bluuebunny/crossref_metadata_2025_split huggingface dataset:
Impact of an oncology clinical pharmacist specialist in an outpatient multiple myeloma clinic.
Authors: Virani Amin , Schlei Zach , Jakubowiak Andrzej J. , Jasielec Jagoda , Gleason Christine , Ackermann Megan , Wolfe Brittany , Major Sarah , McIver Amanda , Parsad Sandeep 
Published: May 2020

e14030 Background: Improvements in cancer treatment, supportive care, and the approval of oral chemotherapy medications over the past decade have resulted in an increasing number of cancer patients treated in outpatient settings. Transitioning cancer treatments to the outpatient setting places greater emphasis on proper medication counseling and optimal side effect management. Current literature demonstrates improvements in medication adherence and effective cancer related symptom management with the addition of an oncology pharmacist. Historically, the University of Chicago Medical Center (UCMC) has not employed pharmacists into their ambulatory oncology clinics. UCMC is evaluating pharmacist’s roles in these clinics. Methods: The primary objective of this project is to evaluate the clinical and financial impacts of an oncology clinical pharmacist specialist in an interdisciplinary multiple myeloma (MM) clinic. This will be evaluated by monitoring the interventions made by the pharmacist in clinic through a validated scoring tool. This tool associates a value for each type of intervention made based on current literature and internal evaluations at UCMC. The oncology clinical pharmacist specialist will be available for consult by the MM clinic staff. The pharmacist may be consulted for any medication related inquiry. Based on the consult the pharmacist will categorize their interventions into twelve predefined intervention categories. Results: Study results showed the implementation of a clinical pharmacist specialist into the MM clinic over 39 clinic days resulted in 241 patient consults and 474 interventions made by the pharmacist. The most frequent interventions made by the pharmacist were medication teaching (97), dose adjustments by pharmacist (82) and medication reconciliation (63). Based on the dollar values associated with each intervention type, the value of interventions made by the pharmacist during the study period was $189,441 with a predicted annual value of $757,764. Conclusions: An clinical pharmacist specialist in the MM clinic lead to dramatic and sustainable financial and clinical impacts. Further investigation into other oncology clinics is warranted.
Early nutrition intervention in cancer patients: A systematic review and meta-analysis.
Authors: Batukbhai Bhavina D O , Champagne Madeleine , Rai Karan , Barrett-Campbell Odeth , Riblet Natalie 
Published: May 2020

e14037 Background: Malnutrition is a common finding seen in 50-80% of cancer patients. Malnutrition negatively impacts quality of life (QOL), treatment-related outcomes, and survival. While early nutrition interventions (ENI) may be beneficial in preventing malnutrition, they are not part of routine cancer care. We conducted a systematic review and meta-analysis to assess the benefits of ENI compared to standard of care (SC) on nutritional status, QOL, and survival in patients with newly diagnosed cancer. Methods: We searched MEDLINE, EMBASE, Cochrane, and CINAHL databases from inception through October 2019 to identify randomized control trials (RCT) and cohort studies comparing ENI to SC in adult patients with newly diagnosed oncologic malignancies. We required that nutrition interventions began within 8 weeks of diagnosis and lasted at least 6 weeks. Outcomes of interest included nutritional status (change in weight or BMI), mortality, and QOL. We assessed for risk of bias among included studies using the Cochrane Risk of Bias tool (RCT) and the Newcastle-Ottawa scale (cohort studies). We summarized change in weight (kilogram, kg) at 3 and 6 months using standardized mean differences (SMD) and 95% Confidence Intervals (CI). Because some studies had insufficient data on weight to allow for quantitative analysis, we summarized their findings qualitatively. We used a qualitative approach to summarize QOL. Mortality at 1 and 2 years was compared using relative risk (RR) and 95%CI. Random effects models were used to pool data as there was meaningful and statistically significant heterogeneity (I2 > 50%; p-value < 0.1). Results: 2,781 studies were identified and screened by two independent reviewers for eligibility. 18 independent studies (subjects = 1936) met inclusion criteria. Among 9 studies included in the quantitative analysis for nutritional status, patients who received ENI had better nutritional status compared to SC (SMD at 3 months 0.49kg, 95%CI 0.08-0.79; at 6 months: 0.27kg, 95% CI 0.09-0.45). ENI was also associated with a lower risk of death compared to SC, although the findings were not significant (1 year RR: 0.41[95% CI 0.09-1.81]; 2 year RR: 0.79 [95 CI 0.58-1.09]). Of the ten studies reporting on QOL, six found that patients in the ENI group had improved QOL outcomes. There was insufficient data available to perform subgroup analysis based on cancer type. The quality of studies was generally good and 11 of the included studies had low risk of bias. Conclusions: Patients with newly diagnosed cancer may benefit from ENI due to better weight outcomes, QOL, and survival.
Utilization of online crowdfunding for alternative cancer treatments at home and abroad.
Authors: Gruhl Joshua David , Peterson John Stuart , Davis Sydney , Olsen Jaxon Carey , Parsons Matthew , Kann Benjamin H , Stoddard Gregory J. , Johnson Skyler B 
Published: May 2020

e14044 Background: The use of alternative cancer treatments has been associated with decreased survival. However, little is known about the types of individuals who seek out these therapies, the alternative therapies pursued, and the associated costs. We utilized GoFundMe campaigns to characterize the types of patients who sought alternative cancer treatments, the types of therapies pursued, and the associated costs. Methods: We queried GoFundMe ( www.gofundme.com ) for English language campaigns using the term “alternative cancer treatment” using custom code for web scraping on 10/25/2019. We identified 1000 campaigns between 2011-2019, of which, 795 had received donations and were used for analysis. We studied each individual campaign in detail and extracted relevant information. Results: The majority of patients were female (63.5%). The most common cancer types were breast (25.3%), colorectal (10.8%), and lung (5.5%) cancer. Of patients reporting cancer stage, 79.3% had stage IV disease. Of those reporting prior cancer treatment history, 34.8% had never undergone traditional cancer treatment; 42.1%, 46.8%, and 26.6% had undergone prior surgery, chemotherapy, and radiotherapy, respectively. The most common proposed alternative treatments were vitamins and minerals (23.4%), herbs and botanicals (16.1%), special diets (13.0%), supplements (10.8%), heat or light therapy (7.8%), IV infusions (7.8%), homeopathic/naturopathic therapies (7.2%), and hyperbaric oxygen therapy (5.9%). Among all campaigns, a total of $36,394,110 was requested and a total of $8,601,759 (23.9%) was raised. The median campaign fundraising goal was $25900 (US dollars; Interquartile range [IQR] $1000 – $50000); the median amount donated was $5805 (IQR $2595 – $12580). These costs include travel as 629 (79.1%) campaigns were for patients residing in the USA, 70 (8.8%) in Europe, 62 (7.8%) in Canada, and 34 (4.3%) in other regions, from which, 54.3% of patients stated that they planned to travel internationally—most commonly to Mexico—for their alternative cancer treatments. Conclusions: Millions of dollars have been requested and raised between 2011 and 2019 for alternative cancer treatments. The majority of patients sought treatment at alternative clinics internationally, were females, had stage IV disease and primary tumors of the breast, colon/rectum or lung, who had previously undergone traditional cancer therapies, and highlight a group of patients where improved communication/education between providers and patients appears to be needed.
Clinical utility of comprehensive genomic pathway and integrated network analyses in personalized oncology.
Authors: Sahajpal Nikhil Shri , Mondal Ashis , Ahluwalia Meenakshi , Njau Allan Njoroge , Kota Vamsi , Okechukwu Nwogbo , Jackson Gretchen Purcell , Brotman David , Weeraratne Dilhan , Jillella Anand P. , Snowdon Jane , Kolhe Ravindra 
Published: May 2020

e14051 Background: Adoption of next-generation sequencing (NGS) technology in routine clinical practice has enabled the detection of genetic aberrations such as single nucleotide variants, copy number alterations, and gene fusions. Pathway and network analyses (PNA) are key components for evaluation of NGS data in a clinical setting to explain findings involving thousands of altered genes and proteins with a smaller and more interpretable set of altered processes. Though PNA have been applied to identify driver genes and pathways in cohort-based analyses, its application in precision oncology remains unexplored. We investigate the potential utility of the Watson for Genomics (WfG) pathway analyses tool in interpreting complex and multiple genomic alterations in individual cancers. Methods: DNA and RNA isolated from 70 patient tumors across 30 different cancer types were processed with Illumina’s TST170 NGS platform. WfG’s feature of pathway analyses was used to identify gene variants, signaling pathways, networks, and the drugs targeting these alterations based on evidence in the clinical literature and FDA drug databases. Results: Analyses defined 5 different pathway/network models: 1) downstream therapeutic targets, 2) synthetic lethality, 3) combinatorial downstream targets + synthetic lethality, 4) two or more pathways converging to downstream targets, and 5) complex profile analyses. The five PNA models are illustrated by the following unique cases. 1) A thyroid cancer case with HRAS variant and activated RAF1 downstream pathway showed MAPK1/3 were suggestive of relevant targets. 2) An acute myeloid leukemia case with BRCA1, BRCA2 and PTEN variants, targeting a common synthetic lethal partner PARP1 was ideal for therapy. 3) A penile carcinoma case with BRAF, CDKN2A and TP53 variants, targeting the BRAF downstream pathway in combination with either CDKN2A or TP53 were the likely choice for therapy. 4) A glioma case with activated PI3K and MEK downstream pathway, targeting a common downstream marker would block both pathways. 5) A breast carcinoma case with a complex pathogenic variant profile provided relevant clinical information and levels of evidence for multiple drug targets. Conclusions: We discovered that the integrated WfG pathway analyses tool is ideal for visualization of the variants with levels of evidence from clinical literature and FDA drug databases that can help inform treatment options and provides a holistic understanding of a specific tumor profile allowing the treating clinician to select personalized targeted therapy.
Computational pathological identification of prostate cancer following neoadjuvant treatment.
Authors: Dogdas Belma , Kanan Christopher , Raciti Patricia , Tian Shaozhou Ken , Brookman-May Sabine Doris , Wetherhold Lisa , Smith Andressa , Rooney Oliver Brendan , McCarthy Sharon Anne , Alvarez John David , Lopez-Gitlitz Angela , Casson Adam , Godrich Ran , Kunz Jeremy D. , Ceballos Rodrigo , Leibowitz Carla , Grady Leo , Fuchs Thomas J. 
Published: May 2020

e14052 Background: The need for accurate pathological identification and quantitation of prostate cancer (PC) following neoadjuvant treatment with androgen deprivation therapy (ADT) and androgen receptor antagonists is increasing as PC treatment continues to evolve. In clinical practice, pathological assessment of residual tumor is a tedious and time-consuming process due to the volume of tissue from radical prostatectomy (RP). In addition, neoadjuvant treatments can greatly alter both benign and neoplastic prostate tissue morphology making the pathology assessment difficult for even specialized pathologists. Paige Prostate 1.0 is a clinical-grade artificial intelligence (AI) system for PC detection. It was trained and evaluated in over 50,000 prostate biopsy slides with validation across more than 800 institutions worldwide using multiple slide scanners. Methods: We evaluated the performance of Paige Prostate 1.0 at identifying prostatic tumor on 64 hematoxylin and eosin stained slides exhibiting neoadjuvant treatment effect from apalutamide, enzalutamide, and/or ADT. Results: Analysis of the receiver operating characteristic curve demonstrated an area under the curve of 0.96. Using the Paige Prostate 1.0 operating point, it achieved a sensitivity of 91% and a specificity of 94%, corresponding to the correct identification of challenging treated morphology in 59/64 slides using expert pathologists as the reference. False negative cases were typically represented by atypical small acinar proliferation that required expert pathological consensus confirmation. Conclusions: To our knowledge, this is the first AI based evaluation of residual disease in PC with hormone neoadjuvant therapy. Paige Prostate 1.0 effectively identified tumor despite treatment effects. Future work will include optimization of Paige Prostate 1.0 by training with RP specimens from a larger cohort of appropriate samples, as well as precise measurement of residual tumor burden to further improve its accuracy and reproducibility. Paige prostate residual disease detection 1.0 has the potential to impact emerging clinical practice at the patient level and to complement the pathological assessment of RPs in global phase 3 clinical trials, such as PROTEUS, in a standardized, reproducible, and robust way.
Phenotyping clinical trial treatment regimens in cancer: An integrative ontology, artificial intelligence and knowledge engineering approach.
Authors: Mai Yun , Lee Kyeryoung , Liu Zongzhi , Li Zhiqiang , Jones Scott , Ma Meng , Fink Marc , Ghosh Krish , Chen Rong , Schadt Eric , Wang Xiaoyan 
Published: May 2020

e14054 Background: Matching clinical attributes (i.e. indications, lab tests, treatment regimens) of clinical trial eligibility criteria with real world patient data is extremely challenging. Attribute phenotyping is one of the key components of Trial2Patient, a customized system developed by Sema4 to find patients for clinical trials. Transforming treatment regimens to a standard ontology and encoding drugs with standard nomenclatures will facilitate the semantic retrieval of treatments mentioned in clinical trial criteria. This will also enable the interoperation between different data sources that is often required for fast-learning and scalable healthcare information system. Methods: Free text containing treatment regimen/medication terms were extracted and preprocessed from three sources: 1) clinical trials listed in a commercial database citeline.com, 2) clinical trials listed in clinicaltrials.gov, and 3) National Comprehensive Cancer Network (NCCN) Guidelines. The regimen terms such as neoadjuvant therapy for non-small cell lung cancer, checkpoint inhibitor, EGFR inhibitor, androgen deprivation therapy (ADT), among many others, were profiled by AI methods (i.e. pattern reorganization and rule-based) and knowledge engineering via Sema4’s in-house knowledge base (CAV), Pharmaprojects in citline.com and NCCN Guidelines. The drugs related to each regimen were identified and mapped to RxCUI via RxNorm ontology. Results: We identified 76 regimen terms for non-small cell lung cancer (NSCLC), small cell lung cancer (SCLC) and prostate cancer (e.g. PD-L1 ≥1% nonsquamous NSCLC, bone antiresorptive therapy for M1 castration resistant prostate cancer), and 14,476 drug-category pair (e.g. pembrolizumab is a PD-1 inhibitor, pembrolizumab is used as the third line and beyond systemic therapy for M1 CRPC). All drugs identified were mapped to RxCUI for real world patient matching. Conclusions: This approach systematically extracted and normalized regimens and medications mentioned in clinical trials in NSCLC, SCLC and prostate cancer to standard codes. These standardized data can be used in mapping treatment histories of a patient to the eligibility criteria for clinical studies or for identifying studies relevant to a patient. The outcome of profiling cancer treatment regimens through standard ontology RxNorm may be particularly valuable in cancer studies based on real-world evidence.
Impact of augmented intelligence (AI) on identification and management of depression in oncology.
Authors: Frownfelter John , Blau Sibel , Zettler Marjorie , Miller Kelly , Kish Jonathan , Valley Amy W. , Gajra Ajeet 
Published: May 2020

e14059 Background: Depression is common in patients with cancer and is associated with worse cancer treatment outcomes. Depression is often underdiagnosed/treated as cancer clinicians are focused on the complex aspects of therapy and care coordination. AI has a potential application in the identification of patients at high risk for depression. Jvion has developed a prescriptive analytics solution (the Machine), which uses AI algorithms and machine learning techniques applied to combined clinical and exogenous datasets to identify patients with a propensity for poor clinical outcomes. The Machine was applied to depression risk (within next 6 months), and recommended patient-specific, dynamic, and actionable insights. While the Machine requires no additional documentation within the electronic health record (EHR) to generate its insights, those insights can be integrated back in to any EHR. Herein, we report the results of a pilot study evaluating the impact of AI-driven insights on depression screening and management at a single oncology practice. Methods: All patients were scored weekly using the Machine depression vector. The Machine risk-stratified the patients and generated recommendations for the provider to consider as they developed a care plan. Patients identified as “at risk” by the Machine were assessed for depression (PHQ-9) by the clinical team regardless of prior screening results. The rate per 1000 unique patients per month (PPM) of depression screenings, case management evaluations, and antidepressant prescriptions were calculated for the 5 months prior to and 17 months post deployment of the Machine in the practice. Results: The oncology practice has 21 providers managing an average of 4329 unique PPM. The mean rate of depression screenings increased from 6.0 per 1000 PPM pre- deployment to 16.2 per 1000 PPM post deployment (+271%). The downstream workflow outcomes of case management evaluations increased from 11.6 to 21.4 per 1000 PPM (+184%) and antidepressant prescriptions increased from 9.2 to 15.5 per 1000 PPM (+168%) pre and post-implementation respectively. The providers reported high satisfaction with the use of the AI solution in depression screening. Conclusions: This oncology practice found deployment of the Jvion AI solution to be feasible. The Machine-generated insights for depression risk were actionable, could be incorporated into workflow, and increased the number of patients identified. If confirmed in larger studies, AI-driven insights may improve the identification and management of depression in patients with cancer.
A mixed methods study for the evaluation of a digital health solution for cancer multidisciplinary team meetings using simulation-based research methods.
Authors: Gardner Clarissa , Ghafur Saira , Fontana Gianluca , Guo Chaohui , Prime Matthew Stewart , Ashrafian Hutan 
Published: May 2020

e14063 Background: Simulation-based research (SBR) methods involve setting up structured scenarios to replicate real-world situations, with the aim of eliciting real-world reactions and behaviours. SBR is useful for the evaluation of new healthcare solutions without compromising patient safety or navigating complex ethical review processes. However, there are few trials using SBR methods for the evaluation of digital health interventions (DHIs), the adoption of which has been hindered in the NHS due to a lack of evidence-base for their efficacy. SBR methods could be an appropriate tool for testing digital solutions. Methods: The Institute of Global Health Innovation (IGHI) developed a series of simulated lung cancer multidisciplinary team (MDT) meetings to test the NAVIFY Tumor Board solution by Roche Diagnostic Information Solutions, a digital solution for the preparation and conduct of cancer MDTs. To provide an environment for participants to evaluate the capabilities of the NAVIFY Tumor Board solution, 10 simulation sessions were organised in which groups of five to six clinicians were recruited to discuss up to 10 mock patient cases across two simulated MDTs, first using standard tools commonly used to conduct MDTs and then using the NAVIFY Tumor Board solution. The cases were developed by the study team at IGHI and consultants in respiratory medicine and oncology. 56 healthcare professionals (respiratory physicians, oncologists, radiologists, histopathologists, clinical nurse specialists and thoracic surgeons) were recruited. The sessions were video recorded and observations were noted by the study team, followed by a focus group in which participants provided feedback about their experience of the simulated MDTs. Results: Through this study we were able to generate evidence and multi-professional recommendations for Roche regarding the functionality, usability and applicability of the solution in the NHS, as well as beneficial features and those which could be improved. Participants reported that the simulations were realistic and a meaningful way of conducting evaluations of digital health solution without impacting clinical practice or patient safety. Conclusions: This study demonstrated the utility and validity of testing commercial tools in simulated settings to generate meaningful insights from endusers. SBR methods were shown to be an acceptable way for frontline practicing clinicians to participate in the testing and development of digital health tools in a standardised setting.