meddra coding points to consider when investing
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Meddra coding points to consider when investing subliminal wealth creation investing

Meddra coding points to consider when investing

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Not yet a member of Life Science Leader? Register today. Sign up for the newsletter that brings you the industry's latest news, technologies, trends and products. Log In or Subscribe. Webinar June 29, Source: Premier Research. Topics in this presentation include: The history and evolution of medical coding The importance of coding dictionaries The effect of modern electronic data capture systems, integrated coding tools, and artificial intelligence on coding How a functional service provider model can guarantee true medical coding standardization Presenter: Stefan Georgiev, Associate Director, Functional Services.

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Learn more about navigating our updated article layout. The PMC legacy view will also be available for a limited time. Federal government websites often end in. The site is secure. Monitoring adverse events AEs is an important part of clinical research and a crucial target for data standards.

The representation of adverse events themselves requires the use of controlled vocabularies with thousands of needed clinical concepts. Several data standards for adverse events currently exist, each with a strong user base. The structure and features of these current adverse event data standards including terminologies and classifications are different, so comparisons and evaluations are not straightforward, nor are strategies for their harmonization.

This paper describes the structural features of each coding system, their content and relationship to the Unified Medical Language System UMLS , and unsettled issues for future interoperability of these standards. An adverse event AE is broadly defined as any clinical event, sign, or symptom that goes in an unwanted direction. Food and Drug Administration and ICH International Conference on Harmonisation definitions, adverse events also include worsening of pre-existing conditions, so comprehensive AE coding systems must encompass diseases, disorders, and conditions as well.

The structure and features of contending adverse event data standards including terminologies and classifications are different, so comparisons between and evaluations of data standards are not straightforward, nor are activities for determining concept equivalence across them. These data standards are weighed against general standards criteria and terminology desiderata, and studied in relation to the Unified Medical Language System UMLS.

We use the UMLS as a tool to examine overlap between the candidate standards. Our description of these heterogeneous coding systems and insights on interoperability strategies should provide a timely resource for current discussions of uniform representation for adverse events and clinical research data. Adverse event data drives decisions to terminate a study, or revise protocols, procedures or informed consent documents, in the interest of protecting human subjects.

The need to monitor adverse events is a fundamental part of clinical research 1 , and the rationale for standard coding of AEs is similar to that for other clinical research data — it enables researchers to record information in a consistent manner. Data standards facilitate the sharing of data, and shared AE data has patient safety implications.

Various functions for uniform AE data representation - including standardized AE reporting systems, the extraction of AEs from existing clinical care data sources, adverse event data management and communication systems, and aggregate analysis - dictate the requirements for an ideal AE data standard, and influence future relationships between competing data standards.

MedDRA is a medical terminology used by the regulated biopharmaceutical industry for data related to pre-marketing to post-marketing data reporting activities. SNOMED CT is a large, formalized, comprehensive medical terminology intended to represent anything of clinical relevance in electronic medical records. In addition to the nearly , pre-coordinated terms, SNOMED CT has the terminology model, concepts, and relationships to construct almost any desired concept.

SNOMED CT is a multi-hierarchical terminology, meaning that concepts can have more than one parent concept, usually from within the same hierarchy or axis , although there is a very small number of cases of cross-axial hierarchical relationships. Figure II. Additionally, the sophisticated terminology model of SNOMED CT allows concepts to be associated through a host of relationships — other than hierarchical — with other concepts e. The U. Regardless, SNOMED CT will have a relationship with other adverse event coding systems as use cases and applications that bring together clinical research and healthcare information systems emerge.

For any given adverse event, specific definitional criteria are listed by grade. Figure III These clinical criteria are not required — there are often several clinical criteria that constitute the description for each AE and grade, yet only one need be present for inclusion in that grade. Access to the classification, training, and supporting tools from the NCI is free to all users.

The UMLS is a multi-purpose resource that includes concepts and terms from over different source vocabularies, and establishes linkages across these source vocabularies with its own concept structure and semantic type categorization of concepts in the biomedical domain. The Semantic Network is designed to categorize concepts using semantic types in the UMLS Metathesaurus and organize relationships among the concept categories using semantic relationships.

Semantic types and relationships correspond to nodes and links in graphical representations of the Semantic Network. The UMLS tools support the mapping of multiple data standards to a common set of concepts via the Metathesaurus The purpose of the Metathesaurus is to provide a common representation for information exchange between health-related information systems using various coding systems and vocabularies.

The UMLS provides mappings across terms from different terminologies by integrating terms from multiple source terminologies with the same meanings into the same UMLS concept in the Metathesaurus. We use the UMLS concept as a lingua franca to determine synonymy and, as a result content overlap, across the three AE coding systems described above.

In order to get a quantitative sense of the coverage and overlap of the three vocabularies, we present some counts of the source terms and their corresponding UMLS concepts based on the AA release of UMLS. MedDRA is significantly smaller, and contains 17, preferred terms - the level of granularity most commonly used in AE reporting.

The CTCAE has 1, preferred terms ignoring the hierarchical terms and terms beginning with grading information. There are , preferred terms in the findings axis, which is about 6 times greater in size than MedDRA.

Overlap is defined by the presence of preferred terms from different terminologies within the same UMLS concept. Hierarchical relationships are present in all 3 coding systems. In general, the number of hierarchical relationships can be a measure of the depth and granularity of a terminology or classification. In contrast, the structure of the CTCAE classification only has two levels of depth for a given adverse event concept — e.

SNOMED CT allows polyhierarchy within axes the 19 broadest groupings of concepts , meaning that a given concept can have any number of parent concepts from the same axis. The polyhierarchy feature is desirable for large terminologies, because it allows multiple strategies for terminology navigation and data integration, and can enable the automated placement of new concepts to facilitate terminology growth and maintenance. In that regard, Bosquet et al observed that MedDRA behaves like a classification, and the lack of polyhierarchy inside of SOCs can lead to inconsistencies and difficulties querying complete groups of related concepts.

The MedDRA structure is based on a rigid level of imposed term specificity rather than by features of the underlying concepts. Combining medically related AE terms can facilitate detection of safety issues by increasing the power of the analysis through larger numbers, a strategy of particular interest for examining AE data encoded with highly specific MedDRA concepts. Although the structure of MedDRA inherently limits the number of hierarchical relationships, a series of specialized, manually-created Special Search Queries SSCs gathers concepts that are functionally related e.

The down-side is that these lists must be manually maintained — i. There are altogether 1 million associative i. However, there is no definitive argument that more relationships make a given terminology superior; only the context of intended use can determine ideal terminology for a given application or domain. However, the lack of a globally accessible standard AE terminology, and associated electronic tools to facilitate efficient and rapid use of terminology at the point of research data collection, make classifications such as CTCAE an appealing option from the perspective of work flow integration, ease of use, and user acceptance.

Of the three AE coding system, each has pros and cons based upon its structure, content, access, and available tools and support. In addition to historical, political and popularity issues, examination of the structural differences between different terminologies MedDRA and SNOMED CT and between terminologies and classification systems CTCAE can offer insight into the issues that might arise in evaluating or integrating various data standards.

It is important to note that standards have pros and cons that are relative to their intended purpose, and that a specified purpose should also dictate the relative importance of the features that we selected for Table I. Also, pregnancy-related outcomes and effects of confidentiality breeches are not included. Further, by definition, worsening of existing conditions can constitute AEs, and the CTCAE, which was created to classify drug toxicities, is not comprehensive in this regard.

Classifications have a requirement to count instances once and only once for a given group or class, and are generally considered unsuitable for primary clinical data collection. Therefore, each of the AE coding systems we present was designed for different purposes and has different expectations. The information in Table 1 is not presented to evaluate any of these coding systems as the features we include in our summary have not been weighed for importance for the AE or clinical research domain.

The ideal standard would certainly have coverage enough to support the domain, as well as have a structure, supporting tools, training, experience and user base to be used consistently and reliably for the data observed.

Additionally, the content representations should be specific enough to support the immediate intended use-cases i. The evaluation of data standards includes assessing content coverage and the structural and maintenance features that affect its use for a given purpose.

We emphasize again that no data standard can be evaluated outside the context of its intended purpose. The table of attributes does not consider the relative importance of each feature relative to specified data standards goals and objectives. In order to meaningfully evaluate heterogeneous coding system for adverse events, the clinical research community as a whole should add to and weigh these criteria.

Meanwhile, our summary can be used to objectively look at the problem of multiple data standards and develop strategies for their interoperation. Further work should examine the performance of these three data standards in terms of real applications and planned data analyses. In addition to the features presented in Table I , other criteria that are important to the implementation and usefulness of the standards should be considered.

A weighing of certain structural features, combined with quantitative estimates for burden and costs of implementation, could shed light on ideal data standards and strategies of this area. Other criteria related to the access, use, and support of each terminology will also be relevant in terms of increased likelihood of adoption. Finally, measures of reliability in coding, ease in coding, efficiency of term retrieval, and data mining potential might illuminate best terminologies for primary or secondary coding.

Results from this type of inquiry might generate a measure of complexity and act as a surrogate for predicting the burden of implementation. The intended purpose of the standards should dictate the evaluation criteria and rating for various competing standards. Because each of the AE data standards we discuss has positive features and a strong user base in distinct areas of clinical research and care delivery activities, the continuation of their use — at least in the short-term - is probable.

However, the relationship between them needs to be defined. Long-term activities of relevant stakeholders might include the harmonization of heterogeneous coding systems. In the long-term, purpose-driven cooperation of relevant stakeholders could possibly spawn a co-evolution of the three heterogeneous data standards, resulting in assimilation of the three coding schemes into one.

The need for mappings between biomedical terminologies usually arises when data encoded in one terminology is reused for a secondary purpose that requires a different system of encoding. Mapping is the most common work-around strategy to solve problems of data interoperability in the absence of a single terminology standard. However, the mapping approach has genuine shortcomings and limitations and should not obviate the quest for a single unified terminology standard.

The process for creating cross-terminology mappings itself is time-consuming and labor-intensive. Automated mapping tools have been developed, but their relatively low levels of recall and precision dictate their use as assistants to human editors rather than a substitute for human resources.

Another problem with the use of mappings is the poorly-defined nature of the mapping relationship. As a result, many of the non-synonymous mappings are from a narrower to a broader concept. There are potential issues in using the non-synonymous mappings for term translation, among them are the problems of information loss and ambiguity.

Information loss occurs when a more specific term is translated into a less specific term e. Ambiguity occurs when there are multiple eligible mapping for a particular source concept e. Another issue of mappings is that their use is almost always context specific. One set of mappings created for a specific use case may not be usable in another. The creation of a set of mappings is only the first step. Ongoing maintenance and updating of mappings is another major effort.

Theoretically, all mappings should be reviewed whenever the terminologies on either end of the mappings are updated. How best to handle versioning in mappings is still a largely unresolved issue. There are also more specific problems in mapping between the three terminologies under discussion. As mentioned before, CTCAE does not cover diseases which are not adverse events and this will limit the extent to which the other two terminologies can be mapped to it. In addition, the grading information in CTCAE is not easily matched in the other terminologies in which severity grading is often absent or defined differently.

Even though it is felt that a standardized mapping of grading information is important, the best approach of achieving this has not been decided upon.

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MedDRA: An Investment for Regulatory Activities Points to Consider Working Group “Coding” data into a standardised terminology facilitates its. The Introductory Guide for Standardised MedDRA Queries (SMQs), prepared in. English, is intended only for use with the English version of. MedDRA Term Selection: Points to Consider” document, the practical use of MedDRA in coding will be discussed based on various examples.