bluuebunny/crossref_metadata_2025_split huggingface dataset:
Understanding barriers and facilitators to clinical trial participation among Black patients with multiple myeloma.
Authors: Saxton Claire , Cutforth Asia , Gonzalo Maria Belen , Nichosl Helen , Fearnley Kirstin 
Published: June 2022

12137 Background: Currently, Black patients make up 20% of people living with multiple myeloma, yet they represent only 6% of participants in clinical trials.1 The underrepresentation of Black patients in clinical trials can contribute to outcome disparities thereby negatively impacting health equity in cancer treatment and outcomes.2 This project examined attitudes towards clinical trials among Black multiple myeloma patients and caregivers. Findings will inform the development of programs aimed at increasing clinical trial participation in this population. Methods: In 2021, the Cancer Support Community conducted an online survey to gain insights on barriers, facilitators, and perceptions of clinical trials among Black multiple myeloma patients and caregivers/care partners. Survey questions were informed by insights from prior focus groups. 94 patients and 101 caregivers were surveyed. Results: Most participants were male (62%) and African American (90%). 5% identified as African Caribbean and 5% as Black and Hispanic. The average age was 46 years. Just over half (51%) currently or previously participated in clinical trials. Of those who chose not to participate in a trial, the most common reasons were fear of side effects (46%) and fear of receiving a placebo (38%). Another barrier to participation reported was discomfort with being randomly assigned to a treatment (56%). Participants reported a significant level of distrust in medical research and doctors, saying that it was “very or somewhat likely” that doctors provide treatment as part of an experiment without patient consent (41%) and that they might be used as a “guinea pig” (25%). Of note, 57% of respondents said COVID had changed their attitude towards participating in clinical trials. 14 of 16 factors mentioned in our focus groups were affirmed by more than half of respondents as facilitating participation in a clinical trial. The top factors were: Understanding potential side effects (66%) My health care team speaks to me about trials (65%) Compensation offered for transportation, childcare, or time off work (62%) My family/community support my decision (61%). Conclusions: These findings are consistent with previous research which found that cancer patients reported the biggest attitudinal barriers to clinical trial participation were fear of side effects, distrust in medical research, and random assignment to clinical trial groups.3 Our study highlights that Blacks and African Americans living with multiple myeloma value multifactorial efforts to increase clinical trial participation: logistical and financial interventions, patient/provider communication, and culturally sensitive support and education programs. These programs can also work to improve health equity by reducing barriers to overall care and encouraging Blacks and African Americans living with multiple myeloma to be active members of their health care team.
Changes in cancer mortality by race and ethnicity following the Affordable Care Act implementation in California.
Authors: Martinez Elena , Gomez Scarlett Lin , Canchola Alison J. , Oh Debora , Murphy James Don , Mehtsun Winta Tsegay , Yabroff K. Robin , Banegas Matthew P. 
Published: June 2022

1500 Background: Implementation of the Affordable Care Act (ACA) has resulted in improvements in cancer outcomes but the extent to which these apply to specific racial and ethnic populations is unknown. We examined changes in health insurance distributions pre- and post-ACA and assessed cancer-specific mortality rates by race and ethnicity. Methods: The population included 167,181 newly diagnosed breast (n = 117,738), colorectal (n = 38,334), and cervix cancer (n = 11,109) patients younger than 65 years and 141,026 patients 65 years or older in the California Cancer Registry. Hazard rate ratios (HRRs) and 95% confidence intervals (CIs) were calculated using multivariable Cox regression to estimate associations with risk of 5-year cancer-specific death for each cancer site pre- (2007-2010) and post-ACA (2014-2017), and by race and ethnicity (American Indian/Alaska Natives, AIAN; Asian Americans; Hispanics; Native Hawaiian/Pacific Islanders, NHPI; non-Hispanic Blacks, NHB; and non-Hispanic whites, NHW). Difference-in-difference analysis was conducted to compare changes over time between younger (< 65 years) and older (65 years and older) patients. Results: Cancer-specific mortality for patients age < 65 was significantly lower post- vs. pre-ACA for colorectal cancer among Hispanic (HRR = 0.83; 95% CI: 0.74-0.93), NHB (HRR = 0.69; 95% CI: 0.58-0.81), and NHW (HRR = 0.90 95% CI: 0.84-0.97) but not Asian American (HRR = 0.95; 95% CI: 0.82-1.10) patients. The HRR for younger NHB colorectal cancer patients was significantly lower than that for patients 65 years of and older (HRR = 1.09; 95% CI, 0.95-1.25, p-interaction < 0.0001). A significantly lower risk of dying from cervix cancer was observed in the post- vs. pre-ACA period among younger NHB women (HRR = 0.68; 95% CI: 0.47-0.99), but this was not significantly different than that for older women (HRR = 0.41; 95% CI, 0.16-1.01, p-interaction = 0.30). No significant differences in breast cancer-specific mortality were observed for any racial or ethnic group. Conclusions: Findings show decreases in cancer-specific mortality for colorectal and cervix cancers for some racial and ethnic groups following ACA implementation in California. These results shed light on ongoing discussions as additional states consider Medicaid expansion. Future studies should assess shifts between health insurance plans resulting from the economic impact of the 2019 novel coronavirus (COVID-19) pandemic.
Operational metrics for the ELAINE II study combining a traditional approach with a just-in-time model.
Authors: Blau Sibel , Peguero Julio Antonio , Moore Halle C. F. , Anderson Ian Churchill , Barve Minal A. , Cherian Mathew Amprayil , Elkhanany Ahmed , O'Sullivan Ciara Catherine Maria , Moreno-Aspitia Alvaro , Plourde Paul , Gleich Lyon L. , Riesen Kendra , Ezzati Ross , Degele Meghan , Shulman Megan , Stempf Stephanie , Cooney Matthew M. , Damodaran Senthil 
Published: June 2022

1504 Background: Trial recruitment that requires specific actionable mutations based on next-generation sequencing (NGS) is challenging. Barriers can include competing studies, physician study awareness, site proximity, mutation incidence, among other concerns. Methods: This study (NCT04432454) opened clinical sites using two methods during the COVID-19 pandemic. The “Traditional” approach included site selection, IRB and contract approval, and trial activation prior to a patient being identified for enrollment. The second approach used the Tempus “TIME” Trials network that would only open a site after identifying a patient with a mutation of interest and eligible for the trial. Results: The first patient enrolled was on 10/12/20 and the last patient was on 6/24/21. A total of 16 sites (6 Traditional and 10 TIME) participated. All Traditional sites, and none of the TIME sites, were affiliated with major academic institutions. Duration for full CTA execution for Traditional sites averaged 200.5 days (range 142 to 257) and for TIME sites averaged 7.6 days (range 2 to 14). IRB approval time average for Traditional sites was 27.5 days (range 12 to 71) and TIME sites was 3.0 days (range 1 to 12 days). Days from site selection to activation letter for Traditional sites was on average 250.0 days (range 187 to 281) and for TIME sites was 131.6 days (range 22 to 248). Time from study activation to first consent was 33.3 days (range 18 to 58) for Traditional sites and 8.8 days (range 1 to 35) for TIME sites. The first patient on-study was at a TIME site 115 days prior to a Traditional site and the first 7 patients enrolled were at TIME sites. Traditional sites consented 23 and enrolled 16 patients while the TIME sites consented 16 and enrolled 13. The trial enrolled all 29 patients in 8 months with the anticipated enrollment duration being 12 to 18 months. Conclusions: Although the Traditional and TIME programs had different operational models, they both contributed a significant number of patients and reduced the projected enrollment timeline. TIME sites enrolled the initial patients. These results demonstrate that the “Just-in-Time model,” in conjunction with a Traditional model, can reduce projected overall time to enrollment in biomarker-driven studies. [Table: see text]
Improving supportive care for patients with thoracic cancer.
Authors: Banks Lakedia Charman , Kapphahn Kris , Das Millie , Wujcik Debra , Stricker Carrie Tompkins , Shanbhag Lucinda , Lin Stephanie , Zhu Ge , Patel Manali I. 
Published: June 2022

1520 Background: Improving lung cancer care among Veterans is a priority within the Veterans Affairs due to higher rates of lung cancer incidence, morbidity, and mortality among Veterans compared to non-veterans. Unaddressed symptom burden is common due to many factors including complex comorbidities, psychosocial challenges, smoking history and limited social support networks. Additionally, complications from social determinants of health can obstruct successful discussions of symptom-burden between Veterans and their clinical care teams which can limit compliance with recommended symptom management strategies. To overcome these barriers, we conducted a randomized controlled trial to test the effectiveness of a lay volunteer-led proactive symptom assessment and symptom intervention. The objective was to determine if the intervention improved clinician documentation from baseline to 6-months post-enrollment compared to usual care. Secondary outcomes included change in patient activation, health-related quality of life (HrQOL), and symptom-burden. Methods: Patients were randomized into the lay volunteer proactive symptom assessment intervention plus usual cancer care (intervention group) or usual cancer care alone (control group). We conducted electronic health record review to assess primary cancer-clinician symptom documentation of Veterans’ symptoms identified as moderate-to-severe at baseline and 6-months using the Edmonton Symptom Assessment Scale. Patient surveys with validated assessments were used to assess patient activation, HrQOL and symptom burden at baseline (time of enrollment) and 6-months post-enrollment. We used regression models to evaluate differences in our primary and secondary outcomes. Results: 60 Veterans were consented and randomized into the study (29 control; 31 intervention). There were no differences in demographic or clinical factors across groups. The median age was 70 years (range 56-85), 95% were male, 70% identified their race as White, 53% were married and 48% had a 2-year or 4-year college degree. The majority had at least 3 comorbidities (54%), diagnosed with stage 3 or 4 (62%) and received systemic treatment with chemotherapy and/or radiation (77%). At 6-months post-enrollment as compared to baseline, the intervention group had greater improvements in symptom documentation (56% from 12.5% vs. 29% from 43%, p = 0.01), greater improvements in patient activation (p<0.001), HrQOL (<0.001), and lower symptom burden (p<0.001) than the control group. Conclusions: Integration of proactive symptom assessment by lay volunteers has a significant and meaningful effect on symptom documentation, patient activation, quality of life, and reducing symptom burden among Veterans with lung cancer. Clinical trial information: NCT03216109.
Impact of proactive symptom monitoring on quality of life (QoL) and treatment toxicity in patients with cancer receiving chemotherapy: A meta-analysis of randomized clinical trials.
Authors: Tamimi Faris , Stajer Sabrina , Savill Jacqueline , Mittal Abhenil , Molto Consolacion , Di Iorio Massimo , Amir Eitan 
Published: June 2022

1545 Background: Early recognition and management of symptoms can improve outcomes in cancer patients receiving treatment. A number of randomized trials have investigated the effects of adding proactive symptom monitoring to usual care (UC). These include web-based, application-based, or telephone-based assessments. Results have been variable, and the impact of proactive symptom monitoring on QoL, treatment toxicity and utilization of unscheduled acute care remains unclear. Methods: A systematic search of MEDLINE identified prospective, randomized trials that studied the effect of proactive monitoring and intervention versus UC in cancer patients receiving chemotherapy. The difference between proactive symptom monitoring and UC on the mean and SD for QoL using validated scales was collected for each study and pooled in a meta-analysis. Analysis was performed using the standardized mean difference (SMD) using random-effects modeling. The effect size was reported as the Hedges’ adjusted g. We also calculated the odds ratios (OR) for the occurrence of several common symptoms of any grade in the individual trials and pooled them in a meta-analysis. Statistical significance was defined as P < 0.05. Quantitative significance was defined as a difference in QoL score exceeding the minimal clinically important difference (MCID) based on previous studies for each QoL framework. Results: Of the 17 trials which met eligibility criteria, FACT-G and EORTC QLQ C30 were the most consistently utilized QoL tools. The mean difference in score between intervention and control at the last evaluation visits was 2.82 (95% CI -0.57 to 6.21; P = 0.10) in FACT-G and 2.33 (95% CI -0.29 to 4.96; P = 0.08) in EORTC QLQ C30, neither of which met quantitative or statistical significance. There was a statistically significant reduction in fatigue (OR 0.67, 95% CI 0.46 to 0.97; P = 0.04), but no difference in constipation (OR 0.63, 95% CI 0.34 to 1.17; P = 0.15), nausea (OR 1.03, 95% CI 0.72 to 1.47; P = 0.89), pain (OR 0.83, 95% CI 0.62 to 1.10; P = 0.19), or diarrhea (OR 1.41, 95% CI 0.40 to 5.01; P = 0.60). SMD for symptom severity was calculated for fatigue, diarrhea, and nausea. Severity of fatigue was statistically lower with proactive symptom monitoring (SMD -0.45, 95% CI -0.69 to -0.22; I² = 0%, P < 0.001), however, magnitude of effect was modest. Conclusions: Proactive symptom monitoring in cancer patients receiving treatment is not associated with significant or meaningful QoL improvement. Similarly, there is limited impact on individual toxicity.
Machine learning application to find patients with lower-risk myelodysplastic syndrome from real-world data.
Authors: Johanson Colden , Huang Hu T. , Idryo Danny , Broome Ronda , Rioth Matthew J. , Matsuno Rayna K. 
Published: June 2022

1555 Background: It is a challenge to identify patients with myelodysplastic syndrome (MDS) using structured data from electronic health records (EHRs). Current claims-based algorithms incorporating diagnosis codes, clinical labs, and procedures have not been validated against an expert reference standard. A machine learning-based approach was investigated to identify erythropoietin-stimulating agent (ESA)-treated, lower-risk (LR)-MDS patients from structured EHR data. Methods: A sample of 1,549 patients from the Syapse Learning Health Network (SLHN) was identified as potential ESA-treated LR-MDS patients by a team of clinicians and epidemiologists based on diagnosis and medication data from multiple health systems’ EHRs and cancer registries. Of these, 404 (25%) were confirmed as ESA-treated LR-MDS patients through a review of patient records by certified cancer registrars (CTRs). The sample was divided into training and validation sets at a ratio of 80/20, stratified by the outcome. Age, sex, diagnosis codes corresponding to MDS and chronic kidney disease, medication (ESA, luspatercept, lenalidomide), clinical lab tests (hemoglobin, absolute neutrophils, platelet, blast percentage), and evidence of bone marrow biopsy were included as the predictive variables for the models. Gradient boosting machines with a nested cross-validation scheme were adopted to build the optimal model on the training set. Model acceptance was evaluated based on precision and recall on the validation set. The optimal model was then applied to the remaining unscreened SLHN patient population. Results: The optimal model identified an additional cohort of 157 patients based on the predicted likelihood. Among these, 69 (44%) were CTR-confirmed ESA-treated LR-MDS patients, all of whom were previously missed by the initial expert-determined selection criteria, as shown in the table. Conclusions: The application of machine learning methods increased the rate of ESA-treated MDS patient identification even after the expertly-determined population was depleted. This suggests the application of machine learning models using EHR data may improve the efficiency of MDS patient identification and screening efforts for research, quality improvement, and clinical care. [Table: see text]
Natural language processing-optimized case selection for real-world evidence studies.
Authors: Koskimaki Jacob , Hu Jenny , Zhang Yiduo , Mena Jose , Jones Nehanda , Lipschultz Elizabeth , Vaidya Vivek Prabhakar , Altay Gabriel , Erese Vance Andrei , Swaminathan Krishna Kumar , Mendonca Emma , Dutt Tarun , Singh Kuldeep , King Tian , Lakkimsetty Vinay Phani Santosh , Al-Olimat Hussein , Manning Brittany , Komatsoulis George Anthony , Chu Simon , Ottens Jeff 
Published: June 2022

1556 Background: Much information describing a patient’s cancer treatment remains in unstructured text in electronic health records and is not recorded in discrete data fields. Accurate data completeness is essential for quality care improvement and research studies on de-identified patient records. Accessing this high-value content often requires manual and extensive curation review. Methods: AstraZeneca, CancerLinQ, ConcertAI, and Tempus have developed a natural language processing (NLP)-assisted process to improve clinical cohort selection for targeted curation efforts. Hybrid, machine-learning model development included text classification, named entity recognition, relation extraction and false positive removal. A subset of nearly 60,000 lung cancer cases were included from the CancerLinQ database, comprised of multiple source EHR systems. NLP models extracted EGFR status, stage, histology, radiation therapy, surgical resection and oral medications. Based on the results, cases were selected for additional manual curation, where curators confirmed findings of the NLP-processed data. Results: NLP methods improved cohort identification. Successfully returned cases using the NLP method ranged from 75.2% to 96.5% over more general case selection criteria based on limited structured data. For all cohorts combined, 84.2% of the cases sent out for NLP curation were returned with curated content (Table). Each cohort contained a range of NLP-derived elements for curators to further review. In comparison, more general case selection criteria yielded a total of 3,878 cases returned out of 41,186 lung cancer cases sent for curation, for a success rate of only 9.6%. Conclusions: NLP-driven case selection of six distinct, complex lung cohorts resulted in an order of magnitude improvement in eligibility over candidate selection using structured EHR data alone. This study demonstrates NLP-assisted approaches can significantly improve efficiency in curating unstructured health data. [Table: see text]
Natural language processing of Veterans’ electronic health records to confirm diagnoses of monoclonal gammopathy of undetermined significance.
Authors: Wang Mei , Yu Yao-Chi , Liu Lawrence , Schoen Martin W. , Thomas Theodore Seth , Colditz Graham A. , Chang Su-Hsin 
Published: June 2022

1557 Background: The Veterans Health Administration (VHA) provides extensive electronic health records (EHRs) on Veterans nationwide. Our prior studies utilized VHA data to study the risk of progression from monoclonal gammopathy of undetermined significance (MGUS) to multiple myeloma. These studies relied on International Classification of Disease (ICD) codes and manual abstraction on clinical notes to both identify and verify MGUS patients. Diagnosis confirmation is necessary because many providers place a diagnosis on the clinical notes to order lab tests, which is often left in the EHR despite a negative test result. However, manual abstraction is labor intensive and time consuming. With the advancement in natural language processing (NLP), we developed a model to make MGUS confirmation more efficient. Methods: We randomly selected 700 patients within patients diagnosed with MGUS from 1999-2021 in the VHA identified via ICD codes. A random sample of 500 patients were selected and split into the training (80%) and the testing (20%) sets. The remainder (n = 200) served as the validation set. There were 32,708 unstructured hematology/oncology Text Integration Utility reports and 9,237 lab reports (including 2,322 discrete results and 6,915 unstructured comments). All reports were manually reviewed to confirm MGUS diagnoses and served as the reference standard. We compiled three lists of keywords suggestive of MGUS diagnosis, subtypes of immunoglobulins, and negation modifiers. We trained a symbolic NLP model to identify diagnoses using combinations of the lists along with M-protein levels from lab reports. The optimized combination that gave the highest recall and precision from the training set was used and evaluated on the testing and validation sets. Results: Among patients with ICD codes for MGUS, manual abstraction confirmed 84% MGUS diagnoses in the testing set and 80% in the validation set. Our NLP model in the training set confirmed 75% and achieved recall, precision, accuracy, and F1 score of 88.1, 98.7, 89.0, and 93.1%, respectively; in the validation set, our rule confirmed 76% patients and the recall, precision, accuracy, and F1 score were 89.4, 94.7, 87.5, and 92.0%, respectively. On average data abstraction took five minutes per patient (excluding data loading time), whereas NLP model completed 13 patients per minute. Conclusions: The developed NLP model to confirm MGUS diagnosis improves accuracy in diagnosis, compared to ICD codes alone. While the performance is similar to that of manual abstraction, our NLP model is an efficient and viable method in MGUS diagnosis confirmation. [Table: see text]