The National Institute of Health (NIH) Common Data Elements (CDEs) initiative has taken a largely theory-driven approach to development. Research and clinical adoption has lagged well behind expectations. One factor limiting adoption is the lack of attention given to parallel development of applied tools that target clinical utility. This article presents an example of one such applied approach using melanoma tumor location. The end results include: 1) clinically derived CDE location modifications, 2) a standard image for the visual display of tumors, and 3) programming code to overlay tumor location markers on the standard image in a web-based environment. It is hoped that others will build and share similar applied tools to help CDE adoption.
In Theory, Applied May Work :)
The National Institute of Health (NIH) Common Data Elements (CDEs) are a collection of data definition standards targeting various research domains, typically consisting of a question and set of permissible responses. For example, the variable “Ear” has two permissible values, “Left” and “Right”. The primary aims of the CDE effort is to establish consistent data definition standard to enhance comparisons across projects, and to facilitate large-scale data consolidation and analysis techniques.
NIH has largely relied on a theory-driven approach lead by the investigative community to create and enhance CDEs. Although clearly one component of what needs to be done, reliance on establishing data definition standards exclusively in this manner risks:
Research Tail Wagging the Dog
Theory-driven new or more precise data definition targets guide CDE changes and enhancements, which are often nuanced and/or years ahead of clinical significance or utility.
An Un-Crossable Chasm
Poor adoption outside the research community due to limited clinical utility.
These two factors have served to limit adoption within clinical settings and thus hampered hopes of data consolidation and analysis initiatives. A number of factors are needed to help increase CDE adoption, an important player in this regard is thought to be parallel development of applied tools, materials and ideas that target use within clinical settings. This article is intended as an applied example of advancing CDEs. A case study of CDE modification is presented using melanoma ‘Tumor Location’ (Public ID: 4320475). The end results include 1) clinically derived modifications to the CDE, 2) an image for the standard display of tumor locations, and 3) programming code to overlay tumor location(s) on the standard image in a web-based environment.
Transforming CDEs Into Action
Providing clinical utility is a powerful incentive to increase adoption of data standards, as well as accurate and timely data capture. Thus, shifting the focus of CDE development to include practical tools that assist with case conceptualization and treatment would greatly enhance adoption and timely data collection. Consider the overwhelming number of forms and variable values collected in the case below and the daunting challenge to come up with ways to quickly grasp and understand the treatment history (see image).
In terms of visualizing the case, the end-result of these applied CDE efforts is seen below which consists of a standard body image that incorporates all possible tumor locations. When multiple tumors are in the same location, the number of tumors will appear in the circle. Note that these efforts were deployed within an active clinical setting. Thus, summarizing tumor location and other clinical data in a report was an important concern. The image overlay was one section of the report. A couple of other standardized sections related to tumor information are described below. More information on clinical report design techniques can be found here.
A standardized image with example indicator images (i.e., red circle) over tumor areas.
A dynamic table was also created that organized tumor location information into system groups (see below). The column heading background color was set to red for system groups that contained at least one tumor. The total number of tumors in each system was also displayed (see below).
Tumor number within each procedure site, conditionally colored red if tumor at site
A standard visual display of tumor onset was also developed to provide a quick mechanism to appreciate the timing of clinical events (see below).
Treatment Timeline table
Example 2: Treatment Timeline table
Information about the existing melanoma tumor location CDE is shown in Table 1 for this important prognostic factor (1-3).
Table 1: Melanoma Anatomic Site
Table 2 compares the proposed and the existing response set. Modifications targeted new sites, increased granularity (e.g., face), and/or lateralization (i.e., left/right/mid-line/other) thought to be clinically useful. Since the rationale for modifications may vary considerably across settings, it is important to note the intended target was an academic surgical registry. Medical Subject Headings (MeSH) Tree Numbers and IDs are presented for the proposed sites, if available.
Table 2: Melanoma Anatomic Site: Existing and Proposed Values
Note: * = Proposed new site. Laterality – L = Left, R = Right, M = Midline, O = Other.
The stand image below was constructed to capture all possible tumor locations. That is, the breadth and level of granularity matched the CDE locations. An indicator image (e.g., red circle) was used to overlay tumor locations.
Tumor Location Pixel Coordinates
Skin and Subcutaneous
Having these additional parameters sets the stage for a number of exciting possibilities in pursuit of quick and efficient consumption of data. For example, varying the size of the indicator image based on the Clark level.
At the location on the webpage where the image is to be shown, insert the following code block.
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2. Beaudoux O, Riffaud L, Barbe C, Grange FEur. Prognostic factors and incidence of primary mucosal melanoma: a population-based study in France. J Dermatol. 2018 Oct 1;28(5):654-660.
3. Farahi, J. M, Fazzari, M., Braunberger, T., Caravaglio, J. V, Kretowicz, A., Wells, K., et al. (2018). Gender differences in melanoma prognostic factors. Dermatology Online Journal, 24(4).