boosted Trees Classifier) and 2) To predict the LOS for classes 1-3, a regression-based model (Model2, an eXtreme Gradient Boosted Trees Regressor with Early Stopping). Class 4 cohort had a wide range (16- 90+ days) for LOS and therefore was excluded from Model2. The other three cohorts belonged to Q1, IQR, and Q4, respectively. Model1 was trained on 4,280 randomly selected sample IP admissions to balance each class and Model2 was trained on 19,636 IP admissions, both with 102 features. Results: The models were tested on PP10-PP11 claims excluding 0 days LOS. Model1 predicted 296 (3,945) IP admissions in class 4 of which 88 were true positives. Assuming at least a 10% reduction in associated total LOS translated to lower LOS of 1-3% for 5 (7) practices. The remaining 3,649 IP admissions were used to predict LOS by Model2. The difference between actual LOS and Predicted LOS was found as 18%-24%. To address model prediction errors, we defined the target LOS to be predicted LOS with an upper bound of at least 10%. This led to reductions of 6% -16% across practices. Finally, combining the results of both ML models we determined that the potential to lower LOS was at least 6% and at most 19%. Conclusions: Our ML models identified opportunities to reduce LOS across multiple OCM cancer practices for 5 major cancer types. We also identified a cohort of patients with critical condition (class 4) that is vital for practice transformation initiatives. These identified reduction in LOS for oncology patients provides cost reduction and quality improvement opportunities in VBC programs. In the future, more opportunities to lower LOS will be explored with a re-admission prediction model.
Implementation of standardized electronic documentation of goals of care discussions to improve cancer care.
Authors: Vasiljevic Katarina , Kane Shelly , Tuyn Brooks , Schoenecke Jennifer , Edmonds Kyle P , Gold Kathryn A. 
Published: June 2024

11127 Background: Early documentation of physician-led goals of care (GOC) discussions is important to providing comprehensive and individualized quality care to cancer patients as it can provide all members of the patient care team with a context for the type of care each patient wishes to receive prior to and at end of life. The electronic medical record (EMR) should provide easy access to these conversations to seamlessly integrate outpatient, emergency room and inpatient care of cancer patients. Methods: The institution-wide initiative was introduced via presentations and emails between 8/2021 and 11/2021 to all Moores Cancer Center faculty at UC San Diego Health. A SmartPhrase was created in our EMR Epic as the intervention, including critical components of a GOC discussion: people present during discussion, goal of cancer-directed treatment, anticipated cancer trajectory shared with the patient, and patient’s health care agent. The SmartPhrase was radiolabeled to be easily searchable for any clinician accessing the patient’s chart. Physician documentation was tracked for the purposes of achieving a group goal with a target of greater than 50% documentation of GOC discussions within 30 days of the first day of the first cycle of chemotherapy. Prior to physician education, 4% of patients had a GOC note using a searchable SmartPhrase. In 12/2021, we began sending individual emails to physicians to remind them of eligible patients. Results: By 1/2022, after education, 50% of physicians ordering chemotherapy had used the GOC SmartPhrase and 29% of patients initiating chemotherapy had a GOC note using the SmartPhrase. After initiation of monthly reminder emails, documentation increased to 51% by 10/2022. An increased use of Advanced Care Planning billing codes was also noted. Conclusions: Implementation of an initiative to standardize electronic documentation of GOC conversations improved early documentation of these vital conversations. Radiolabeling of the SmartPhrase made it easily accessible to all members of a patient’s care team.
Racial/ethnic differences in incidence of squamous cell carcinoma in California.
Authors: Behl Deepti , Parise Carol 
Published: June 2024

11139 Background: Squamous cell lung carcinoma is strongly associated with a history of smoking. Typically, no actionable mutations are found, and treatment options are more limited. It is not known if race has any impact on the incidence of squamous cell cancers, especially for Asian ethnicities since most studies combine multiple Asian populations in an Asian/Pacific Islander (API) category. The purpose of this study was to compare the odds of squamous cell carcinoma versus adenocarcinoma in Hispanic, American Indian, Middle Eastern, and 7 Asian ethnicities when compared with both Black and White men and women. Methods: We accessed 211,987 cases of invasive lung cancer with either squamous cell or adenocarcinoma from the California Cancer Registry 2000-2020. Race was categorized as White, African American/Black, Hispanic, American Indian/Alaskan Native, Middle Eastern, Chinese, Japanese, Korean, Filipino, Southeast Asian (SEA), Pacific Islander, or Asian Indian. Separate logistic regression analyses were used to assess the odds of squamous cell versus adenocarcinoma for each race/ethnicity using White and Black as the reference categories. Analyses were conducted separately for males and females and adjusted for age, smoking status, socioeconomic status, marital status, and Charlson Comorbidity Score. Odds Ratios (OR) and 95% confidence intervals (CI) were computed. Results: When compared with White men, American Indian men had increased odds (OR: 1.39; 95% CI: 1.03, 1.88) and Black, Hispanic, Chinese, Filipino, SEA men had decreased odds of squamous cell carcinoma. Japanese women had increased odds (OR: 1.54; 95% CI: 1.15, 2.07) while Black, Hispanic, Middle Eastern, Filipino, and SEA women had decreased odds of squamous cell carcinoma when compared with White women. When compared with Black men, White men, (OR: 1.11; 95% CI: 1.01, 1.24) American Indian, (OR: 1.56; 95% CI: 1.13, 2.13) and Korean (OR: 1.35; 95% CI: 1.05, 1.73) men had increased odds and Chinese, Filipino, and SEA men had decreased odds of squamous cell carcinoma. White, (OR: 1.13; 95% CI: 1.01, 1.28) Japanese, (OR: 1.75; 95% CI: 1.28, 2.39) and Korean (OR: 1.63; 95% CI: 1.09, 2.42) women had increased odds and Southeast Asians had decreased odds when compared with Black women. Conclusions: Some Asian ethnicities have a higher risk for squamous cell lung cancer than both Black and White men and women even after adjusting for smoking. This is not apparent when the Asian ethnicities are classified as API. Examining individual Asian ethnicities is important since they have little to no shared genetic or cultural variables and are grouped together for convenience rather than any scientifically plausible reason.