bluuebunny/crossref_metadata_2025_split huggingface dataset:
Predictors of electronic patient-reported outcomes use in the survivorship setting.
Authors: Thom Bridgette , Corcoran Stacie , Lavery Jessica A. , Sarpong Leon , Woodside Alexandria , Korenstein Deborah 
Published: May 2020

e14038 Background: Patient-reported outcomes (PRO) offer insight into patient perception of health and symptom burden. Despite a shift toward electronic PRO (ePRO), optimal administration methods are unclear. Our institution recently began ePRO collection in survivorship clinics: patients are invited via email to complete a health survey on our online patient portal prior to annual visits, enabling clinician review of symptoms in advance of the visit. Patients who do not complete an ePRO survey at home are offered an iPad or paper survey at visit check-in. In the first year of ePRO, 87 patients inadvertently submitted multiple responses to the questionnaire, across two modalities. This study aimed to 1) assess determinants of ePRO completion across modalities (portal, iPad, paper); and, 2) among patients who submitted multiple surveys, compare consistency of responses in surveys completed within 30 days of each other. Methods: We reviewed records for 10194 patients seen in breast, thoracic, colorectal, and gynecologic survivorship clinics over one year. Demographics, disease/treatment details, and PRO responses (symptoms, health behaviors, etc.) were extracted. For aim 1, we used multivariate regression to determine predictors of completion method. For aim 2, we calculated Cohen’s kappa coefficients to compare responses based on completion modality. Results: Most patients (65.6%) completed the survey on an iPad in clinic; 16.7% on the portal, 17.7% on paper in clinic. Younger age (p < .001), white race (p < .001), less fatigue (p = .01), and English as primary language (p < .001) were associated with portal use in multivariate analyses. In general, Cohen’s Kappa analyses revealed high agreement between surveys. Conclusions: Our findings highlight demographic gaps in ePRO acceptance. Although most patients completed an ePRO (portal or iPad), few completed it at home in advance of their visit, which has implications for clinic flow and clinician preparation for visits. However, our finding of consistent symptom reporting across mode and location of completion is reassuring. Future work should seek to improve comfort with ePRO completion at home among groups less likely to accept it and explore the implications of symptom burden on ePRO acceptance.
Multistate Markov modelling of long-term outcomes in immunotherapy for metastatic renal cell carcinoma (mRCC) using machine-learned dynamic Bayesian networks.
Authors: Gupta Alind , Brenner Darren , Arora Paul 
Published: May 2020

e14049 Background: Patient-level heterogeneity in response to treatment remains a major challenge in cancer immunotherapy. Long-term individual-level modelling of survival, tumour response and safety outcomes jointly can help improve efficacy and durable clinical benefits. Machine-learned Bayesian networks provide a solution to the “black box” problem of other machine-learning approaches. Objectives: To develop a dynamic Bayesian network model for multivariate risk prediction, survival modelling and long-term simulations of immunotherapy patients using a simulated trial dataset. Methods: A simulated randomized clinical trial dataset for second line immunotherapy of patients with renal cell carcinoma was used for analysis. We machine-learned a dynamic Bayesian network from censored data with incorporation of prior clinical knowledge. Classification performance, probability calibration, goodness-of-fit metrics and prognostic variables were calculated following TRIPOD guidelines. Results: The machine-learned graphical model encoded expected relationships between variables with minimal prior information. Visual and numerical goodness-of-fit checks for survival extrapolations showed that the model fitted data well and was appropriate. Probability calibration was < 10% from ideal. Classification performance for overall survival was high ( c-statistic ~0.85) soon after treatment initiation and gradually plateaued over time. Prognostic variables were calculated by treatment arm for overall survival and severe adverse events. Conclusions: Dynamic Bayesian network model was useful for transparently representing joint relationships between all variables in the trial dataset, for multivariate risk prediction and long-term extrapolations that can be used in economic evaluations for health technology assessments in oncology.
Electronic patient-reported outcomes (ePROs) and machine learning (ML) in predicting the presence and onset of immune-related adverse events (irAEs) of immune checkpoint inhibitor (ICI) therapies.
Authors: Iivanainen Sanna , Ekström Jussi , Kataja Vesa V , Virtanen Henri , Koivunen Jussi 
Published: May 2020

e14058 Background: ICIs have introduced novel irAEs, arising from various organ systems without strong timely dependency on initiation and discontinuation of the therapy. Early detection of the irAEs could result in improved safety profile of the treatment and better quality of life for patients. Symptom data collected by ePROs could be used as an input for ML based prediction models for early detection of irAEs. Methods: The utilized dataset consisted of two data sources. The first dataset consisted of 16 540 reported symptoms from 33 ICI-treated cancer patients, including 18 monitored symptoms collected using Kaiku Health digital platform. The second dataset included prospectively collected irAE data, including initiation and end dates, CTCAE class, and severity of 26 irAEs (the longest irAE lasted 799 days, and the shortest two days while median duration was 61 days). Two ML models were built using extreme gradient boosting, a well-known classification algorithm. Using the ePRO data, the first model was trained to detect the presence and the second model to detect the onset (0-21 days prior to diagnosis) of irAEs. The dataset was split into training (70 % of the data) and test sets (30 % of the data) by random allocation. The test set was left out from the model training and tuning, and was used only to evaluate the model performance. Results: The model trained to predict the presence of irAEs had an excellent performance with the test dataset. The prediction of the irAE onset was more difficult, but the model performance was still at a very good level. The performance metrics for the ML models are presented in Table. Conclusions: Current study suggests that ML based prediction models, using ePRO data as input for the models, can predict the presence and onset of irAEs with high accuracy. Thus, it indicates that digital symptom monitoring combined with ML could enable the detection of irAEs in ICI-treated cancer patients. The results should be validated with a larger dataset from prospective clinical trials. Clinical trial information: NCT03928938. [Table: see text]
Machine learning for prospective identification of immunotherapy related adverse events (irAEs).
Authors: Margalski Daniel , Lycan Thomas , Rajendran Suraj , Topaloglu Umit 
Published: May 2020

e14064 Background: The ubiquitous implementation of immunotherapy has significantly improved outcomes in the treatment of cancer patients; however, once rare adverse events from these therapies have increased in lock step. We now face an increased burden of identification on providers with limited experience in the diagnosis of irAEs. We use machine learning to develop prediction models that will aid providers in identifying patients at high risk for developing irAEs as well as for multiple downstream applications. Methods: We have manually extracted progress notes from 462 patients with non-small cell lung cancer treated with immunotherapy who had known irAEÕs, with focus on pneumonitis, colitis, and rash; the most common symptomatic irAEÕs. Labels were applied by clinician review to train the machine learning algorithm to identify the predictive signals at the earliest stage of recognition possible. As a standard Natural Language Processing method, we cleaned the notes to standardize punctuation, numbers and special characters. Next, we created a word embedding matrix utilizing word2vec as well as Google News Vector. Finally, we implemented a Convolutional Neural Network (CNN) on the Microsoft Azure Databricks platform. Due to class imbalance, we deployed a Synthetic Minority Over-sampling Technique algorithm as a correction. We prioritized F1 score in the analysis given the heterogeneity of the data, but will present accuracy, precision and recall as well. Results: We trained our CNN with 10 epochs resulting in an F1 of 0.428, accuracy of 0.895, precision of 0.75 and recall of 0.3. There was no significant difference in results between the word embedding matrices. Conclusions: Using machine learning, we created an algorithm for irAE prediction that was accurate but lacked recall. This will serve as the foundation of implementations including the creation of a clinical decision support tool to guide focused and appropriate treatment of the unique toxicity of irAEs. Although informative as a starting point, this model had a final F1 score that was lower than expected presumably due to class imbalance of input data and the temporal nature of progress notes, which limits the utility of a CNN. Future iterations of the algorithm will include supplementary documentation and implement recurrent neural networks with long short-term memory architecture to address these limitations.
Evaluation of a regularly updated knowledge base for curation of somatic mutations detected in whole exomes of melanoma and lung, colorectal, and breast cancers.
Authors: Yaung Stephanie J. , Li Jian , Pek Adeline , Niu Lili , Palma John F. , Schmid Maximilian 
Published: May 2020

e14072 Background: Evolving medical guidelines and complex multi-variant data from next-generation sequencing (NGS) testing of cancer samples make routine clinical interpretation of somatic variants challenging. We assessed the ability of NAVIFY(R) Mutation Profiler*, a CE-IVD somatic variant interpretation tool, to yield accurate time- and geography-specific clinical content on 2511 samples from The Cancer Genome Atlas (TCGA) across six solid tumor types. Methods: Whole exomes from lung adenocarcinoma (n = 469), lung squamous cell carcinoma (n = 325), colon adenocarcinoma (n = 368), rectum adenocarcinoma (n = 149), breast invasive carcinoma (n = 806), and skin cutaneous melanoma (n = 394) cases were analyzed. We utilized TCGA data from the Multi-Center Mutation Calling in Multiple Cancers (MC3) project to obtain consensus calling results of single nucleotide variants and indels. The open-access Mutation Annotation Format (MAF) file (v0.2.8) that stores variant calls was lifted to human reference genome GRCh38 and converted to individual Variant Call Format (VCF) files per case. VCF files were uploaded to NAVIFY Mutation Profiler to interpret actionable mutations according to a highly curated and up-to-date knowledge base (Roche Content v2.13.0 released December 6, 2019). We further assessed the accuracy of interpreting co-occurrences of actionable mutations. Results: Over 1.24 million somatic mutations across 20,590 genes were assessed with NAVIFY Mutation Profiler, which reported tier classifications of variants based on consensus recommendations from AMP, ASCO, CAP, and ACMG. 86% of cases had variants of strong (Tier I-A or I-B) or potential (Tier II-C or II-D) clinical significance; 56% of these cases had Tier I classifications, supported by robust clinical evidence. Potentially actionable variant-variant interactions were found in 14% of cases. The tool also identified appropriate tier classifications by geographic region in accordance with local medical guidelines. Conclusions: To benchmark against other tools, we utilized available exome data from TCGA MC3 to assess NAVIFY Mutation Profiler. While this study likely underestimates the fraction of cases with actionable mutations, given that copy number alterations or rearrangements are also present in TCGA samples, we found a higher yield of potentially actionable annotation than other published methods. * This product has not been evaluated by the Food and Drug Administration and is not commercially available in the United States.
Leveraging clinical pathways to identify patient-reported clinical trial enrollment barriers.
Authors: McKinney Mishellene , Stefaniak Nessa , Samborski Cynthia , Murphy Monica L. , Edge Stephen B. , Attwood Kristopher , Liu Lu , Eakle Katherine , Lash Stephen , Yu Wei 
Published: May 2020

e14086 Background: Only about 3% of adult cancer treatment is within a clinical trial (CT). Patient personal factors may impact willingness to participate. We leveraged CTs embedded in Clinical Oncology Pathways (COP) to examine patient barriers to CT participation. Methods: A COP program used in an academic cancer center embeds CTs that are open for accrual at the center. Providers must either select the CT to alert the research team for patient screening for the CT or provide a reason for bypassing the CT before the COP presents standard care options. This study includes patients with solid tumors receiving systemic therapy for whom a CT is presented in the COP and the provider documented that the patient was “not interested in this trial”. Prior to being offered the survey, these patients were asked if they had been interested in participating in a CT and were considered ineligible for the survey if they said “yes”. The survey was comprised of 29 Likert, demographic, multiple choice and open-ended questions. We categorized patients’ reasons for declining to enroll in a CT, and attitudes about CTs. Results: Among 1108 patients with CTs presented to the provider in COPs from May 2018 to June 2019 there were 164 identified by the provider as not interested in a CT. 23 were deceased, and 54 were not able to be contacted. 57 were otherwise ineligible. Of these, 33% did not recall being offered a CT. Of the 30 patients approached, 9 declined and 21 completed the survey (70% participation). Cancer types included gastrointestinal (33%), breast (19%), non-small cell (14%), pancreas (14%) and others (20%). 66% were female and 80% had metastatic cancer. Age ranged from 41 to 69 years (median 66). Race/ethnicity was Caucasian (81%), African American (10%), American Indian/Alaskan Native (5%) and non-specified (5%). When asked, “How do you view being asked to participate in cancer research, 76% saw it as “a positive thing”. Patient primary concerns were desire for more information on study outcomes (42%), concerns about additional appointments or tests (28%) and concerns about receiving a placebo (23%). Conclusions: There is a gap between patients’ positive views of clinical trials and their willingness to participate. This suggests that barriers identified are modifiable. Many who initially decline CTs may be open to joining a clinical trial with further education.Interventions such as the Meropol et al Preparatory Education About Clinical Trials (J Clin Oncol 2015:34:469) should be considered for broad implementation.
Professional medical writing assistance in oncology clinical trials.
Authors: Abi Jaoude Joseph , Kouzy Ramez , Mainwaring Walker , Lin Timothy , Miller Austin B. , Jethanandani Amit , Espinoza Andres F. , Taniguchi Cullen M. , Ludmir Ethan B. 
Published: May 2020

e14088 Background: Academic publishing remains the cornerstone of biomedical research and scientific communication. Researchers often assign professional medical writers (PMWs) to craft publications, as these individuals may improve the overall writing quality and/or reduce time to publication. We sought to characterize the landscape of PMW utilization in phase III cancer clinical trials. Methods: We searched ClinicalTrials.gov for phase III randomized clinical trials between the years 2003 and 2018. Randomized multi-arm trials assessing a therapeutic intervention in cancer patients were included. After identifying the corresponding publications, we identified whether a PMW was involved in writing the manuscript based on author disclosures, along with any related funding information. Results: Six-hundred oncology RCTs with corresponding manuscripts were identified. In total, 260 (43.3%) trials used a PMW. Financial support to medical writers was largely provided by the industry (247/260, 95.0%). In multivariate analyses, PMW utilization was higher among industry-funded trials compared to non-industry-funded trials (OR: 14.2, p= 0.001). PMWs were used more frequently to report successful trials that met their primary endpoint (OR: 1.8, p= 0.03) but did not differ between English-speaking and non-English-speaking countries ( p= 0.19). Cooperative group trials used PMWs with less frequency compared to non-cooperative groups (OR 0.68, p < 0.001). PMWs were used more often in trials that led to subsequent drug FDA approval (69.6% vs 40.6% in trials that did not lead to subsequent FDA approval), but this association did not reach statistical significance in multivariate analysis ( p = 0.744). The use of PMWs has increased significantly over time (OR: 1.11/year, p =0.001). Conclusions: In this analysis, we show a strikingly high prevalence of PMW utilization in phase III oncology trials, with a vast majority of industry-supported studies using PMWs. We believe that professional writing assistance plays an important role in clear and efficient scientific communication. However, the disproportionate role of PMWs in reporting positive, industry-funded trials may represent a conflict of interest. We urge continued and increased reporting of utilization and funding of professional writing assistance in cancer clinical trials.
Associations between concordance with oncology clinical decision support and clinical outcomes in lung cancer patients.
Authors: Suwanvecho Suthida , Suwanrusme Harit , Jirakulaporn Tanawat , Lungchukiet Palita , Thanakarn Nittaya , Taechakraichana Nimit , Decha Wimolrat , Boonpakdee Wisanu , Wongrattananon Pattanawadee , Preininger Anita M , Solomon Metasebya , Wang Suwei , Hekmat Rezzan , Esquivel Jaime , Dankwa-Mullan Irene , Patel Vimla L , Shortliffe Edward , Arriaga Yull Edwin , Jackson Gretchen Purcell , Kiatikajornthada Narongsak 
Published: May 2020

e14114 Background: Watson for Oncology (WfO) is an artificial intelligence-based clinical decision-support system which provides therapeutic options and associated scientific evidence to cancer-treating physicians. Oncologists at Bumrungrad International Hospital (BIH) have used WfO since 2015. We examined the association between concordance of WfO therapeutic options and BIH treatment decisions with short-term clinical outcomes for lung cancer patients. Methods: This study included lung cancer patients seen at BIH for treatment and follow-up care and for whom WfO was used from 2015 to 2018. Charts were reviewed for concordance with WfO, documentation of disease progression, response to treatment, and survival. We evaluated concordance between oncologists’ treatments and therapeutic options listed as “recommended” by WfO. We evaluated association between WfO concordance and partial or complete response rates over a 24-month period by comparison of proportions with odds ratio. Progression-free survival (PFS, time from diagnosis until progression or death) was evaluated by Kaplan-Meier log-rank test. Results: Seventy-nine lung cancer patients were included. We identified a trend towards higher response rates in concordant cases (59.2%, N = 32), as compared to discordant (48.0%, N = 12), with an odds ratio of 1.56 (see table). There was not a significant difference in PFS between concordant and discordant cohorts. Conclusions: In this small-cohort, retrospective study, lung cancer patients receiving treatments that are concordant with WfO recommended therapeutic options trended towards higher response rates than patients with discordant treatments. Use of a clinical decision-support system may help support cancer-treating physicians in delivering best practice and evidence-based care that may improve short-term outcomes. Prospective studies with larger samples and other cancer types are underway. [Table: see text]
Using multiple perspectives analysis to propose state cancer control policy in Abia State, Nigeria.
Authors: Eguzo Kelechi Ngozi , Ekanem Uwemedimbuk Smart , Chukwuemeka Oluoha , Nnah Kingsley Kelechi , Olatunbosun Olufemi , Muller Andries , Walker Vivian , Mpofu Chris , Ramsden Vivian R 
Published: May 2020

e14132 Background: Beyond the National Cancer Control Plan, most States in Nigeria do not have State cancer control policies. Using the multiple perspectives analysis framework, this research sought to explore the perspectives of patients diagnosed with cancer, healthcare providers and health policymakers regarding cancer policy in Abia State. Methods: A concurrent mixed methods action research design was used. Sample included individuals aged ≥18 years who were diagnosed with cancer (patients), provided cancer treatment (providers) or made health policy (policymakers) in the State. Data were collected using surveys and key informant interviews. Data analysis involved descriptive statistics, chi-square test and a deductive thematic analysis. Results: Participants were 29 patients, 50 providers and 33 policymakers (n = 122), with an average age of 45 (±11) years. Challenges identified by ≥60% of participants were: low public awareness (75.9%); limited availability of treatment options (62.6%); lack of treatment pathways (92.8%); lack of local cancer data (95.2%); and, absence of support groups for patients (88.0%). Each group of participants (n = 3) rated cancer 9 out of 10 as a public health priority. Some qualitative themes were: low cancer awareness, delays in cancer treatment and financial burden on patients. “One thing I’d like to include in the policy is to make cancer a reportable disease in the State.” -Policymaker. Most participants (80%, 90/112) recommended that health insurance should fund ≥16% of cancer control activities, although policymakers were more likely to make quarterly insurance contributions than patients (7 out of 10 vs. 5 out of 10). Conclusions: Cancer control was an important issue for people in the State. Inadequate prevention services with a background of > 3-month diagnostic delays, characterized cancer control in Abia State. Future cancer control policy should emphasize cancer prevention, the creation of local clinical pathways and a blended financing model. [Table: see text]