bluuebunny/crossref_metadata_2025_split huggingface dataset:
Using machine learning on real-world data to predict metastatic status.
Authors: Green Foad H. , Huang Hu T. , Lerman Michelle , Tran Mary , Subramanian Vinod , Loving Joshua , Rioth Matthew J. 
Published: June 2022

1550 Background: Real world data (RWD) is increasingly used to inform research, patient care, and population health in oncology; however, using RWD at scale requires accurate methods to identify clinically-relevant attributes. Metastatic status is a highly relevant clinical attribute in cancer patients but it is not routinely captured in structured formats and its determination conventionally requires review and interpretation by certified tumor registrars (CTRs). Clinical diagnoses, treatments, imaging procedures and other clinical variables documented in electronic health records (EHRs) can be used to differentiate metastatic from non-metastatic patients. This study describes an effective machine learning approach in utilizing prevalent and standardized data elements from EHRs across multiple health systems. Methods: 28,043 lung cancer and breast cancer patients from two large health systems within the Syapse Learning Health Network with data sources from CTR abstraction and EHRs were analyzed. Patients were labeled for reference metastatic status by CTRs and split into training (n = 22,434) and testing (n = 5,609) cohorts, with proportionate distribution of cancer type and metastatic status between cohorts. A regularized gradient boosting algorithm, XGBoost, was trained using over 750 variables from the patient records collected at the time of or after the initial cancer diagnosis. Results: Integration of ICD-10-CM codes with antineoplastic treatment history and radiologic imaging procedure orders achieved metastatic status prediction with increases to precision and recall in lung cancer (21% and 32% respectively) and breast cancer (39% and 9% respectively), when compared to the use of only ICD-10-CM diagnosis codes for secondary malignant neoplasms (Table). The addition of treatment and procedure data from different cancer types improved the model classification within individual cancer types. Conclusions: One of the biggest challenges in using RWD for precision oncology is identification of clinically-relevant phenotypes at scale. Here we demonstrate a scalable evidence-based method utilizing structured data for imputing metastatic status with high predictive power from two separate health systems. With further validation, this approach may be generalized to other cancer types, applied to temporal slices of data to identify changes in metastatic status, as well as provide a high-confidence designation of metastatic status for other use cases such as staging.[Table: see text]
Can an artificial intelligence-based platform reduce physician burden and increase access to clinical trials?
Authors: Gortzak Uzan Limor , Lichtenfeld J. Leonard Leonard , Bader Tzvia , Gaziel Avital 
Published: June 2022

1552 Background: Clinical trials for cancer patients are an important treatment option and a resource for drug development. Although patients are more actively searching for treatment options through various approaches, the national average of cancer patients accrued to clinical trials remains static at only 2-4%. The most widely recognized resource is clinicaltrials.gov, which does not offer direct trial matching, is geared toward healthcare professionals and uses complex medical terms that are challenging for many patients. For clinicians, the absence of real-time access and insight into all relevant oncology trials, forms a burden on their already-limited time. Alleviating this burden could reduce barriers to clinical trial accrual. Methods: Using AI and an unsupervised natural language process approach, the Trialjectory platform monitors, analyzes and matches patients to clinical trials. Matching is achieved by patient response to a dynamic set of questions curated based on the eligibility criteria of available trials. The online questionnaire (www.trialjectory.com) collects detailed clinical data that include disease characteristics (histological, molecular and mutational status), treatment history, general health and comorbidities. All collected data points are incorporated into the matching process, yielding a high-quality actionable matched-trial list. A skilled support team is available to answer questions and concerns via email, text or phone calls. The platform enables patients to share the matched trial list with their oncologist for discussion. Results: from July 2019 - December 2021, 49,906 cancer patients completed Trialjectory’s questionnaire. Patient accrual grew rapidly since the second half of 2019 with 1462, 2201, 9364,12685, and 24194 new questionnaire completions in each consecutive 6-month period from the 2nd half of 2019 through December 2021. 49,199 (98.6%) of registered patients were found eligible for available trials. Of the matched patients, 4428 patients (9%) applied to a clinical trial. Our matching engine identifies each specific trial match to each patient profile with a sensitivity of 90% and a specificity of 95%. When aggregated to a patient level, the chance that a patient will be matched for at least one trial from those that he is eligible to is 99.9%. Metrics are validated by automatically matching multiple artificially-created patient profiles and manually verifying the quality of the match. Conclusions: TrialJectory is an effective AI-based tool that offers cancer patients and their oncologists direct, precise and quick access to relevant clinical trials. Our data show a high adoption and growing registration rates and well-above average clinical trial application rate. By democratizing the access to clinical trials, increased opportunities are noted that might lead to increased trial participation.
Applicability of a web app for breast cancer risk calculation and personalized recommendations for screening in Mexico.
Authors: Muñoz Lozano Jose Felipe , Zayas Omar A. , Salazar-Mejía Carlos Eduardo , Oyervides Victor , Vidal-Gutiérrez Oscar , Pineiro-Retif Rafael , Garza Brenda , Gomez Ricardo Andres , Renteria Larisa , Pérez-Ibave Diana Cristina 
Published: June 2022

1567 Background: A minority of women with breast cancer in Mexico are being diagnosed through a screening program, which translates into late diagnosis and a worse prognosis. It is imperative to develop easy access and low-cost interventions to increase the screening rate. Methods: We developed a web app tailored to guide patients to seek medical consultation if they were at high risk for breast cancer or standard screening recommendations based on the predicted risk for age (https://cuccuanl.com/calcula-tu-riesgo-de-cancer-de-mama/). The web app consisted of an 8-question survey designed using object-based programming with HTML5, the additional logic was programmed on JavaScript. The program logic automatically guided patients to medical consultation if they were at high risk for breast cancer, or standard screening recommendations based on their predicted risk for age. On each confirmation page, the contact information of the hospital appeared, also, consultation at the clinic could be scheduled within the web app. The web app was distributed via social media on International Breast Cancer Awareness Day. Results: A total of 1,012 persons answered the survey after a follow-up period of two weeks. The median age of respondents was 34 years. Among participants, 10.8% were considered at very high risk for breast cancer due to symptoms, 22% were classified as high risk based on family history or more than 5 years of contraceptive use, and 19% were considered as average-risk population for whom age-based screening tests were recommended. The remaining 48% of participants were considered at low risk for breast cancer development and were directed to educational information about breast cancer awareness. Among all persons that answered our survey, 21 requested a specialized medical appointment within the web app. Conclusions: This pilot study showed a high response of Mexican population seeking breast cancer risk evaluation, 11% of women that responded to our survey had symptoms highly suggestive of breast cancer. The use of web apps can result in mass diffusion which will help reach people with less medical access, a common scenario in many developing countries. Further analysis should be made to measure the real impact on breast cancer diagnosis and oncological outcomes. This study shows how effective social media is as a means of diffusion on health topics. [Table: see text]
Interface software can markedly reduce time and improve accuracy for clinical trial data transfer from EMR to EDC: The results of two measure of work time studies comparing commercially available clinical data transfer software to current practice manual data transfer.
Authors: Thompson Maureen , Grenier Len , Alvarez Catarina , Poon Gary , Melear Jason M. 
Published: June 2022

1573 Background: Clinical studies and new drug approvals are delayed by slow data transfer and transcription errors from site entered data. These delays have been compounded by a shortage of data entry personnel at sites such that data entry approaches 10-30 days post visit. Data transfer is largely performed by manual keyboard entry from electronic medical records (EMR) into electronic case report forms (ECRF). Methods: We conducted two separate measure-of-work time studies to compare a commercially available interface software product, ProXimity to the current manual data entry. The clinical trial data from two different EMRs (ARIA and IKM G2) to an EDC (Medidata Rave). The EDC mirrored an IRB approved clinical study. Time to transfer data and error rates were the primary and secondary endpoints, respectively. For study 1 Aria EMR data from 3 subjects and 1497 data fields including demographics, vital signs, ECOG PS, physical findings, adverse events, and lab results including CBC, CMP, urinalysis, coagulation, serology were selected for visits from Screening and C2D1. For Study 2 IKM G2 data from 6 subjects and 834 data fields included demographics, vitals, and lab results. The data entry personnel were aware of the timed nature of the study. Results: Study 1 ProXimity took 13.2 min to transfer the data compared to 73.4 min for manual entry, with error rates of 0.8% compared to 3.5%, respectively. In Study 2 Proximity took 6.5 min compared to 29 min for manual entry, with error rates of 1.4% each due to non- conformant data (text). Conclusions: Software data transfer interfaces can markedly shorten the time for data entry, reduce error rates and reduce operational costs.
Market determinants of commercial prices for intravenous chemotherapy infusions.
Authors: Milligan Michael , Orav E. John , Mitchell Aaron Philip , Lam Miranda 
Published: June 2022

1585 Background: Recent price transparency legislation mandated that hospitals across the country report their individually negotiated prices with insurers. Using this data, we sought to characterize the prices paid for standard intravenous (IV) chemotherapy infusions, and determine the hospital, regional, and market factors associated with higher prices. Methods: We utilized a database of U.S. hospital-reported price transparency data to characterize prices for the most commonly billed chemotherapy drug administration Common Procedural Terminology (CPT) codes— 96413 (initial IV chemotherapy infusion) and 96415 (additional hour of IV chemotherapy). We obtained standard charges and commercial prices negotiated with private payers from hospitals that directly administer chemotherapy. To assess variation in prices, we calculated the ratio of the 90th percentile price to the 10th percentile price among private payers in each hospital and among hospitals in each Hospital Referral Region (HRR). We performed multivariable linear regressions to assess hospital, regional, and market factors associated with higher prices. Results: A total of 1,458 hospitals reported at least one price for CPT code 96413 or 96415. Hospitals reported 1 chargemaster and a median of 18 (IQR: 8–35) commercial prices negotiated with different private payers. National median commercial prices for CPT codes 96413 and 96415 were $536.00 (IQR: $326.43-$784.63) and $175.06 (IQR: $98.28-$327.25), respectively. Within each hospital, the 90th percentile commercial price was 2.2 times higher, on average, than the 10th percentile price for CPT code 96413, and 2.8 times higher for 96415. Among different hospitals within each HRR, the median commercial price at the 90th percentile hospital was 1.5 times higher than at the 10th percentile for CPT code 96413, and 2.3 times higher for 96415. On multivariable analysis, higher prices for CPT code 96413 were observed at for-profit hospitals ($215.12 higher than government not-for-profit hospitals, 95% CI: $55.22-$429.61). Higher prices for CPT code 96415 were observed at hospitals with higher predicted practice costs ($35.79 for every 1% increase in the geographical practice cost index, 95% CI: $16.69-$54.87), and a lower disproportionate share percentage ($0.96 for every 1% decrease in DSH patient percentage, 95% CI: $0.08-$19.41). Conclusions: Commercial prices for commonly billed IV chemotherapy infusions demonstrate significant variability. Prices for identical infusions vary by a factor of 2 depending on which hospital or private payer a patient selects. While prices for CPT code 96415 are largely explained by the relative cost of care, prices for the more expensive 96413 appear to be driven by the profit-status of the hospital. Further study is required to characterize the implications of such high levels of price variability on access to care and overall healthcare costs.
Telemedicine adoption and utilization among financially distressed patients with cancer during the COVID-19 pandemic: Insights from a longitudinal nationwide survey.
Authors: Hassan Abbas , Chu Carrie K. , Liu Jun , Angove Rebekah , Rocque Gabrielle Betty , Gallagher Kathleen D. , Momoh Adeyiza O. , Caston Nicole E. , Williams Courtney , Wheeler Stephanie B. , Butler Charles E. , Offodile Anaeze Chidiebele 
Published: June 2022

1596 Background: Telemedicine use during the COVID-19 pandemic among financially distressed patients with cancer, with respect to the determinants of adoption and patterns of utilization, has yet to be delineated. We sought to systematically characterize telemedicine utilization in financially distressed patients with cancer during the COVID-19 pandemic. Methods: We conducted an analysis of survey data assessing the use of telemedicine in patients with cancer during the COVID-19 pandemic collected by Patient Advocate Foundation (PAF) from May 2020 to December 2020. Primary study outcome was telemedicine utilization rate. Secondary outcomes were independent predictors of telemedicine utilization patterns, volume, and utilization preferences. Multivariate and poisson regression analyses were used to identify predictive factors. Results: Of the 1,390 respondents, 627 completed two survey waves and were included in this study. Telemedicine adoption during the pandemic was reported by 67% of patients, with most (63%) preferring video visits. Younger age (odds ratio, 6.07; 95% CI, 1.47-25.1), and higher comorbidities (odds ratio, 1.79; 95% CI, 1.13-2.65) were independent predictors associated with telemedicine adoption. Younger age (19-35 yrs.) (incidence rate ratios [IRR], 1.78; 95%CI, 24-115%) and higher comorbidities (≥3) (IRR; 1.36; 95%CI, 20-55%) were independent predictors associated with higher utilization volume. As area deprivation index increased by 10 units, the number of visits decreased by 3% (IRR 1.03, 95%CI, 1.03-1.05). Conclusions: The rapid adoption of telemedicine may exacerbate existing inequities, particularly among vulnerable financially under-resourced patients with cancer. Policy-level interventions are needed for the equitable and efficient provision of this service.
Evaluation of tumor responses and overall survival in patients with recurrent glioblastoma (GBM) from a phase IIa trial of a CMV vaccine immunotherapeutic candidate (VBI-1901).
Authors: Wen Patrick Y. , Reardon David A. , Forst Deborah Anne , Lee Eudocia Quant , Haas Brittany , Daoud Teresa , Berthoud Tamara , Diaz-Mitoma Francisco , Anderson David E , Lassman Andrew B. , Iwamoto Fabio Massaiti 
Published: June 2022

2014 Background: Cytomegalovirus (CMV) antigens have been reported in over 90% of GBMs. CD4+ and CD8+ T cells are most frequently directed against the gB and pp65 antigens, respectively, which are immunogenic targets in a CMV-based GBM immunotherapeutic. Methods: A total of 20 first-recurrent GBM patients were enrolled, with Karnofsky Performance Status of at least 70, across 2 arms of the Phase IIa extension phase to receive VBI-1901 (a gB/pp65 enveloped virus-like particle [eVLP]) adjuvanted with either GM-CSF (given intradermally) or AS01B (given intramuscularly) (NCT03382977). Patients were vaccinated with VBI-1901 every 4 weeks, with serologic immune-monitoring 2 weeks after each vaccination and surveillance brain MRI scans every 6 weeks. Results: 10 patients (6 women, 4 men) with a median age of 58 (33-67 yrs) were enrolled into the GM-CSF arm and 10 patients (3 women, 7 men) with a median age of 65 (40-67) enrolled into the AS01B arm. The 12-month OS rates for the GM-CSF and AS01B arms were 60% and 70%, respectively; the 18-month OS rate for the GM-CSF arm was 30%, and for the AS01B arm is expected to be 30%-40% (data has not yet matured). Two durable partial responses (locally determined by RANO) have been observed in the GM-CSF arm, with one patient progression-free and on protocol after 2 years with a tumor size reduction of 93% relative to start of treatment. Immunological analyses demonstrate that prolonged, monthly dosing with VBI-1901 does not lead to immunological tolerance. Dynamic boosting and loss in the peripheral blood of CMV-specific CD4 Tem cells after treatment with VBI-1901 formulated with GM-CSF may correlate with tumor responses. Conclusions: The U.S. FDA granted Fast Track Designation to VBI-1901 adjuvanted with GM-CSF in first-recurrent GBM patients, and an expansion of the ongoing trial with this formulation in this patient population, with the addition of randomization with a contemporaneous control arm, is anticipated to begin in H1 2022. Acknowledgement: GlaxoSmithKline Biologicals SA provided the AS01B adjuvant used in this study. Clinical trial information: NCT03382977.
Risk of intracranial hemorrhage with direct oral anticoagulants versus low molecular weight heparin in glioblastoma: A retrospective cohort study.
Authors: Reed-Guy Lauren , Desai Arati Suvas , Phillips Richard Edmund , Croteau Desiree , O'Neill Meghan , Albright Karen , Brem Steven , O'Rourke Donald M. , Amankulor Nduka , Bagley Stephen Joseph 
Published: June 2022

2015 Background: Glioblastoma (GBM) is associated with a high rate of venous thromboembolism (VTE), but there is little data to guide anticoagulation in GBM patients, in whom the risks of VTE must be balanced against the risk of intracranial hemorrhage (ICH). Methods: We performed a single-institution retrospective cohort study of patients with GBM diagnosed with VTE from 2014-2021 who were treated with low molecular weight heparin (LMWH) or a direct oral anticoagulant (DOAC). The cumulative incidence of ICH was compared between the LMWH and DOAC groups. The primary outcome was clinically relevant ICH within the first 30 days of anticoagulation, defined as any ICH that was fatal, symptomatic, required surgical intervention, and/or led to cessation of anticoagulation. Key secondary outcomes included clinically relevant ICH within 6 months, fatal ICH within 30 days and 6 months, any bleeding within 30 days and 6 months, and recurrent VTE within 6 months. Fisher’s exact test was used for comparison of primary and secondary endpoints between the two groups. Cumulative incidence curves were generated using the Kaplan-Meier method, and the cumulative incidence of clinically relevant ICH at both the 30-day timepoint and 6-month timepoint was compared between the DOAC and LMWH groups using the Gray test to account for death as a competing risk. Results: A total of 121 patients were identified in the primary cohort for 30-day outcome analyses (DOAC, n = 33; LMWH, n = 88). For 6-month outcome analyses, the cohort included only patients who were maintained on their initial anticoagulant (DOAC or LMWH) and did not switch anticoagulants during the 6 months following diagnosis of VTE (DOAC, n = 32; LMWH, n = 75). The cumulative incidence of clinically relevant ICH at 30 days was 0% (0/33) in the DOAC group and 9% (8/88) in the LMWH group (p = 0.11). The cumulative incidence of clinically relevant ICH at 6 months was 0% (0/32) in the DOAC group and 24% (18/75) in the LMWH group (p = 0.001), with 4 fatal ICHs in the LMWH group. Other outcomes are displayed in the Table. Conclusions: Our study suggests that DOACs are associated with a lower incidence of clinically relevant ICH in patients with GBM-associated VTE compared to LMWH. These data support the use of DOACs as a safe alternative to LMWH in patients with GBM.[Table: see text]
Mebendazole in recurrent glioblastoma: Results of a phase 2 randomized study.
Authors: Menon Nandini Sharrel , Chatterjee Abhishek , Tonse Raees , Choudhary Amit , Mahajan Abhishek , Puranik Ameya , Sridhar Epari , Pathak Shruti , Jadhav Monica , Peelay Zoya , Walavalkar Rutuja , Kumar Hemanth , Krishna Madala Ravi , Chandrasekharan Arun , Pande Nikhil , Gupta Tejpal , Banavali Shripad Dinanath , Jalali Rakesh , Patil Vijay Maruti 
Published: June 2022

2029 Background: Recurrent glioblastoma (GBM) has dismal outcomes and limited treatment options. Mebendazole (MBZ) is an anti-helminthic drug with in-vivo and in-vitro activity against glioma cell lines and has been demonstrated to be well tolerated in combination with lomustine (CCNU) and temozolomide (TMZ). In this phase 2 study, we sought to determine whether the addition of MBZ to CCNU or TMZ would improve overall survival (OS) in recurrent GBM. Methods: Adult patients with ECOG PS 0-3, with recurrent glioblastoma who were not eligible for re-radiation, were randomized 1:1 between CCNU-MBZ (n = 44) and TMZ-MBZ (n = 44). The primary endpoint was OS at 9 months, selected to reflect the BELOB trial. A 9-month OS of 55% or more in any arm was hypothesized to warrant further evaluation and a value below 35% was too low to warrant further investigation. Results: At 17.4 months, 68 events for OS analysis had occurred. The 9-month overall survival was 36.6% (95%CI 22.3-51) and 45% (95%CI 29.6-59.2) in the TMZ-MBZ and CCNU-MBZ arms respectively. ECOG PS was the only independent prognostic factor impacting OS (HR-0.478 95%CI 0.268-0.851; P = 0.012). Twenty-three patients (28.6%) enrolled had an ECOG PS 2-3 with inferior outcomes (median OS-5.67, HR-2.092 95%CI 1.175-3.731). Analysis restricted to ECOG PS 0-1 (n = 65) patients revealed a 9-month OS of 39.6% (95% CI 22.4-56.3) and 57.9% (95% CI 38.7-73) in TMZ-MBZ and CCNU-MBZ arms respectively. Grade 3-5 adverse events were seen in 8 (18.6%; n = 43) and 4 (9.5%; n = 42) patients in the TMZ-MBZ and CCNU-MBZ arms respectively. Conclusions: The addition of MBZ to TMZ or CCNU failed to achieve the pre-set benchmark of 55% 9-month OS. This was probably due to 28.6% of patients with poor PS of 2-3. In patients with ECOG PS 0-1, CCNU-MBZ had a 9 month OS of 57.9% and needs to be evaluated further. Clinical trial information: CTRI/2018/01/011542.