ReviewA systematic review of validated methods for identifying patients with rheumatoid arthritis using administrative or claims data
Introduction
Mini-Sentinel, a pilot project sponsored by the United States Food and Drug Administration (FDA), aims to inform and facilitate the development of an active surveillance system, the Sentinel System, for monitoring the safety of FDA-regulated medical products [1]. Mini-Sentinel is one facet of the Sentinel Initiative, an FDA effort to develop a national electronic system that will complement existing methods of safety surveillance.
To support this goal, Mini-Sentinel uses administrative and claims data to examine relationships between medical product exposures and health outcomes [1], [2]. This serves to refine safety signals and facilitate active surveillance of adverse events potentially related to medical products. A first step in developing the Sentinel system is to understand the validity of algorithms (i.e., combinations of billing, procedural, or diagnosis codes, or pharmacy claims) for identifying health outcomes of interest in administrative data. Mini-Sentinel program collaborators selected health outcomes of interest using an expert elicitation process through which investigators developed a list of candidate outcomes based on input from global vaccine safety experts. A panel of 5 vaccine experts then prioritized the list via an iterative process using criteria including clinical severity, public health importance, incidence, and relevance [2].
A relationship between vaccination and autoimmune diseases such as Guillain Barré, multiple sclerosis, and type 1 diabetes has been suggested primarily based on series of individual case safety reports [3]. In rheumatology, a case series of patients developing rheumatoid arthritis (RA) after hepatitis B vaccination [4], suggested that vaccines may precipitate rheumatic autoimmune diseases, although controversy remains since previous studies failed to confirm that association [5], [6] and did not find evidence for other vaccines including tetanus and influenza [7].
RA is a common disease that affects 1% of the population [8]. Patients with RA die prematurely and are at increased risk of multiple comorbidities, including infections [9]. The last two decades have brought significant changes in the management of patients with RA. These are summarized in early treatment and tight control of inflammation with the use of traditional disease-modifying antirheumatic drugs (DMARDs) and/or new biologic agents. The goal is to achieve low disease activity or remission.
Current guidelines from the American College of Rheumatology (ACR) review the use of traditional DMARDs, biologic agents, monitoring for side effects, tuberculosis screening, and the need for vaccinations in patients starting or receiving DMARDs or biologic agents [10]. However, optimal clinical use of these drugs requires accurate determination of the risks associated with their use. In rheumatology, many studies are focused on the safety of traditional and biologic DMARDs. To facilitate these studies, investigators have developed algorithms to identify patients with RA. These strategies include using multiple diagnosis codes or sets of codes and medications to define the presence of a disease.
The goal of this project was to identify algorithms used to detect RA using administrative data sources and to describe the performance characteristics of these algorithms as reported by the studies in which they were used.
Section snippets
Materials and methods
A detailed description of the methods for the project can be found in the accompanying paper by McPheeters et al. [11]. Briefly, we searched the MEDLINE database via the PubMed interface using the strategies outlined in Appendix A. We also checked the reference lists of included studies for additional relevant citations. The search strategy was developed by building on prior Mini-Sentinel approaches to searching [12]. We expanded those approaches and tested the need to incorporate additional
Results
We identified 1218 non-duplicate citations with potential relevance; of these, 580 required full-text review. Of these, 99 studies met our inclusion criteria, and of these, nine reported methods for confirming cases identified and reported the number of cases confirmed (Fig. 1). These studies are the focus of this report (Table 1). Table 2 provides definitions for each code used in these nine studies. The other studies meeting overall inclusion criteria provided algorithms but no discussion of
Discussion
Studies assessing the validity of coding algorithms for RA reported PPVs that ranged from 34% [18] to 97% [14], reflecting the effects of coding accuracy (including physician experience with diagnosis of the disease [24]), variation in populations, range of approaches, algorithms, and gold standards. All but one study [22] used the medical record to identify the gold standard, but the gold standard definitions differed in important ways, including the requirement in some studies that the
Funding source
Mini-Sentinel is funded by the Food and Drug Administration (FDA) through Department of Health and Human Services (HHS) Contract Number HHSF223200910006I. The views expressed in this document do not necessarily reflect the official policies of the Department of Health and Human Services, nor does mention of trade names, commercial practices, or organizations imply endorsement by the U.S. government. CPC was funded by the Vanderbilt Physician Scientist Development Award.
Role of the funding source
FDA staff reviewed articles prior to publication but had no role in study design or conduct.
Authorship statement
All authors declare that they have participated in: (1) the conception and design of the study, or acquisition of data, or analysis and interpretation of data, (2) drafting the article or revising it critically for important intellectual content, (3) final approval of the version submitted.
Conflict of interest
The authors have no conflicts to declare.
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