RE-CODE DCM (REsearch Objectives and Common Data Elements for Degenerative Cervical Myelopathy): A Consensus Process to Improve Research Efficiency in DCM, Through Establishment of a Standardized Dataset for Clinical Research and the Definition of the Research Priorities.

STUDY

DESIGN

Mixed-method consensus process.

OBJECTIVES

Degenerative cervical myelopathy (DCM) is a common and disabling condition that arises when mechanical stress damages the spinal cord as a result of degenerative changes in the surrounding spinal structures. RECODE-DCM (REsearch Objectives and Common Data Elements for Degenerative Cervical Myelopathy) aims to improve efficient use of health care resources within the field of DCM by using a multi-stakeholder partnership to define the DCM research priorities, to develop a minimum dataset for DCM clinical studies, and confirm a definition of DCM.

METHODS

This requires a multi-stakeholder partnership and multiple parallel consensus development processes. It will be conducted via 4 phases, adhering to the guidance set out by the COMET (Core Outcomes in Effectiveness Trials) and JLA (James Lind Alliance) initiatives. Phase 1 will consist of preliminary work to inform online Delphi processes (Phase 2) and a consensus meeting (Phase 3). Following the findings of the consensus meeting, a synthesis of relevant measurement instruments will be compiled and assessed as per the COSMIN (Consensus-based Standards for the Selection of Health Measurement Instruments) criteria, to allow recommendations to be made on how to measure agreed data points. Phase 4 will monitor and promote the use of eventual recommendations.

CONCLUSIONS

RECODE-DCM sets out to establish for the first time an index term, minimum dataset, and research priorities together. Our aim is to reduce waste of health care resources in the future by using patient priorities to inform the scope of future DCM research activities. The consistent use of a standard dataset in DCM clinical studies, audit, and clinical surveillance will facilitate pooled analysis of future data and, ultimately, a deeper understanding of DCM.