scholarly journals Common Data Elements for Meaningful Stroke Documentation in Routine Care and Clinical Research (Preprint)

10.2196/27396 ◽  
2021 ◽  
Author(s):  
Sarah Berenspöhler ◽  
Jens Minnerup Sr ◽  
Martin Dugas Sr ◽  
Julian Varghese
Keyword(s):  
Clinical Research ◽  
Routine Care ◽  
Common Data Elements ◽  
Data Elements

Common Data Elements for Meaningful Stroke Documentation in Routine Care and Clinical Research (Preprint)

2021 ◽  
Author(s):  
Sarah Berenspöhler ◽  
Jens Minnerup Sr ◽  
Martin Dugas Sr ◽  
Julian Varghese
Keyword(s):  
Clinical Research ◽  
Information Management ◽  
Medical Information ◽  
Routine Care ◽  
Free Form ◽  
Common Data Elements ◽  
Core Dataset ◽  
Source Data ◽  
Data Elements ◽  
Medical Concepts

BACKGROUND The medical information management regarding stroke patients is currently a very time-consuming endeavour. There are clear guidelines and procedures to treat patients suffering from an acute stroke - but how well are these established practices reflected in patient documentation? This paper compares a variety of documentation processes regarding stroke. The main objective of this work is to provide an overview regarding the most commonly occurring medical concepts in stroke documentation and identify overlaps between different documentation contexts to allow for the definition of a core dataset that could be utilized in potential data interfaces. OBJECTIVE A list of most common data elements could be identified to pave the way for a core dataset in stroke care and research. METHODS Medical source documentation forms from different documentation contexts including hospitals, clinical trials, registries and international standards regarding stroke treatment with following rehabilitation were digitized in the Operational Data Model (ODM). Each source data element was semantically annotated using the Unified Medical Language System (UMLS). Concept codes were analysed for semantic overlaps. A concept was considered to be common if it appeared at least in two documentation contexts. The resulting common concepts were extended with implementation details including data types and permissible values based on frequent patterns of source data elements using an established expert-based and semi-automatic approach. RESULTS In total, 3287 data elements were identified and 1051 of these emerged as unique medical concepts. The 100 most frequent medical concepts cover 50% of all concept occurrences in the stroke documentations and the 50 most frequent concepts cover 34%. A list of common data elements was implemented in different standardized machine-readable formats on a public metadata repository for interoperable re-use. CONCLUSIONS Standardization of medical documentation is a prerequisite for data exchange as well as the transferability and reutilization of data. In the long run standardization would lead to saving time and money and extend the capabilities such data could be used for. In the context of this work a lack of standardization was observed regarding the current information management. Free form text fields and intricate questions complicate the automated data access and transfer between institutions. This work also revealed the potential of a unified documentation process as a core dataset of the 50 most frequent CDEs already accounts for 34% of the documentations in medical information management. Such a dataset offers a starting point for a standardized and interoperable data collection in routine care, quality management and clinical research.

scholarly journals Postmarketing Safety Study Tool: A Web Based, Dynamic, and Interoperable System for Postmarketing Drug Surveillance Studies

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
A. Anil Sinaci ◽  
Gokce B. Laleci Erturkmen ◽  
Suat Gonul ◽  
Mustafa Yuksel ◽  
Paolo Invernizzi ◽  
...  
Keyword(s):  
Clinical Research ◽  
Real Life ◽  
Critical Factor ◽  
Drug Surveillance ◽  
Safety Study ◽  
Data Interoperability ◽  
Common Data Elements ◽  
Lombardy Region ◽  
Research Activities ◽  
Data Elements

Postmarketing drug surveillance is a crucial aspect of the clinical research activities in pharmacovigilance and pharmacoepidemiology. Successful utilization of available Electronic Health Record (EHR) data can complement and strengthen postmarketing safety studies. In terms of the secondary use of EHRs, access and analysis of patient data across different domains are a critical factor; we address this data interoperability problem between EHR systems and clinical research systems in this paper. We demonstrate that this problem can be solved in an upper level with the use of common data elements in a standardized fashion so that clinical researchers can work with different EHR systems independently of the underlying information model. Postmarketing Safety Study Tool lets the clinical researchers extract data from different EHR systems by designing data collection set schemas through common data elements. The tool interacts with a semantic metadata registry through IHE data element exchange profile. Postmarketing Safety Study Tool and its supporting components have been implemented and deployed on the central data warehouse of the Lombardy region, Italy, which contains anonymized records of about 16 million patients with over 10-year longitudinal data on average. Clinical researchers in Roche validate the tool with real life use cases.

scholarly journals Development of Common Data Elements for Use in Chiari Malformation Type I Clinical Research: An NIH/NINDS Project

Neurosurgery ◽  
2019 ◽  
Vol 85 (6) ◽  
pp. 854-860 ◽  
Author(s):  
Mark G Luciano ◽  
Ulrich Batzdorf ◽  
Roger W Kula ◽  
Brandon G Rocque ◽  
Cormac O Maher ◽  
...  
Keyword(s):  
Clinical Research ◽  
Data Sharing ◽  
Chiari I Malformation ◽  
Type I ◽  
Design Data ◽  
Chiari Malformation Type I ◽  
Common Data Elements ◽  
Clinical Research Trial ◽  
Symptom Patterns ◽  
Data Elements

ABSTRACT The management of Chiari I malformation (CMI) is controversial because treatment methods vary and treatment decisions rest on incomplete understanding of its complex symptom patterns, etiologies, and natural history. Validity of studies that attempt to compare treatment of CMI has been limited because of variable terminology and methods used to describe study subjects. The goal of this project was to standardize terminology and methods by developing a comprehensive set of Common Data Elements (CDEs), data definitions, case report forms (CRFs), and outcome measure recommendations for use in CMI clinical research, as part of the CDE project at the National Institute of Neurological Disorders and Stroke (NINDS) of the US National Institutes of Health. A working group, comprising over 30 experts, developed and identified CDEs, template CRFs, data dictionaries, and guidelines to aid investigators starting and conducting CMI clinical research studies. The recommendations were compiled, internally reviewed, and posted online for external public comment. In October 2016, version 1.0 of the CMI CDE recommendations became available on the NINDS CDE website. The recommendations span these domains: Core Demographics/Epidemiology; Presentation/Symptoms; Co-Morbidities/Genetics; Imaging; Treatment; and Outcome. Widespread use of CDEs could facilitate CMI clinical research trial design, data sharing, retrospective analyses, and consistent data sharing between CMI investigators around the world. Updating of CDEs will be necessary to keep them relevant and applicable to evolving research goals for understanding CMI and its treatment.

Abstract WP203: National Institute of Neurological Disorders and Stroke Common Data Elements: Public Review and Updates to the Stroke Recommendations

Stroke ◽  
2020 ◽  
Vol 51 (Suppl_1) ◽  
Author(s):  
Katelyn Gay ◽  
Damon Collie ◽  
Muniza Sheikh ◽  
Joy Esterlitz ◽  
Jeffrey Saver ◽  
...  
Keyword(s):  
Data Collection ◽  
Clinical Research ◽  
Neurological Disorders ◽  
Vital Signs ◽  
Cerebral Aneurysms ◽  
Research Data ◽  
Common Data Element ◽  
Common Data Elements ◽  
Stroke Research ◽  
Data Elements

Objective: The National Institute of Neurological Disorders and Stroke (NINDS) initiated the Common Data Element (CDE) project to provide standardized clinical research data collection formats that increase the efficiency and effectiveness of studies and reduce start-up time, as well as improve data quality and facilitate and accelerate data sharing. In 2010, Stroke-specific CDEs were posted on the NINDS CDE website. The Stroke Oversight Committee (OC) reviewed Core CDEs in 2015; and in 2018, recommended that Stroke CDEs undergo a comprehensive review and update to Version 2.0. Background: In August 2018, a Stroke V2.0 Working Group (WG) consisting of over 50 worldwide subject matter experts was convened by NINDS. The WG was asked to review all current Stroke CDEs and subarachnoid hemorrhage and unruptured cerebral aneurysms (SAH) CDEs (developed in 2017) for harmonization and inclusion within Stroke V2.0. Methods: The Stroke V2.0 WG divided into eight domain-specific subgroups: Biospecimens, Biomarkers, and Laboratory Tests; Hospital Course and Acute Therapies; Imaging; Long Term Therapies; Medical History and Prior Health Status; Outcomes and Endpoints; Stroke Presentation and Vital Signs; and Stroke Types and Subtypes. Subgroups met regularly to review, revise and add to the existing Stroke CDEs based on developments in stroke research. Following an internal WG review, a public review of the draft updates will be held. The WG will consider public feedback before V2.0 is finalized. The Stroke OC plans to review the project status at the 2020 International Stroke Conference. Results: The Stroke V2.0 CDE recommendations will include updated and new template case report forms, data dictionaries, instrument informational documents and guideline documents. The updates will reflect the current state of science, streamline CDE recommendations, and incorporate SAH CDEs. Stroke V2.0 CDEs will be available on the NINDS CDE website in 2020. Conclusions: The NINDS CDEs are periodically revised as research progresses. Through the update of the Stroke CDEs to V2.0, the initiative strives to maintain the utility of CDEs as a valuable clinical research resource. NINDS encourages use of CDEs to standardize research data collection across studies.

Abstract WP332: Updating the Structure of Stroke Clinical Research Data: Version 2 of the Stroke Common Data Elements From the National Institutes of Health/National Institute of Neurological Disorders and Stroke

Stroke ◽  
2016 ◽  
Vol 47 (suppl_1) ◽  
Author(s):  
Joy R Esterlitz ◽  
Jeffrey L Saver ◽  
Steven Warach ◽  
Thomas G Brott ◽  
Ralph L Sacco ◽  
...  
Keyword(s):  
Clinical Research ◽  
Data Sharing ◽  
Neurological Disorders ◽  
National Institutes Of Health ◽  
Common Data Elements ◽  
Start Up ◽  
Efficiency And Effectiveness ◽  
Data Elements ◽  
Study Type

Introduction: In order to increase the efficiency and effectiveness of neurovascular clinical research studies, increase data quality, facilitate data sharing, help educate new clinical investigators and reduce study start-up time, the National Institute of Neurological Disorders and Stroke (NINDS) convened a Working Group (WG) that developed Version 1.0 (published 2010) Stroke-specific Common Data Elements (CDEs). Since their initial publication, intervening advances in science and initial experience with the CDEs identified a need to update them and refine guidance on their deployment. Hypothesis/Objective: The NINDS has updated guidance on uniform data structures for use in cerebrovascular research in epidemiology, clinical trials and imaging studies in order to advance the prevention, acute treatment and recovery from cerebrovascular disease. Methods: The NINDS convened experts in research and data element design drawing strongly from investigators in the NIH StrokeNet and other NINDS clinical research projects. Results: Stroke CDE leadership developed a revised process for classifying Stroke CDEs among the four hierarchical categories of Core, Supplemental - Highly Recommended, Supplemental and Exploratory. Due to the heterogeneity of stroke conditions and study types, the classification of Supplemental - Highly Recommended was used for study type (clinical trial or observational), disease type (e.g., ischemic stroke, intracerebral hemorrhage, subarachnoid hemorrhage) and disease phase (primary prevention, acute, recovery and secondary prevention). Conclusion: The second iteration of NINDS CDE recommendations for neurovascular disease is an important step towards more efficient study start-up time and improved data sharing. The updated CDEs were released on the NINDS CDE website in May 2015. The information at this meeting will include examples of how the Stroke CDEs may be used by a research study, an explanation of the new CDE classifications, and examples of navigating and selecting CDEs from the NINDS CDE website. Support: This project was funded by HHSN271201200034C.

scholarly journals Common data elements for clinical research in mitochondrial disease: a National Institute for Neurological Disorders and Stroke project

2017 ◽  
Vol 40 (3) ◽  
pp. 403-414 ◽  
Author(s):  
Amel Karaa ◽  
◽  
Shamima Rahman ◽  
Anne Lombès ◽  
Patrick Yu-Wai-Man ◽  
...  
Keyword(s):  
Clinical Research ◽  
Mitochondrial Disease ◽  
Neurological Disorders ◽  
Common Data Elements ◽  
Data Elements

1.003 FACILITATING CLINICAL RESEARCH: THE NATIONAL INSTITUTE OF NEUROLOGICAL DISORDERS (NINDS) AND STROKE PARKINSON'S DISEASE COMMON DATA ELEMENTS PROJECT

2012 ◽  
Vol 18 ◽  
pp. S10-S11 ◽  
Author(s):  
C.M. Tanner ◽  
K. Kieburtz ◽  
W. Galpern ◽  
M. Delong ◽  
D. Dickson ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Clinical Research ◽  
Neurological Disorders ◽  
Common Data Elements ◽  
Data Elements

scholarly journals Design and Rationale for Common Data Elements for Clinical Research in Pediatric Critical Care Medicine

2020 ◽  
Vol 21 (11) ◽  
pp. e1038-e1041
Author(s):  
Shan L. Ward ◽  
Heidi R. Flori ◽  
Tellen D. Bennett ◽  
Anil Sapru ◽  
Peter M. Mourani ◽  
...  
Keyword(s):  
Critical Care ◽  
Clinical Research ◽  
Critical Care Medicine ◽  
Common Data Elements ◽  
Pediatric Critical Care ◽  
Data Elements
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