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The importance of real-world data to precision medicine

PERSONALIZED MEDICINE 2019年第2期16卷 79-82页

作者： Kalra, Dipak Univ Ghent Dept Med Informat & Stat B-9000 Ghent Belgium

[...]the promise of personalized medicine is greater than this, and making inroads into some of society's greatest healthcare burdens, such as long-term conditions and their primary/secondary prevention, requires much richer clinical and patient-generated data alongside the molecular data, in order to meaningfully characterize patients into subgroups for whom treatments, care pathways and prevention strategies can be better tailored. What is new and challenging is how to conduct research on large networks of routinely collected health data (real-world data) on the broad range of patients who reflect the true diversity of persons with a condition and not only those who volunteer for clinical trials or biobanks. A code of practice defining good practices in data sharing and in protecting data subject privacy (including compliance with the EU General data Protection Regulation). The use of common data models is a necessity for the moment, but our research and healthcare communities need to unite to promote the better adoption of interoperability standards across the health ecosystem, so that all actors can learn from large scale health data to improve care as well as conduct research.

关键词： big data common data model data protection federated data analysis real world data

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Computerisation in general practice: Lessons for Canada from the UK and Australia

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Studies in Health Technology and Informatics 2013年 183卷 28-36页

作者： Shaw, Nicola T. Bainbridge, Mike Health Informatics Institute and Algoma University Sault Ste. Marie ON Canada University of Victoria Victoria BC Canada

In 2000 Shaw and Kidd published an article on the lessons that could be learned from the UK in general practice computerization. Over a decade later many of these lessons remain yet to be learned. Hence Shaw & Bainbridge felt that it was time to revisit these issues and review progress made against each in both the UK and Australia in an effort to help Canada learn the lessons as it follows behind both countries. Nine lessons are identified, reviewed and discussed in the hope that Canada will choose to take note and leapfrog these jurisdictions by learning from history, rather than being doomed to repeat it.. © 2013 ITCH 2013 Steering Committee and IOS Press. All rights reserved.

关键词： business case coding common data model data validation & verification education EHR EMR General practice computerization information exchange research security & confidentiality system support training & support

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The Value of Federated data Networks in Oncology: What Research Questions Do They Answer? Outcomes From a Systematic Literature Review

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VALUE IN HEALTH 2022年第5期25卷 855-868页

作者： Hunger, Matthias Bardenheuer, Kristina Passey, Alun Schade, Rene Sharma, Ruchika Hague, Clare ICON plc Global Hlth Econ Outcomes Res & Epidemiol Dublin Ireland Janssen Pharmaceut NV Beerse Belgium Epi Hlth Econ Market Access & Reimbursement Janssen EMEA Johnson & Johnson Pl 1 D-41470 Neuss Germany

Objectives: Real-world evidence (RWE) plays an important role in addressing key research questions of interest to healthcare decision makers. Federated data networks (FDNs) apply novel technology to enable the conduct of RWE studies with multiple partners, without the need to share the individual partner's data set. A systematic review of the published literature was performed to determine which types of research questions can best be addressed through FDNs, specifically in the field of oncology. Methods: Systematic searches of MEDLINE and Embase were undertaken to identify the types of research questions that had been addressed in studies using FDNs. Additional information was retrieved about study characteristics, statistical methods, Results: In total, 40 publications were included where research questions on the following had been addressed (multiple categories possible): disease natural history (58%), safety surveillance (18%), treatment pathways (15%), comparative effectiveness (10%), and cost/resource use studies (3%)-13% of studies had to be left uncategorized. A total of 50% of the studies were run with data partners in networks of #5. The size of the networks ranged from 227 patients to .5 million patients. Statistical methods used included distributed learning and distributed regression methods. Conclusions: Further work is needed to raise awareness of the important role that FDNs can play in leveraging readily available RWE to address key research questions of interest in cancer and the benefits to the research community in engaging in federated data initiatives with a long-term perspective.

关键词： common data model data analysis evidence generation federated data network Observational Medical Outcomes Partnership oncology real-world evidence systematic literature review

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Deep-learning-based automated terminology mapping in OMOP-CDM

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JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION 2021年第7期28卷 1489-1496页

作者： Kang, Byungkon Yoon, Jisang Kim, Ha Young Jo, Sung Jin Lee, Yourim Kam, Hye Jin State Univ New York Dept Comp Sci Incheon South Korea Yonsei Univ Grad Sch Informat Seoul South Korea Pohang Univ Sci & Technol Dept Ind & Management Engn Pohang North Gyeongsan South Korea EvidNet RWE Analyt Seongnam Si Gyeonggi Do South Korea Hanwha Life New Business Unit Healthcare Life Solut Cluster 5063 Ro Seoul South Korea

Objective: Accessing medical data from multiple institutions is difficult owing to the interinstitutional diversity of vocabularies. Standardization schemes, such as the common data model, have been proposed as solutions to this problem, but such schemes require expensive human supervision. This study aims to construct a trainable system that can automate the process of semantic interinstitutional code mapping. Materials and Methods: To automate mapping between source and target codes, we compute the embedding-based semantic similarity between corresponding descriptive sentences. We also implement a systematic approach for preparing training data for similarity computation. Experimental results are compared to traditional word-based mappings. Results: The proposed model is compared against the state-of-the-art automated matching system, which is called Usagi, of the Observational Medical Outcomes Partnership common data model. By incorporating multiple negative training samples per positive sample, our semantic matching method significantly outperforms Usagi. Its matching accuracy is at least 10% greater than that of Usagi, and this trend is consistent across various top-k measurements. Discussion: The proposed deep learning-based mapping approach outperforms previous simple word-level matching algorithms because it can account for contextual and semantic information. Additionally, we demonstrate that the manner in which negative training samples are selected significantly affects the overall performance of the system. Conclusion: Incorporating the semantics of code descriptions more significantly increases matching accuracy compared to traditional text co-occurrence-based approaches. The negative training sample collection methodology is also an important component of the proposed trainable system that can be adopted in both present and future related systems.

关键词： deep-learning terminology mapping embedding common data model automated mapping

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Time Series AI model for Acute Kidney Injury Detection Based on a Multicenter Distributed Research Network: Development and Verification Study

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JMIR MEDICAL INFORMATICS 2024年 12卷 e47693页

作者： Heo, Suncheol Kang, Eun-Ae Yu, Jae Yong Kim, Hae Reong Lee, Suehyun Kim, Kwangsoo Hwangbo, Yul Park, Rae Woong Shin, Hyunah Ryu, Kyeongmin Kim, Chungsoo Jung, Hyojung Chegal, Yebin Lee, Jae-Hyun Park, Yu Rang Yonsei Univ Coll Med Dept Biomed Syst Informat 50-1 Yonsei Ro Seoul 03722 South Korea Yonsei Univ Dept Res Affairs Med Informat Collaborat Unit Coll Med Seoul South Korea Gachon Univ Dept Comp Engn Seongnam South Korea Seoul Natl Univ Hosp Transdisciplinary Dept Med & Adv Technol Seoul South Korea Natl Canc Ctr Healthcare AI Team Goyang South Korea Ajou Univ Sch Med Dept Biomed Informat Suwon South Korea Konyang Univ Hosp Healthcare Data Sci Ctr Daejeon South Korea Ajou Univ Grad Sch Med Dept Biomed Sci Suwon South Korea Korea Univ Dept Stat Seoul South Korea Yonsei Univ Coll Med Dept Internal Med Div Allergy & Immunol Seoul 120752 South Korea Yonsei Univ Inst Allergy Coll Med Seoul South Korea

Background: Acute kidney injury (AKI) is a marker of clinical deterioration and renal toxicity. While there are many studies offering prediction models for the early detection of AKI, those predicting AKI occurrence using distributed research network (DRN)-based time series data are rare. Objective: In this study, we aimed to detect the early occurrence of AKI by applying an interpretable long short-term memory (LSTM)-based model to hospital electronic health record (EHR)-based time series data in patients who took nephrotoxic drugs using a DRN. Methods: We conducted a multi-institutional retrospective cohort study of data from 6 hospitals using a DRN. For each institution, a patient-based data set was constructed using 5 drugs for AKI, and an interpretable multivariable LSTM (IMVLSTM) model was used for training. This study used propensity score matching to mitigate differences in demographics and clinical characteristics. Additionally, the temporal attention values of the AKI prediction model's contribution variables were demonstrated for each institution and drug, with differences in highly important feature distributions between the case and control data confirmed using 1-way ANOVA. Results: This study analyzed 8643 and 31,012 patients with and without AKI, respectively, across 6 hospitals. When analyzing the distribution of AKI onset, vancomycin showed an earlier onset (median 12, IQR 5-25 days), and acyclovir was the slowest compared to the other drugs (median 23, IQR 10-41 days). Our temporal deep learning model for AKI prediction performed well for most drugs. Acyclovir had the highest average area under the receiver operating characteristic curve score per drug (0.94), followed by acetaminophen (0.93), vancomycin (0.92), naproxen (0.90), and celecoxib (0.89). Based on the temporal attention values of the variables in the AKI prediction model, verified lymphocytes and calcvancomycin ium had the highest attention, whereas lymphocytes, albumin, and hemoglobin

关键词： adverse drug reaction real world data multicenter study distributed research network common data model time series AI time series artificial intelligence machine learning adverse reaction adverse reactions detect detection toxic toxicity renal kidney nephrology pharmaceutical pharmacology pharmacy pharmaceutics

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Healthcare burden changes by restricted physical activities in lumbar spinal stenosis and spondylolisthesis: a retrospective large cohort study during the COVID-19 pandemic

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BMC MUSCULOSKELETAL DISORDERS 2024年第1期25卷 411页

作者： Kim, Jun-Hoe Chegal, Yebin Kim, Suhyun Park, Hangeul Kim, Young Rak Kim, Sum Kim, Kwangsoo Lee, Chang-Hyun Kim, Chi Heon Chung, Chun Kee Seoul Natl Univ Hosp Dept Neurosurg Seoul South Korea Korea Univ Dept Stat Seoul South Korea Seoul Natl Univ Hosp Transdisciplinary Dept Med & Adv Technol Seoul South Korea Seoul Natl Univ Coll Med Dept Neurosurg 101 Daehak Ro Seoul 03080 South Korea Seoul Natl Univ Dept Brain & Cognit Sci Seoul 03080 South Korea

Background Lumbar spinal stenosis (LSS) and spondylolisthesis (SPL) are characterized as degenerative spinal pathologies and share considerable similarities. However, opinions vary on whether to recommend exercise or restrict it for these diseases. Few studies have objectively compared the effects of daily physical activity on LSS and SPL because it is impossible to restrict activities ethnically and practically. We investigated the effect of restricting physical activity due to social distancing (SoD) on LSS and SPL, focusing on the aspect of healthcare burden changes during the pandemic period. Methods We included first-visit patients diagnosed exclusively with LSS and SPL in 2017 and followed them up for two years before and after the implementation of the SoD policy. As controls, patients who first visited in 2015 and were followed for four years without SoD were analyzed. The common data model was employed to analyze each patient's diagnostic codes and treatments. Hospital visits and medical costs were analyzed by regression discontinuity in time to control for temporal effects on dependent variables. Results Among 33,484 patients, 2,615 with LSS and 446 with SPL were included. A significant decrease in hospital visits was observed in the LSS (difference, -3.94 times/month100 patients;p = 0.023) and SPL (difference, -3.44 times/month100 patients;p = 0.026) groups after SoD. This decrease was not observed in the data from the control group. Concerning medical costs, the LSS group showed a statistically significant reduction in median copayment (difference, -$45/monthpatient;p < 0.001) after SoD, whereas a significant change was not observed in the SPL group (difference, -$19/monthpatient;p = 0.160). Conclusion Restricted physical activity during the SoD period decreased the healthcare burden for patients with LSS or, conversely, it did not significantly affect patients with SPL. Under circumstances of physical inac

关键词： Lumbar spinal stenosis Spondylolisthesis COVID-19 common data model

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Increasing trust in real-world evidence through evaluation of observational data quality

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JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION 2021年第10期28卷 2251-2257页

作者： Blacketer, Clair Defalco, Frank J. Ryan, Patrick B. Rijnbeek, Peter R. Janssen Res & Dev LLC Observat Hlth Data Analyt 1125 Trenton Harbourton Rd Titusville NJ 08560 USA Erasmus MC Dept Med Informat Rotterdam Netherlands Columbia Univ Dept Biomed Informat New York NY USA

Objective: Advances in standardization of observational healthcare data have enabled methodological breakthroughs, rapid global collaboration, and generation of real-world evidence to improve patient outcomes. Standardizations in data structure, such as use of common data models, need to be coupled with standardized approaches for data quality assessment. To ensure confidence in real-world evidence generated from the analysis of real-world data, one must first have confidence in the data itself. Materials and Methods: We describe the implementation of check types across a data quality framework of conformance, completeness, plausibility, with both verification and validation. We illustrate how data quality checks, paired with decision thresholds, can be configured to customize data quality reporting across a range of observational health data sources. We discuss how data quality reporting can become part of the overall realworld evidence generation and dissemination process to promote transparency and build confidence in the resulting output. Results: The data Quality Dashboard is an open-source R package that reports potential quality issues in an OMOP CDM instance through the systematic execution and summarization of over 3300 configurable data quality checks. Discussion: Transparently communicating how well common data model-standardized databases adhere to a set of quality measures adds a crucial piece that is currently missing from observational research. Conclusion: Assessing and improving the quality of our data will inherently improve the quality of the evidence we generate.

关键词： data Quality data Standardization common data model Real World Evidence

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Fitness of real-world data for clinical trial data collection: Results and lessons from a HARMONY Outcomes ancillary study

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CLINICAL TRIALS 2022年第6期19卷 655-664页

作者： Hammill, Bradley G. Leimberger, Jeffrey D. Lampron, Zachary Raman, Sudha R. O'Brien, Emily C. Wurst, Keele E. Mountcastle, Sally Cunnington, Marianne Janmohamed, Salim Curtis, Lesley H. Duke Clin Res Inst 215 Morris St Durham NC 27701 USA Duke Univ Sch Med Durham NC USA GlaxoSmithKline Stevenage Herts England GlaxoSmithKline Res Triangle Pk NC USA GlaxoSmithKline Brentford England

Background Despite the extensive use of real-world data for retrospective, observational clinical research, our understanding of how real-world data might increase the efficiency of data collection in patient-level randomized clinical trials is largely unknown. The structure of real-world data is inherently heterogeneous, with each source electronic health record and claims database different from the next. Their fitness-for-use as data sources for multisite trials in the United States has not been established. Methods For a subset of participants in the HARMONY Outcomes Trial, we obtained electronic health record data from recruiting sites or Medicare claims data from the Centers for Medicare & Medicaid Services. For baseline characteristics and follow-up events, we assessed the level of agreement between these real-world data and data documented in the trial database. Results Real-world data-derived demographic information tended to agree with trial-reported demographic information, although real-world data were less accurate in identifying medical history. The ability of real-world data to identify baseline medication usage differed by real-world data source, with claims data demonstrating substantially better performance than electronic health record data. The limited number of lab results in the collected electronic health record data matched closely with values in the trial database. There were not enough follow-up events in the ancillary study population to draw meaningful conclusions about the performance of real-world data for identification of events. Based on the conduct of this ancillary study, the challenges and opportunities of using real-world data within clinical trials are discussed. Conclusion Based on a subset of participants from the HARMONY Outcomes Trial, our results suggest that electronic health record or claims data, as currently available, are unlikely to be a complete substitute for trial data collection of medical history or baseline lab

关键词： Electronic health record claims data common data model

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Invited Commentary: Go BIG and Go Global-Executing Large-Scale, Multisite Pharmacoepidemiologic Studies Using Real-World data

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AMERICAN JOURNAL OF EPIDEMIOLOGY 2022年第8期191卷 1368-1371页

作者： Maro, Judith C. Toh, Sengwee Harvard Pilgrim Hlth Care Inst Dept Populat Med 401 Pk DrSuite 401 East Boston MA 02215 USA Harvard Med Sch 401 Pk DrSuite 401 East Boston MA 02215 USA

At the time medical products are approved, we rarely know enough about their comparative safety and effectiveness vis-a-vis alternative therapies to advise patients and providers. Postmarket generation of evidence on rare adverse events following medical product exposure increasingly requires analysis of millions of longitudinal patient records that can provide complete capture of data on patient experiences. In the accompanying article by Pradhan et al. (Am J Epidemiology. 2022;XXX(XX):XXX-XXX), the authors demonstrate how observational database studies are often the most practical approach, provided these databases are carefully chosen to be "fit for purpose." Distributed data networks with common data models have proliferated in the last 2 decades in pharmacoepidemiology, allowing efficient capture of patient data in a standardized and structured format across disparate real-world data sources. Use of common data models facilitates transparency by allowing standardized programming approaches that can be easily reproduced. The distributed data network architecture, combined with a common data approach, supports not only multisite observational studies but also pragmatic clinical trials. It also helps bridge international boundaries and further increases the sample size and diversity of study populations.

关键词： common data model multisite studies networks pharmacoepidemiology real-world data

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Earth system curator: metadata infrastructure for climate modeling

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EARTH SCIENCE INFORMATICS 2008年第3-4期1卷 131-149页

作者： Dunlap, Rocky Mark, Leo Rugaber, Spencer Balaji, V. Chastang, Julien Cinquini, Luca DeLuca, Cecelia Middleton, Don Murphy, Sylvia Georgia Inst Technol Atlanta GA 30332 USA Princeton Univ Princeton NJ 08544 USA Natl Ctr Atmospher Res Boulder CO 80307 USA

The Earth System Curator is a National Science Foundation sponsored project developing a metadata formalism for describing the digital resources used in climate simulations. The primary motivating observation of the project is that a simulation/model's source code plus the configuration parameters required for a model run are a compact representation of the dataset generated when the model is executed. The end goal of the project is a convergence of models and data where both resources are accessed uniformly from a single registry. In this paper we review the current metadata landscape of the climate modeling community, present our work on developing a metadata formalism for describing climate models, and reflect on technical challenges we have faced that require new research in the area of Earth Science Informatics.

关键词： Climate data portal Climate modeling common data model Metadata management

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