2017 EULAR recommendations for a core data set to support observational research and clinical care in rheumatoid arthritis

Personalised medicine, new discoveries and studies on rare exposures or outcomes require large samples that are increasingly difficult for any single investigator to obtain. Collaborative work is limited by heterogeneities, both what is being collected and how it is defined. To develop a core set for data collection in rheumatoid arthritis (RA) research which (1) allows harmonisation of data collection in future observational studies, (2) acts as a common data model against which existing databases can be mapped and (3) serves as a template for standardised data collection in routine clinical practice to support generation of research-quality data. A multistep, international multistakeholder consensus process was carried out involving voting via online surveys and two face-to-face meetings. A core set of 21 items (‘what to collect’) and their instruments (‘how to collect’) was agreed: age, gender, disease duration, diagnosis of RA, body mass index, smoking, swollen/tender joints, patient/evaluator global, pain, quality of life, function, composite scores, acute phase reactants, serology, structural damage, treatment and comorbidities. The core set should facilitate collaborative research, allow for comparisons across studies and harmonise future data from clinical practice via electronic medical record systems.

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OP0285 The Eular Task Force for Standardising Minimum Data Collection in Rheumatoid Arthritis Observational Research: Results of A Hierarchical Literature Review: Table 1.

Annals of the Rheumatic Diseases ◽

10.1136/annrheumdis-2016-eular.4631 ◽

2016 ◽

Vol 75 (Suppl 2) ◽

pp. 166.1-166 ◽

Cited By ~ 2

Author(s):

K. Chatzidionysiou ◽

E. Nikiphorou ◽

L. Gossec ◽

K. Hyrich ◽

C. Filip ◽

...

Keyword(s):

Rheumatoid Arthritis ◽

Data Collection ◽

Literature Review ◽

Task Force ◽

Observational Research ◽

Research Results ◽

Minimum Data

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2018 EULAR recommendations for a core data set to support observational research and clinical care in giant cell arteritis

Annals of the Rheumatic Diseases ◽

10.1136/annrheumdis-2018-214755 ◽

2019 ◽

Vol 78 (9) ◽

pp. 1160-1166 ◽

Cited By ~ 8

Author(s):

Lisa Ehlers ◽

Johan Askling ◽

Hans WJ Bijlsma ◽

Maria Cinta Cid ◽

Maurizio Cutolo ◽

...

Keyword(s):

Clinical Research ◽

Giant Cell Arteritis ◽

Giant Cell ◽

Task Force ◽

Systemic Vasculitis ◽

Clinical Care ◽

Observational Research ◽

Disease Course ◽

Data Set ◽

Core Set

Giant cell arteritis (GCA) represents the most common form of primary systemic vasculitis and is frequently associated with comorbidities related to the disease itself or induced by the treatment. Systematically collected data on disease course, treatment and outcomes of GCA remain scarce. The aim of this EULAR Task Force was to identify a core set of items which can easily be collected by experienced clinicians, in order to facilitate collaborative research into the course and outcomes of GCA. A multidisciplinary EULAR task force group of 20 experts including rheumatologists, internists, epidemiologists and patient representatives was assembled. During a 1-day meeting, breakout groups discussed items from a previously compiled collection of parameters describing GCA status and disease course. Feedback from breakout groups was further discussed. Final consensus was achieved by means of several rounds of email discussions after the meeting. A three-round Delphi survey was conducted to determine a core set of parameters including the level of agreement. 117 parameters were regarded as relevant. Potential items were subdivided into the following categories: General, demographics, GCA-related signs and symptoms, other medical conditions and treatment. Possible instruments and assessment intervals were proposed for documentation of each item. To facilitate implementation of the recommendations in clinical care and clinical research, a minimum core set of 50 parameters was agreed. This proposed core set intends to ensure that relevant items from different GCA registries and databases can be compared for the dual purposes of facilitating clinical research and improving clinical care.

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An Overview of ARM Program Climate Research Facility Data Quality Assurance

The Open Atmospheric Science Journal ◽

10.2174/1874282300802010192 ◽

2008 ◽

Vol 2 (1) ◽

pp. 192-216 ◽

Cited By ~ 15

Author(s):

R.A. Peppler ◽

C.N. Long ◽

D.L. Sisterson ◽

D.D. Turner ◽

C.P. Bahrmann ◽

...

Keyword(s):

Quality Assurance ◽

Data Collection ◽

Data Quality ◽

Data Stream ◽

Value Added ◽

Quality Data ◽

Quality Assurance Program ◽

Research Facility ◽

Data Set ◽

Climate Research

We present an overview of key aspects of the Atmospheric Radiation Measurement (ARM) Program Climate Research Facility (ACRF) data quality assurance program. Processes described include instrument deployment and calibration; instrument and facility maintenance; data collection and processing infrastructure; data stream inspection and assessment; problem reporting, review and resolution; data archival, display and distribution; data stream reprocessing; engineering and operations management; and the roles of value-added data processing and targeted field campaigns in specifying data quality and characterizing field measurements. The paper also includes a discussion of recent directions in ACRF data quality assurance. A comprehensive, end-to-end data quality assurance program is essential for producing a high-quality data set from measurements made by automated weather and climate networks. The processes developed during the ARM Program offer a possible framework for use by other instrumentation- and geographically-diverse data collection networks and highlight the myriad aspects that go into producing research-quality data.

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Efficient capture of high-quality real-world data on treatments for glaucoma: the Fight Glaucoma Blindness! Registry

BMJ Open Ophthalmology ◽

10.1136/bmjophth-2021-000903 ◽

2021 ◽

Vol 6 (1) ◽

pp. e000903

Author(s):

Mitchell Lawlor ◽

Vuong Nguyen ◽

Anne Brooks ◽

Colin Clement ◽

Jamie E Craig ◽

...

Keyword(s):

Data Collection ◽

Real World ◽

Steering Committee ◽

Quality Data ◽

Easy Access ◽

Routine Practice ◽

Real World Data ◽

High Quality ◽

Data Set ◽

Web Based

ObjectiveTo describe the development and implementation of a web-based high-quality data collection tool to track the outcomes of glaucoma treatments in routine practice.Methods and analysisThis is a prospective observational registry study. An international steering committee undertook an iterative structured process to define a minimum, patient-centred data set designed to track outcomes of glaucoma treatment. The outcomes were coded into a web-based programme allowing easy access for rapid data entry. Clinicians receive personal reports enabling instant audit of their outcomes. Analyses of aggregated anonymised data on real-world outcomes are analysed and periodically reported with the goal of improving patient care.ResultsThe minimum data set developed by the international steering committee includes the following: a baseline visit captures 13 mandatory fields in order to accurately phenotype each patient’s subtype of glaucoma and to allow comparison between services, and a follow-up visit includes only four mandatory fields to allow completion within 30 s.Currently, there are 157 surgeons in 158 ophthalmology practices across Australia and New Zealand who are registered. These surgeons are tracking 5570 eyes of 3001 patients and have recorded 67 074 visits. The median number of eyes per surgeon is 22 eyes with a range of 1–575. The most common glaucoma procedure, excluding cataract surgery, is iStent inject, with 2316 cases.ConclusionThis software tool effectively facilitates data collection on safety and efficacy outcomes of treatments for different subgroups of glaucoma within a real-world setting. It provides a template to evaluate new treatments as they are introduced into practice.

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COVID-19 in people with multiple sclerosis: A global data sharing initiative

Multiple Sclerosis Journal ◽

10.1177/1352458520941485 ◽

2020 ◽

Vol 26 (10) ◽

pp. 1157-1162 ◽

Cited By ~ 2

Author(s):

Liesbet M Peeters ◽

Tina Parciak ◽

Clare Walton ◽

Lotte Geys ◽

Yves Moreau ◽

...

Keyword(s):

Data Collection ◽

Data Sharing ◽

Scale Up ◽

Global Scale ◽

Quality Data ◽

Data Set ◽

Core Data ◽

Task Forces ◽

Multiple Partners ◽

Share Data

Background: We need high-quality data to assess the determinants for COVID-19 severity in people with MS (PwMS). Several studies have recently emerged but there is great benefit in aligning data collection efforts at a global scale. Objectives: Our mission is to scale-up COVID-19 data collection efforts and provide the MS community with data-driven insights as soon as possible. Methods: Numerous stakeholders were brought together. Small dedicated interdisciplinary task forces were created to speed-up the formulation of the study design and work plan. First step was to agree upon a COVID-19 MS core data set. Second, we worked on providing a user-friendly and rapid pipeline to share COVID-19 data at a global scale. Results: The COVID-19 MS core data set was agreed within 48 hours. To date, 23 data collection partners are involved and the first data imports have been performed successfully. Data processing and analysis is an on-going process. Conclusions: We reached a consensus on a core data set and established data sharing processes with multiple partners to address an urgent need for information to guide clinical practice. First results show that partners are motivated to share data to attain the ultimate joint goal: better understand the effect of COVID-19 in PwMS.

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Building a Centre of Excellence in Data Management in East Africa

10.21203/rs.3.rs-779411/v1 ◽

2021 ◽

Author(s):

Betty Agwang ◽

Yuka Manabe

Keyword(s):

Data Collection ◽

Infectious Diseases ◽

Clinical Research ◽

Data Management ◽

The United States ◽

Sub Saharan Africa ◽

Research Quality ◽

Data Management System ◽

Quality Data ◽

High Quality

Abstract Background: In resource-limited settings, there is a paucity of high quality data management systems for clinical research. The result is that data are often managed in high-income countries disadvantaging researchers at sites where the data are collected. An institutional data management system to address the data collection concerns of the collaborators and sponsors is a key institutional capacity element for high quality research. Our goal was to build a local data management center to streamline data collection and validation compliant with international regulatory bodies. Methods: Leveraging established collaborations between Office of Cyber Infrastructure and Computational Biology of the National Institutes of Health and the John Hopkins University School of Medicine in the United States, the Infectious Diseases Institute at Makerere University built a data management coordinating center. This included mentorship from the NIAID International Centers for Excellence in Research and training of key personnel in South Africa at a functioning data center. The number of studies, case report forms processed and the number of publications emanating from studies using the data management unit since its inception were tabulated. Results: The Infectious Diseases Institute data management core began processing data in 2009 with 3 personnel, hardware (network-enabled scanners, desktops, server held in Bethesda with nightly back up) and software licenses, in addition to on-site support from the NIH. In the last 10 years, 850,869 pages of data have been processed from 60 studies in Uganda, across sub-Saharan Africa, Asia and South America. Real-time data cleaning and data analysis occur routinely and enhance clinical research quality; a total of 212 publications from IDI investigators have been published over the past 10 years. Apart from the server back-up services provided by the NIH, the center is now self-sustaining from fees charged to individual studies. Conclusion: Collaborative partnership among research institutions enabled the IDI to build a core data management and coordination center to support clinical studies, build institutional research capacity, and to advance data quality and integrity for the investigators and sponsors.

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Methods of data collection and water-quality data for Standley Lake, Jefferson County, Colorado, 1989-90

Open-File Report ◽

10.3133/ofr9244 ◽

1992 ◽

Author(s):

Barbara C. Ruddy ◽

David A. Johncox ◽

David K. Mueller

Keyword(s):

Water Quality ◽

Data Collection ◽

Quality Data ◽

Jefferson County ◽

Water Quality Data

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Collecting High-Quality Data

10.1093/oso/9780199366088.003.0007 ◽

2018 ◽

Author(s):

Mary Kay Gugerty ◽

Dean Karlan

Keyword(s):

Data Collection ◽

Social Desirability ◽

Measurement Errors ◽

Monitoring And Evaluation ◽

Quality Data ◽

Social Desirability Bias ◽

High Quality ◽

High Quality Data ◽

Evaluation Systems ◽

Demand Effect

Without high-quality data, even the best-designed monitoring and evaluation systems will collapse. Chapter 7 introduces some the basics of collecting high-quality data and discusses how to address challenges that frequently arise. High-quality data must be clearly defined and have an indicator that validly and reliably measures the intended concept. The chapter then explains how to avoid common biases and measurement errors like anchoring, social desirability bias, the experimenter demand effect, unclear wording, long recall periods, and translation context. It then guides organizations on how to find indicators, test data collection instruments, manage surveys, and train staff appropriately for data collection and entry.

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