Dataset Information

Content and criterion validity of the preliminary core dataset for clinical trials in fibromyalgia syndrome.

ABSTRACT: OBJECTIVE:Increasing research interest and emerging new therapies for treatment of fibromyalgia (FM) have led to a need to develop a consensus on a core set of outcome measures that should be assessed and reported in all clinical trials, to facilitate interpretation of the data and understanding of the disease. This aligns with the key objective of the Outcome Measures in Rheumatology (OMERACT) initiative to improve outcome measurement through a data driven, interactive consensus process. METHODS:Through patient focus groups and Delphi processes, working groups at previous OMERACT meetings identified potential domains to be included in the core data set. A systematic review has shown that instruments measuring these domains are available and are at least moderately sensitive to change. Most instruments have been validated in multiple languages. This pooled analysis study aims to develop the core data set by analyzing data from 10 randomized controlled trials (RCT) in FM. RESULTS:Results from this study provide support for the inclusion of the following in the core data set: pain, tenderness, fatigue, sleep, patient global assessment, and multidimensional function/health related quality of life. Construct validity was demonstrated with outcome instruments showing convergent and divergent validity. Content and criterion validity were confirmed by multivariate analysis showing R square values between 0.4 and 0.6. Low R square value is associated with studies in which one or more domains were not assessed. CONCLUSION:The core data set was supported by high consensus among attendees at OMERACT 9. Establishing an international standard for RCT in FM should facilitate future metaanalyses and indirect comparisons.

SUBMITTER: Choy EH

PROVIDER: S-EPMC3412585 | biostudies-other | 2009 Oct

REPOSITORIES: biostudies-other

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Content and criterion validity of the preliminary core dataset for clinical trials in fibromyalgia syndrome.

Choy Ernest H EH Arnold Lesley M LM Clauw Daniel J DJ Crofford Leslie J LJ Glass Jennifer M JM Simon Lee S LS Martin Susan A SA Strand C Vibeke CV Williams David A DA Mease Philip J PJ

The Journal of rheumatology 20091001 10

<h4>Objective</h4>Increasing research interest and emerging new therapies for treatment of fibromyalgia (FM) have led to a need to develop a consensus on a core set of outcome measures that should be assessed and reported in all clinical trials, to facilitate interpretation of the data and understanding of the disease. This aligns with the key objective of the Outcome Measures in Rheumatology (OMERACT) initiative to improve outcome measurement through a data driven, interactive consensus process ...[more]

PMID: 19820222

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Project description:BackgroundScreening for eligible patients continues to pose a great challenge for many clinical trials. This has led to a rapidly growing interest in standardizing computable representations of eligibility criteria (EC) in order to develop tools that leverage data from electronic health record (EHR) systems. Although laboratory procedures (LP) represent a common entity of EC that is readily available and retrievable from EHR systems, there is a lack of interoperable data models for this entity of EC. A public, specialized data model that utilizes international, widely-adopted terminology for LP, e.g. Logical Observation Identifiers Names and Codes (LOINC®), is much needed to support automated screening tools.ObjectiveThe aim of this study is to establish a core dataset for LP most frequently requested to recruit patients for clinical trials using LOINC terminology. Employing such a core dataset could enhance the interface between study feasibility platforms and EHR systems and significantly improve automatic patient recruitment.MethodsWe used a semi-automated approach to analyze 10,516 screening forms from the Medical Data Models (MDM) portal's data repository that are pre-annotated with Unified Medical Language System (UMLS). An automated semantic analysis based on concept frequency is followed by an extensive manual expert review performed by physicians to analyze complex recruitment-relevant concepts not amenable to automatic approach.ResultsBased on analysis of 138,225 EC from 10,516 screening forms, 55 laboratory procedures represented 77.87% of all UMLS laboratory concept occurrences identified in the selected EC forms. We identified 26,413 unique UMLS concepts from 118 UMLS semantic types and covered the vast majority of Medical Subject Headings (MeSH) disease domains.ConclusionsOnly a small set of common LP covers the majority of laboratory concepts in screening EC forms which supports the feasibility of establishing a focused core dataset for LP. We present ELaPro, a novel, LOINC-mapped, core dataset for the most frequent 55 LP requested in screening for clinical trials. ELaPro is available in multiple machine-readable data formats like CSV, ODM and HL7 FHIR. The extensive manual curation of this large number of free-text EC as well as the combining of UMLS and LOINC terminologies distinguishes this specialized dataset from previous relevant datasets in the literature.

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Dataset Information

Content and criterion validity of the preliminary core dataset for clinical trials in fibromyalgia syndrome.

Publications

Content and criterion validity of the preliminary core dataset for clinical trials in fibromyalgia syndrome.

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