Data Informatics Research Articles

Methylation of Imprinted Genes in Sperm DNA Correlated to Urinary Polycyclic Aromatic Hydrocarbons (PAHs) Exposure Levels in Reproductive-Aged Men and the Birth Outcomes of the Offspring.

Polycyclic aromatic hydrocarbons (PAHs) are known environmental pollutants. Studies are very limited regarding the impacts of paternal PAHs exposure on birth outcomes as well as the underpinning mechanisms in human. In this study, 302 reproductive-aged males (22–46 years old) were enrolled and demographic informatics data were obtained by questionnaires. The levels of urinary hydroxylated PAHs (OH-PAHs) were assessed by ultra-high performance liquid chromatography-tandem mass spectrometry; and methylation levels of the imprinting genes H19, Meg3, and Peg3 of sperm DNA were evaluated via bisulfite pyrosequencing. The analysis of the correlation between OH-PAHs levels and methylation levels of imprinting genes showed that OH-PAHs are correlated with some CpG sites in H19, Peg3, and Meg3. To further investigate an association of urinary OH-PAHs with birth outcomes, follow-up study of wives of these subjects has been performed for 1–3 years. As the result, a total of 157 babies were born. The birth outcomes parameters including birth weight (BW), length (BL), and ponderal index (PI) were recorded. The further analysis of generalized estimating equation indicated a negative correlation between urinary total OH-PAHs levels and newborn BW (β = −0.081, p = 0.020); but this association has not been found for BL and PI. Furthermore, a logistic regression analysis was employed for examining associations of the methylation of imprinting genes with birth outcomes parameters, which indicated a negative correlation between BW and H19, namely, each unit percent (%) elevation in methylation of H19 (but not Peg3 and Meg3) was significantly associated with a 0.135 g reduction of BW (β = −0.135; 95% CI 0.781–0.978). Putting together, these results show that paternal non-occupational environmental exposure to PAHs is associated with newborn BW. And imprinting gene H19 methylation may be involved in the underlying mechanisms. This study in human population adds a support for previous animal study and implies that environmental impact on the offspring through paternal pathway.

A big-data driven approach to analyzing and modeling human mobility trend under non-pharmaceutical interventions during COVID-19 pandemic

During the unprecedented coronavirus disease 2019 (COVID-19) challenge, non-pharmaceutical interventions became a widely adopted strategy to limit physical movements and interactions to mitigate virus transmissions. For situational awareness and decision-support, quickly available yet accurate big-data analytics about human mobility and social distancing is invaluable to agencies and decision-makers. This paper presents a big-data-driven analytical framework that ingests terabytes of data on a daily basis and quantitatively assesses the human mobility trend during COVID-19. Using mobile device location data of over 150 million monthly active samples in the United States (U.S.), the study successfully measures human mobility with three main metrics at the county level: daily average number of trips per person; daily average person-miles traveled; and daily percentage of residents staying home. A set of generalized additive mixed models is employed to disentangle the policy effect on human mobility from other confounding effects including virus effect, socio-demographic effect, weather effect, industry effect, and spatiotemporal autocorrelation. Results reveal the policy plays a limited, time-decreasing, and region-specific effect on human movement. The stay-at-home orders only contribute to a 3.5%-7.9% decrease in human mobility, while the reopening guidelines lead to a 1.6%-5.2% mobility increase. Results also indicate a reasonable spatial heterogeneity among the U.S. counties, wherein the number of confirmed COVID-19 cases, income levels, industry structure, age and racial distribution play important roles. The data informatics generated by the framework are made available to the public for a timely understanding of mobility trends and policy effects, as well as for time-sensitive decision support to further contain the spread of the virus.

고령자의 중강도-고강도 신체활동량에 따른 대사증후군 위험요인에 미치는 영향

The purpose of this study is to provide basic data to help elderly investigate the amount of moderate-to-vigorous physical activity (MVPA) by age and gender in elderly and to compare and analyze and prevent risk factors for metabolic syndrome. A total of 1,700 data were collected from 761 male and 939 female elderly who participated in National Health Insurance Service-Health Screening. It was conducted with the approval of the Research Ethics Review Committee. The data set was drawn from a retrospective cohort based on Nasaret International Hospital Medical Informatics Data (NIHMID), and as the data were deidentified, the informed consent of the subject. In male elderly, MVPA (P<.05), waist circumference (P<.05), diastolic blood pressure (P<.001), fasting blood glucose (P<.05), and HDL-cholesterol (P<. 05) showed the significantly difference by age. In female elderly, MVPA (P<.05), waist circumference (P<.001), systolic blood pressure (P<.05), diastolic blood pressure (P<.05), fasting blood glucose (P<.05), HDL-cholesterol (P<.001), and triglycerides (P<.05) showed the significantly difference by age. MVPA, systolic blood pressure (r=-.051, P<.05), and TG (r=-.052, P<.05) were negatively correlated, and HDL-C (r=.060, P<.05) was positive correlation. There was a negative correlation between MVPA and age (r=-.111, P<.001). In male and female elderly people were confirmed to have decreased physical activity as their age increased, and also to increase the risk of chronic disease as a result of decreased physical activity as well as negative risk factors for metabolic syndrome. From this point of view, a detailed study will be need to increase the amount of physical activity according to the physical characteristics and age of the individual.

Long-term persistence to low-dose aspirin for cardiovascular prevention in routine clinical practice in United Kingdom and Germany

Abstract Background Low-dose aspirin is effective in the prevention of ischemic vascular events and studies have shown a preventative effect on colorectal cancer (CRC). Discontinuation of low-dose aspirin is associated with increased risk of ischaemic vascular events. However, data on long-term persistence to low-dose aspirin from routine clinical practice are limited. Purpose To assess the long-term persistence to low-dose aspirin therapy in the primary and secondary cardiovascular (CVD) prevention population Methods This retrospective cohort study used data from United Kingdom (UK) – The Health Improvement Network database and Germany (DE) – IQVIA Disease Analyzer and analyzed using an adaptation of Observational Health Data Sciences and Informatics (OHDSI) ATLAS Tool. Patients 18 years or older, with at least two prescriptions of low-dose aspirin (75–100mg) within the first year of index date during the study period between 2007 and 2018, and with at least 12 months of observation before and after the index date were included in the study. The patients with a CVD diagnosis or a CABG/PCI procedure before the index date or a prescription of dual antiplatelet at index date were classified as secondary CVD prevention. The remaining patients were classified as potential primary CVD prevention. The index date was first prescription of low-dose aspirin. Persistence was calculated if the gap between two prescriptions exceeds 60 days. Patients with such gaps still receiving prescription after 60 days were plotted based on the number of gaps identified during the follow-up and if no prescription was recorded then they were considered discontinued. Results The total number of patients receiving low-dose aspirin was 327,806 (183,089 in UK and 144,717 in DE with up to 10 years of follow-up). A total of 112,887 and 101,704 received low-dose aspirin for secondary CVD prevention; 70,202 and 43,013 potentially for primary CVD prevention in UK and DE, respectively. Persistence at two years with a few gaps was 67% and 59% in UK and DE for secondary CVD prevention; 57% and 53% for primary CVD prevention, respectively. With multiple Gaps, 50% and 36% still receive prescription low-dose aspirin for 10 years in UK and DE, respectively for secondary CVD prevention. For Primary prevention, 36% and 32% receive prescription of aspirin for 10 years with multiple gaps in UK and DE (Figure). Conclusion After the initial drop in persistence the patients tend to continue their low-dose aspirin treatment for long-term, although with multiple gaps. Overall, the persistence was higher in secondary compared to primary CVD prevention. Improving persistence to low-dose aspirin therapy in the initial years may help in continuity of their treatment over long-term. Persistence might be underestimated due to potential over the counter use of low-dose aspirin. Funding Acknowledgement Type of funding source: Private company. Main funding source(s): Bayer AG

Long-term adherence to low-dose aspirin therapy for cardiovascular prevention in routine clinical practice in United Kingdom and Germany

Abstract Background Low-dose aspirin is effective in the prevention of ischemic vascular events and studies have shown a preventative effect on colorectal cancer (CRC). However, adherence to therapy is likely an important factor for these benefits to be seen and non-adherence is associated with sub-optimal outcomes. Purpose To assess the long-term adherence to low-dose aspirin therapy in the primary and secondary cardiovascular (CVD) prevention population Methods This retrospective cohort study used data from United Kingdom (UK) – The Health Improvement Network Database and Germany (DE) – IQVIA Disease Analyzer and analyzed using an adaptation of Observational Health Data Sciences and Informatics (OHDSI) ATLAS Tool. Patients 18 years or older, with at least two prescriptions of low-dose aspirin (75–100mg) within the first year of index date during the study period between 2007 and 2018, and with at least 12 months of observation before and after the index date were included in the study. The patients with a CVD diagnosis or a CABG/PCI procedure before the index date or a prescription of dual antiplatelet at index date were classified as secondary CVD prevention. The remaining patients were classified as potential primary CVD prevention. The index date was first prescription of low-dose aspirin. Adherence was calculated using medication possession ratio (MPR) which is number of days of medication supplied within the refill interval divided by number of days in the refill interval. Long term adherence was divided in three categories namely patients with ≤2 years, 2-≤5 years, and 5-≤10 years of follow-up. Results The total number of patients receiving low-dose aspirin was 327,806 (183,089 in UK and 144,717 in DE with up to 10 years of follow-up). A total of 112,887 and 101,704 received low-dose aspirin for secondary CVD prevention; 70,202 and 43,013 potentially for primary CVD prevention in UK and DE, respectively. Median adherence to low-dose aspirin in UK and DE for patients with ≤2 years of follow-up was 88% and 80% for secondary CVD prevention, respectively; 84% and 74% for 2-≤5 years; and 75% and 59% for 5-≤10 years, respectively. Median adherence to low-dose aspirin in UK and DE for patients with ≤2 years was 76% and 75% for potential primary CVD prevention; 65% and 66% for 2-≤5 years; 49% and 49% for 5-≤10 years, respectively (Figure). Conclusion The long-term adherence to low dose aspirin was quite high with more than half the patients being adherent to their treatment. Overall, the adherence was higher in secondary compared to primary CVD prevention. The estimates for adherence might be underestimated due to potential over the counter use of low-dose aspirin. Funding Acknowledgement Type of funding source: Private company. Main funding source(s): Bayer AG

Population Heath Informatics Can Advance Interoperability: National Program of Cancer Registries Electronic Pathology Reporting Project.

Given the reach, breadth, and volume of data collected from multiple clinical settings and systems, US central cancer registries (CCRs) are uniquely positioned to test and advance cancer health information exchange. This article describes a current Centers for Disease Control and Prevention (CDC) National Program of Cancer Registries (NPCR) cancer informatics data exchange initiative. CDC is using an established cloud-based platform developed by the Association of Public Health Laboratories (APHL) for national notifiable disease reporting to enable direct transmission of standardized electronic pathology (ePath) data from laboratories to CCRs in multiple states. The APHL Informatics Messaging Services (AIMS) Platform provides an infrastructure to enable a large national laboratory to submit data to a single platform. State health departments receive data from the AIMS Platform through a secure portal, eliminating separate data exchange routes with each CCR. Key factors enabling ePath data exchange from laboratories to CCRs are having established cancer registry data standards and using a single platform/portal to reduce data streams. NPCR plans to expand this approach in alignment with ongoing cancer informatics efforts in clinical settings. The 50 CCRs supported by NPCR provide a variety of scenarios to develop and disseminate cancer data informatics initiatives and have tremendous potential to increase the implementation of cancer data exchange.

PREFACE

We are honored to bring you this collection of articles from the 2020 10th International Conference on Future Environment and Energy (ICFEE 2020) which was held in Kyoto, Japan from January 7 to 9, 2020. ICFEE 2020 is dedicated to issues related to Future Environment and Energy.The primary focus of the conference is to bring together academic scientists, engineers, and industry researchers to share their experiences and research results, and discuss the practical challenges encountered and the solutions adopted. The ICFEE 2020 served as a good platform for the scientific community where more than 100 participants met, exchanged ideas and research results, and examined the way forward regarding future environment and energy.ICFEE 2020 Conference Chair Prof. Kondo Akira Osaka University, Japan did the Opening Remarks and delivers his keynote speeches titled “Evaluation on current and future air quality in Continental Southeast Asia by using WRF/CMAQ”; also there were two other Keynote Speeches. They are Prof. Jae K. Park from University of Wisconsin-Madison, USA) presented Keynote Speech titled “Innovative Upcycling of Scrap Tires for Pollutant Abatement” on 8th January. This was followed by Keynote Speaker Prof. Rodney Stewart, form Griffith University, Queensland, Australia who spoke on “Role of Intelligent Sensor Networks and Big Data Informatics for Managing Urban Water and Energy Resources”. All the three keynote speakers focused on the latest information and most innovative developments in their respective expertise areas on future environment and energy. For the conference proper, there were nine sub-sessions and one poster session with various interesting topics as follows: Environmental Management and Sustainable Development, Atmospheric Monitoring and Water Quality Analysis, Environmental and Chemical Engineering, Building Energy Efficiency and Energy Management, Life Cycle Analysis of Photovoltaic Solar Panels, Combustion Science and Power Engineering, Urban Engineering and Transportation Engineering, Battery Technology and Photovoltaic System Optimization, Geological and Civil Engineering.ICFEE 2020 was indeed a golden opportunity for academics, students, researchers and engineers to interact with the experts and specialists to get their advice or consultation on state-of-the-art developments in future environment and energy, technical matters, as well as research methodologies and innovative strategies in the field of environmental and energy management. On the third day of the conference, we had a one-day visit in Kyoto city.This proceeding presents a selection from papers submitted to the conference from participants from universities, research institutes and industry. All papers were subjected to peer-review by conference committee members and international reviewers. The papers selected were based on their quality and their relevancy to theconference. The volume tends to present to the readers the recent advances in the field of Future Environment and Energy and various related areas, such as Energy and Environment, Renewable Energy, Advanced Energy Technologies, Fuels and Combustion, Benefits of the Conference, etc.Finally, it is appropriate that we record our thanks to our organizing committees and reviewers for their work in securing a substantial input of papers from 15 countries and in encouraging participation from those areas. We are also indebted to those who served as Keynote Speakers, Conference Chairmen and Session Chairmen. Without their support, the conference could not have been as successful as it was. We also acknowledge the authors who have contributed to this volume, without whose expert inputs, this compilation of proceedings would not have been possible. We would like to thank all the conference participants for their support to ICFEE 2020.Prof. Kondo Akira Osaka University, Japan April 23, 2020

Exploring Design Principles for Sharing of Personal Informatics Data on Ephemeral Social Media

People often do not receive the engagement or responses they desire when they share on broad social media platforms. Sharers are hesitant to share trivial accomplishments, and the emphasis on data often results posts that audiences find repetitive or unengaging. Ephemeral social media's focus on self-authored content and sharing trivial accomplishments has the potential to ameliorate these challenges. We explore design principles for incorporating personal informatics data like steps, heart rate, or duration in data-driven stickers as a first step towards integrating these data into ephemeral social media. We examine the effect of a sticker's presentation style, domain, domain-relevance, and background through three surveys with 506 total participants. We uncover the importance of domain-relevant backgrounds and stickers, identify the situational value of stickers styled as analogies, embellished, and badges, and demonstrate that data-driven stickers can make ephemeral content more informative and entertaining, discussing implications for platforms and tools.

Editorial Special Issue on “AI-Driven Informatics, Sensing, Imaging and Big Data Analytics for Fighting the COVID-19 Pandemic”

The papers in this special section focuses on artificial intelligent-driven informatics, sensing, imaging and big data analytics in dealing with the COVID-19 pandemic.

PIMD: An Integrative Approach for Drug Repositioning Using Multiple Characterization Fusion

The accumulation of various types of drug informatics data and computational approaches for drug repositioning can accelerate pharmaceutical research and development. However, the integration of multi-dimensional drug data for precision repositioning remains a pressing challenge. Here, we propose a systematic framework named PIMD to predict drug therapeutic properties by integrating multi-dimensional data for drug repositioning. In PIMD, drug similarity networks (DSNs) based on chemical, pharmacological, and clinical data are fused into an integrated DSN (iDSN) composed of many clusters. Rather than simple fusion, PIMD offers a systematic way to annotate clusters. Unexpected drugs within clusters and drug pairs with a high iDSN similarity score are therefore identified to predict novel therapeutic uses. PIMD provides new insights into the universality, individuality, and complementarity of different drug properties by evaluating the contribution of each property data. To test the performance of PIMD, we use chemical, pharmacological, and clinical properties to generate an iDSN. Analyses of the contributions of each drug property indicate that this iDSN was driven by all data types and performs better than other DSNs. Within the top 20 recommended drug pairs, 7 drugs have been reported to be repurposed. The source code for PIMD is available at https://github.com/Sepstar/PIMD/.

Trends in Heart Failure Mortality Disparities Before and After the Implementation of The Affordable Care Act: The Chicago Experience

Background Heart failure remains a leading cause of morbidity and mortality in the United States affecting 6.2 million Americans, with a projected prevalence of > 8 million affected by 2030. The impact of heart failure is not shared equitably, the overall age-adjusted death rate for heart failure is 89.7 per 100,000. African American males (118.2) and females (86.0) have a higher death rate compared to white men (111.3) and women (80.4). Studies have also shown that economic burden has been associated with increased heart failure mortality. Access to care has been associated with poor outcomes in HF. The Patient protection and Affordable Care Act (ACA) was signed into law March 10th, 2010 and led to increased health insurance coverage and increased healthcare access. In this study we describe the secular trends on HF mortality in the city of Chicago. Methods This was a retrospective analysis of the City of Chicago Department of Public Health's Epidemiology & Public Health Informatics Data portal from 1999-2017. City level heart failure mortality rates were collected in rates per 100,000 and assessed for change prior to implementation of the ACA and late implementation of the ACA. Mortality rates were categorized by race, gender, and economic hardship. Economic hardship was defined by the 6 variable economic hardship index and was subcategorized as high, medium, and low hardship. Results Overall age-adjusted heart failure mortality rates decreased annually by -1.9% by the end of the pre-ACA period (p= 0.001). By the late ACA period there was a +1.8% annual increase in overall heart failure mortality (p = 0.001). African Americans had a -2.4% reduction and Whites had a -1.14% reduction prior to ACA. Post ACA African Americans had a +2.9% annual increase in HF mortality, and Whites had a -1.14% reduction (p = 0.001). In women HF mortality decreased annually by -2.9% pre-ACA (0.001) and continued to decline by -1.1% (p = .001) by late ACA. HF Mortality in men decreased by -1.1% (p = .004) annually pre-ACA, and increased by +3.1% (p = .001) annually by late ACA. Medium economic burden was associated with a +5.7% (p = .009) annual increase in HF mortality by late ACA. No difference was observed in those with high or low economic burden. Conclusions Following implementation of the ACA, the city of Chicago experienced an increase in the rate of HF mortality. The post ACA period was also notable for widening of racial, gender, and economic disparities in HF mortality. Further investigation into contributing factors is warranted.

Abstract IA24: A modern data commons approach to advance population science

Abstract Since 2014, NCI has been building data science platforms to integrate genetic information about tumors with data on how patients respond to therapy. This work advances the 2011 Institute of Medicine recommendation to build a unified system to collect, integrate, and share cancer data from the widest possible set of research studies. NCI is working to fulfill this recommendation by establishing an NCI Cancer Research Data Commons (CRDC), virtual, expandable informatics infrastructure and data repositories, which are designed to support the work of the NCI intramural and extramural cancer research communities. The CRDC is a crucial component of a broader National Cancer Data Ecosystem that spans discovery research, patient participation, and population surveillance. The development of such an ecosystem was a key recommendation of the Cancer Moonshot Blue Ribbon Panel. NCI has been developing the Cancer Research Data Commons based on the understanding that big data emerging from research programs, precision medicine trials, and surveillance can be used to develop and inform models that predict how patients will response to treatment. Numerous NCI-funded programs will provide key data for this ecosystem, including a variety of discovery programs, clinical trials, cohort studies, and NCI’s Surveillance, Epidemiology, and End Results program (SEER), which is the most comprehensive source of population-based information in the country that includes stage of cancer, detailed characterization of the cancer including important biomarkers at the time of diagnosis, and performs active follow-up to determine patient survival data. The CRDC is designed to be as flexible as possible so that many types and sources of data can be accessed in a unified, interoperable manner. It is this data interoperability that facilitates novel insights into cancer biology and treatment. During the past several years, NCI has been developing key building blocks that comprise the CRDC. These building blocks include (i) Data Commons Nodes, which are repositories that house harmonized data from multiple programs; (ii) Cloud Resources, which provide workspaces, analysis capabilities, and access to elastic computing resources in commercial cloud environments; (iii) a Data Commons Framework (DCF), which is a set of modular, reusable services that maximize interoperability across cancer datatypes and repositories, increase sustainability, and reduce future resource cost; and (iv) semantics services to support data models, vocabularies, ontologies, data submission, and cross-domain queries. The modular design of the CRDC means that new services and components can be added flexibly, which allows for new data sources and new technologies to become integrated as they become available. The NCI Cancer Research Data Commons will serve as a platform for understanding fundamental cancer biology questions, including cancer initiation, progression, and recurrence, in ways that support new prevention, treatment, and surveillance strategies. Citation Format: Anthony Kerlavage. A modern data commons approach to advance population science [abstract]. In: Proceedings of the AACR Special Conference on Modernizing Population Sciences in the Digital Age; 2019 Feb 19-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2020;29(9 Suppl):Abstract nr IA24.

Dietmar Offenhuber: Collaboration Via the Many Traces Our Data Sets Leave Behind.

We asked Dietmar Offenhuber for an interview upon investigating his recent works, Staubmarke (Dust mark) and Ozone Tattoo, which together suggest an artist with an insightful eye for injecting particulate data engagingly into art pieces that spur awareness in an audience. We were impressed by his deep considerations on the potential for scientific and informatics data sets to offer the potential for, and foster, wider discussions and collaborations.

Telemedicine: an Essential Requirement for the Health Care Providers, with Emphasis on Legal Aspects

Telemedicine is the use of telecommunication and information technologies in order to provide clinical health care at a distance. These technologies allow communications between patient and medical staff with convenience as well as the transmission of medical, imaging and health informatics data from one site to another. It is also used to save lives in critical care and emergency situations. Although telemedicine systems have many advantages, including the distribution of high quality medical services to remote areas, failure to comply with infrastructure will reduce the efficiency and quality of their services. Issues such as building the infrastructure of the medical information industry, including the legal infrastructure, and thus providing a suitable platform for the legal and ethical issues of Telemedicine, as well as obtaining the necessary permits and requirements, will play an important role in the successful implementation of a Telemedicine system. The purpose of this study was to become more familiar with the field of Telemedicine and its services, as well as to review some legal issues in the field of e-health. Telemedicine is not able to solve the problems of the health and social systems, but the problems of the health and social systems cannot be solved without Telemedicine.

A Novel Class Noise Detection Method for High-Dimensional Data in Industrial Informatics

The data in industrial informatics may be high-dimensional and mislabeled. Irrelevant or noisy features pose a significant challenge to the detection of high-dimensional mislabeling. The traditional method usually adopts a two-step solution, first finding the relevant subspace and then using it for mislabeling detection. This two-step method struggles to provide the optimal mislabeling detection performance, since it separates the procedures of feature selection and label error detection. To solve this problem, in this article, we integrate the two steps and propose a sequential ensemble noise filter (SENF). In the SENF, relevant features are selected and used to generate a noise score for each instance. Continuously, these noise scores guide feature selection in the regression learning. Thus, the SENF falls in the scope of sequential ensemble learning. We evaluate our approach on several benchmark datasets with high dimensionality and much label noise. It is shown that the SENF is significantly better than other existing label noise detection methods.

Neuroharmony: A new tool for harmonizing volumetric MRI data from unseen scanners

This research has been conducted using the UK Biobank Resource (Project Number 40323) and has been supported by a Wellcome Trust’s Innovator Award (208519/Z/17/Z) to Andrea Mechelli. The present work was carried out within the scope of the research program Dipartimenti di Eccellenza (art.1, commi 314-337 legge 232/2016), which was supported by a grant from MIUR to the Department of General Psychology, University of Padua. The data from UCLA, LOSS AVERSION, EMOTIONREGULATION, FALSEBELIEFS, MATURATIONAL CHANGES, ASSOCIATIVE LEARNING, HARMAVOIDANCE, PLACEBO, MORAL JUDGEMENT, CYBERBALL, ROUTE LEARNING, SEQUENTIAL INFERENCE VBM, WASHINGTON UNIVERSITY datasets were obtained from the OpenfMRI database. Their accession numbers are ds000030, ds000053, ds000108, ds000109, ds000119, ds000168, ds000202, ds000208, ds000212, ds000214, ds000217, ds000222, and ds000243, respectively. The acquisition of dataset HMRRC was supported by the National Natural Science Foundation of China to Prof. Qiyong Gong (81220108013, 8122010801, 81621003, 81761128023 and 81227002). Part of the data used in this article (NITRC) have been funded in whole or in part with Federal funds from the Department of Health and Human Services, National Institute of Biomedical Imaging and Bioengineering, the National Institute of Neurological Disorders and Stroke, under the following NIH grants: 1R43NS074540, 2R44NS074540, and 1U24EB023398and previously GSA Contract No. GS-00F-0034P, Order Number HHSN268200100090U. This research has been conducted using the UK Biobank Resource. Part of the data used in preparation of this article were obtained from the Alzheimer’s Disease Repository Without Borders (ARWiBo – www.arwibo.it). The overall goal of ARWiBo is to contribute, thorough synergy with neuGRID (https://neugrid2.eu), to global data sharing and analysis in order to develop effective therapies, prevention methods and a cure for Alzheimer’ and other neurodegenerative diseases. Part of the data used in this article was downloaded from the Collaborative Informatics and Neuroimaging Suite Data Exchange tool (COINS; http://coins.mrn.org/dx) and data collection was performed at the Mind Research Network and funded by a Center of Biomedical Research Excellence (COBRE) grant 5P20RR021938/P20GM103472 from the NIH to Dr. Vince Calhoun. Part of the data used for this study were downloaded from the Function BIRN Data Repository (http://fbirnbdr.birncommunity.org:8080/BDR/), supported by grants to the Function BIRN (U24-RR021992) Testbed funded by the National Center for Research Resources at the National Institutes of Health, U.S.A. Part of the data used in the preparation of this work were obtained from the Mind Clinical Imaging Consortium database through the Mind Research Network (www.mrn.org). The MCIC project was supported by the Department of Energy under Award Number DE-FG02-08ER64581. MCIC is the result of efforts of co-investigators from University of Iowa, University of Minnesota, University of New Mexico, Massachusetts General Hospital. CLING/HMS: The CliNG study sample was partially supported by the Deutsche Forschungsgemeinschaft (DFG) via the Clinical Research Group 241 ‘Genotype-phenotype relationships and neurobiology of the longitudinal course of psychosis’, TP2 (PI Gruber; http://www.kfo241.de; grant number GR 1950/5-1). Part of the data used in preparation of this article were obtained from the NU Schizophrenia Data and Software Tool (NUSDAST) database (http://central.xnat.org/REST/projects/NUDataSharing) As such, the investigators within NUSDAST contributed to the design and implementation of NUSDAST and/or provided data but did not participate in analysis or writing of this report. Part of the data used in the preparation of this article were obtained from the Parkinson’s Progression Markers Initiative (PPMI) database (www.ppmi-info.org/data). For up-to-date information on the study, visit www.ppmi-info.org. PPMI – a public-private partnership – is funded by the Michael J. Fox Foundation for Parkinson’s Research and funding partners, including [list the full names of all of the PPMI funding partners found at www.ppmi-info.org/fundingpartners]. Part of the data used in preparation of this article were obtained from the SchizConnect database (http://schizconnect.org). As such, the investigators within SchizConnect contributed to the design and implementation of SchizConnect and/or provided data but did not participate in analysis or writing of this report. Data collection and sharing for this project was funded by NIMH cooperative agreement 1U01 MH097435. Joao Sato is supported by Sao Paulo Research Foundation (FAPESP, Brazil) Grants 2018/04654-9 and 2018/21934-5.

Scale-up of the Accrual to Clinical Trials (ACT) network across the Clinical and Translational Science Award Consortium: a mixed-methods evaluation of the first 18 months.

The Clinical and Translational Science Award (CTSA) Program is a Consortium of nearly 60 academic medical research centers across the USA and a natural network for evaluating the spread and uptake of translational research innovation across the Consortium. Dissemination of the Accrual to Clinical Trials (ACT) Network, a federated clinical informatics data network for population-based cohort discovery, began January 2018 across the Consortium. Diffusion of innovation theory guided dissemination design and evaluation. Mixed-methods assessed the spread and uptake across the Consortium through July 1, 2019 (n = 48 CTSAs). Methods included prospective time activity tracking (Kaplan-Meier curves), and survey and qualitative interviews. Within 18 months, nearly 80% of CTSAs had joined the data network and two-thirds of CTSAs achieving technical readiness had initiated launch to local clinical investigators. Over 10,000 ACT Network queries are projected for 2019; and by 2020, nearly all CTSAs will have joined the network. Median time-from-technical-readiness-to-local-launch was 154 days (interquartile range: 87-225 days]. Quality improvement processes reduced time-to-launch by 35.2% (64 days, p = 0.0036). Lessons learned include: (1) conceptualize dissemination as two-stage adoption demonstrating value for both CTSA hub service providers and clinical investigators; (2) include institutional trial into dissemination strategies so CTSA hubs can refine internal workflows and gather local user feedback endorsement; (3) embrace designing-for-dissemination during technology development; and (4) sustain adaptive dissemination and customer relationship management to keep CTSA hubs and users engaged. Scale-up and spread of the ACT Network provides lessons learned for others disseminating innovation across the CTSA Consortium. The Network is primed for embedded implementation research.

Better Together: The Making and Maturation of the Palliative Care Research Cooperative Group (PCRC) (GP756)

• Explain the resources and contributions of the Palliative Care Research Cooperative Group (PCRC) to advancing palliative care science.• Understand how the attendee can leverage the PCRC to enhance their palliative care science. Despite advances, significant gaps remain in the evidence base to inform care for people with serious illness. The Palliative Care Research Cooperative Group (PCRC) was formed to generate needed evidence and bolster research capacity. We describe the PCRC’s growth and contributions to advancing palliative care research. The PCRC exists to lead, catalyze, and empower a community of palliative care investigators. The PCRC:• Supports investigators at all levels of experience in the conduct of clinical studies;• Conducts nationally representative, multi-institutional studies that include diverse populations;• Leverages Standard Data Elements and a de-identified data repository that amplifies the impact of any single study; and• Provides methodological resources, participant access/recruitment, and the expertise of PCRC investigators from multiple disciplines. After developing a governance structure and completing a proof of concept study, the PCRC expanded its infrastructure via additional resource cores (Clinical Studies/Methodology; Measurement; Data Informatics and Statistics; and Caregiver Research), an Investigator Development Center to complement the Project Coordinating Center, and Scientific Review and Publications Committees. Since its inception in 2010, the PCRC has grown to over 550 members representing over 185 distinct locations. To date, the PCRC has supported submission of 146 research applications and conduct of 50 studies. The PCRC promotes investigator development through pilot grant awards (n=35), monthly webinars (n=49), and Clinical Trials Intensives (n=4). The PCRC convenes regular investigator meetings, has special interest groups, and communicates via a newsletter, website, and twitter. With a particular focus on facilitating conduct of rigorous multi-site clinical studies, the PCRC fosters an engaged multi-disciplinary research community, filling a void in generating and disseminating evidence that informs the provision of high quality palliative care.

Abstract 27: Getting granular: A structured database of doses and schedules in hematology/oncology

Abstract Introduction: Granular cancer patient treatment data collection, and subsequent mapping to standard regimen definitions, are vital next steps in advancement of observational studies in oncology. However, the identification of regimen details, including dose and schedule, is a prerequisite for both collection and mapping. At the patient level, claims databases are a useful but limited resource. Most cancer registries, such as the National Cancer Institute Surveillance, Epidemiology, and End Results (SEER) program and the Commission on Cancer National Cancer Database, capture only a simplification of actual treatments, such as binary exposure to chemotherapy (yes/no) or a list of individual chemotherapeutic agent names. Moreover, even with optimal patient data collection, a “gold standard” database of expected chemotherapy doses and schedules as part of standard-of-care (SOC) does not currently exist. Methods: We extracted and normalized the semistructured dosing and timing data from HemOnc.org, the largest publicly available website of SOC chemotherapy drugs and regimens. To that end, we undertook two broad and parallel approaches: 1) standardization of prescribing instructions within a given cycle (SIGs) or pertaining to cycle timing (“cycleSIGs”); and 2) parsing of resultant content into structured variables. This effort was carried out iteratively with the goal of creating standard “canonical” forms for intermittent intravenous (IV), continuous IV (CIV), other routes (e.g., oral), and radiation SIGs. All SIGs were bound to regimen and treatment context (e.g., cyclophosphamide dosing differs in R-CHOP versus R-CVP, and number of cycles often differs between adjuvant and metastatic contexts). Results: There are currently 14,569 regimen-context-SIG-cycleSIG quartets in the database (October 2019). Parsing of SIGs into structured variables resulted in 7,792 canonical IV, 675 canonical CIV, 3,762 canonical other, 510 canonical radiation, and 2,249 noncanonical results. Some SIGs are multipart and were broken into steps. For example, “6 mg/m2 IV once on day 1, then 3 mg/m2 IV once on day 8” constitutes two separate steps. There were 948 unique cycleSIGs (e.g., “21-day cycle for 4 cycles”), which were also parsed into components. Discussion: This effort has produced a large dataset of granular drug and cycle SIG information that reflects SOC dosing parameters in hematology/oncology. This dataset can be used to understand discrepancies between real-world outcomes and clinical trial results, e.g., by elucidating the effect of dose reductions and treatment delays on treatment outcomes. The Observational Health Data Sciences and Informatics (OHDSI) oncology workgroup is arranging to add this new information to the portion of the HemOnc vocabulary that is available through the OHDSI terminology management tool. Ongoing efforts also include translating the maximum possible number of noncanonical sigs into canonical forms, which can further enhance simplicity and usability of the dataset. Citation Format: Zachary H. Moldwin, Andrew Malty, Donna R. Rivera, Anastasios Siapos, Christian G. Reich, Michael J. Gurley, Rimma Belenkaya, Dmitry Dymshyts, Jeremy L. Warner. Getting granular: A structured database of doses and schedules in hematology/oncology [abstract]. In: Proceedings of the AACR Special Conference on Advancing Precision Medicine Drug Development: Incorporation of Real-World Data and Other Novel Strategies; Jan 9-12, 2020; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2020;26(12_Suppl_1):Abstract nr 27.

Detecting and Filtering Immune-Related Adverse Events Signal Based on Text Mining and Observational Health Data Sciences and Informatics Common Data Model: Framework Development Study.

BackgroundImmune checkpoint inhibitors are associated with unique immune-related adverse events (irAEs). As most of the immune checkpoint inhibitors are new to the market, it is important to conduct studies using real-world data sources to investigate their safety profiles.ObjectiveThe aim of the study was to develop a framework for signal detection and filtration of novel irAEs for 6 Food and Drug Administration–approved immune checkpoint inhibitors.MethodsIn our framework, we first used the Food and Drug Administration’s Adverse Event Reporting System (FAERS) standardized in an Observational Health Data Sciences and Informatics (OHDSI) common data model (CDM) to collect immune checkpoint inhibitor-related event data and conducted irAE signal detection. OHDSI CDM is a standard-driven data model that focuses on transforming different databases into a common format and standardizing medical terms to a common representation. We then filtered those already known irAEs from drug labels and literature by using a customized text-mining pipeline based on clinical text analysis and knowledge extraction system with Medical Dictionary for Regulatory Activities (MedDRA) as a dictionary. Finally, we classified the irAE detection results into three different categories to discover potentially new irAE signals.ResultsBy our text-mining pipeline, 490 irAE terms were identified from drug labels, and 918 terms were identified from the literature. In addition, of the 94 positive signals detected using CDM-based FAERS, 53 signals (56%) were labeled signals, 10 (11%) were unlabeled published signals, and 31 (33%) were potentially new signals.ConclusionsWe demonstrated that our approach is effective for irAE signal detection and filtration. Moreover, our CDM-based framework could facilitate adverse drug events detection and filtration toward the goal of next-generation pharmacovigilance that seamlessly integrates electronic health record data for improved signal detection.