Michigan body map: connecting the NIH HEAL IMPOWR network to the HEAL ecosystem

Background

As part of the National Institutes of Health (NIH) Helping to End Addiction Long-term (HEAL) Initiative, the Integrative Management of Chronic Pain and OUD for Whole Recovery (IMPOWR) Network combines 11 studies from 6 different institutions to examine the combined impact of chronic pain and opioid use disorder (OUD).^1,2 The NIH HEAL Initiative is a trans-agency effort to speed scientific solutions to stem the national opioid public health crisis. Its goals are to increase the understanding and treatment of pain and improve the treatment and prevention of OUD and overdose. One of the unique features of the IMPOWR network is the harmonization of data collection across chronic pain and OUD studies through the use of shared common data elements (CDEs), including assessment of the location of a participant’s chronic pain.^3,4 Currently, the HEAL CDE program does not require the use of a body map.³ However, several established pain body maps are in use across HEAL.

The Michigan Body Map (MBM) is a validated self-reported measure of pain location that was developed to quantify pain locations and allow for the assessment of both widespread and centralized pain (Figure 1).⁵ The MBM can be completed in less than a minute, which allows for rapid use with low study burden. Since its development, the MBM has been validated and compared with the Brief Pain Inventory (BPI) body map and the original Widespread Pain Index (WPI) for fibromyalgia.^5,6 It is currently being validated for use in both acute and chronic pain in the NIH Acute to Chronic Pain Signatures network and is being used within the HEAL Back Pain Consortium (BACPAC) and Pragmatic and Implementation Studies for the Management of Pain to Reduce Opioid Prescribing (PRISM) networks.⁷

Figure 1.

Michigan Body Map.

The IMPOWR Dissemination Education and Coordination Center (IDEA-CC) implemented the CDE selection process in the IMPOWR network.¹ The IMPOWR network chose to use the MBM for several reasons. First, the MBM is used in several HEAL studies, which allows secondary data analyses through connection to and harmonization with other datasets. A second feature is the ability to automatically calculate widespread pain regions and the WPI.⁸ The MBM’s connection with chronic overlapping pain conditions (COPC) and WPI enables collaboration with HEAL and non-HEAL pain studies that include these measures rather than a body map.

A challenge common to IMPOWR and many of the HEAL networks is the survey burden for participants, including the number of questions per survey and the breadth of domains (eg, stigma, trauma, function) needed to capture the biopsychosocial nature of pain and OUD. The IDEA-CC team focused on meeting both scientific and user-friendly design needs by leveraging technology.

Although the MBM is in the REDCap image library,⁹ it had several barriers to use for investigators. To start, the 34 body parts on the map are unlabeled in the REDCap library. Additionally, REDCap formatting does not allow for easy incorporation of the HEAL-assigned variable names. In an evaluation of programming from existing HEAL projects, none had started applying the new NIH HEAL-assigned CDE variable names to the MBM for use. Many HEAL teams have approached this problem through complicated postprocessing and programming steps. Given the complexity of our multisite network’s projects and planned data pipeline, our IDEA-CC team focused on developing front-end solutions for both our projects that could have secondary uses in other projects.

Methods

IDEA-CC performed the initial programming of the HEAL and non-HEAL CDE variables used for the network into REDCap format. Programming was performed in REDCap, with the HEAL-assigned variable names used for all components of both the acute and chronic versions of the MBM. We then developed separate scoring equations for each of the 35 variables and calculation variables for WPI regions and the WPI. Two additional steps were taken for the MBM programming to connect with data sources beyond the HEAL networks. The variables were then mapped to Clinical Data Interchange Standards Consortium (CDISC) codes in the REDCap library, as well as to International Classification of Diseases (ICD-10) codes in a separate file.

Results

The IDEA-CC has developed several layers of data programming that enhance the MBM painful area identification and connect these data to other existing data, making data findable, accessible, interoperable, and reusable (FAIR), in alignment with the NIH HEAL Data Ecosystem.¹⁰ Our design focus incorporated ease of use for both investigators and participants. Because of permissions associated with use, the preprogrammed REDCap libraries for the acute and chronic pain MBM dictionaries will be available via request to the HEAL Data team or corresponding author for non–HEAL-funded requests, and the CDISC and ICD-10 mapping are available with citation and attribution. The open sharing of the REDCap survey will create a resource that is easy for participants to complete by clicking on affected areas. Investigators will have a ready-to-deploy resource that provides pain location and auto-calculates key scores, allowing combination with other data sources.

Discussion

The NIH HEAL IMPOWR network is one of many HEAL Initiative studies developing solutions for the prevention and treatment of pain and addiction. Recognizing this, the IDEA-CC has focused on both our initial responsibility of harmonizing data with the IMPOWR network and also finding ways to enhance the impact while decreasing the burden of study data collection on the participants and for the sites. An important part of harmonization was that we incorporated the established HEAL variable names in the REDCAP library, which allows these data to connect with other studies at the variable level without an additional postprocessing step.

Furthermore, we wanted to take advantage of the ability to automate connections between a study using MBM with other research studies through WPI regions and scoring.⁶ Additionally, by linking the MBM to both concept and billing codes, these data can also link to other electronic health record and claims data. This flexible ability to implement these data and connect with other sources will support secondary data analysis and novel combinations of data. Our hope is that the ready-to-use library and supplemental mappings will facilitate the easy addition of this body map to studies without pain as the primary outcome or focus, while adding minimal burden for participants and researchers.

Funding

Research reported in this publication was supported by NIH National Institute of Biomedical Imaging and Bioengineering of the National Institutes of Health under grant number K08EB022631 and the NIH HEAL Initiative and National Institute of Drug Abuse under grant number R24DA055306.

This report does not represent the official view of the National Institute of Neurological Disorders and Stroke (NINDS), the National Institutes of Health (NIH), or any part of the US Federal Government. No official support or endorsement of this article by the NINDS or NIH is intended or should be inferred.

Conflicts of interest: None declared.