Scoping review to develop common data elements for lumbar spinal stenosis.

BACKGROUND CONTEXT: Common data elements (CDE) represent an important tool for understanding and classifying health outcomes across settings. Although CDEs have been developed for a number of disorders, to date CDEs for lumbar spinal stenosis (LSS) have not been fully developed. To facilitate the identification of CDEs and measures to assess them, this technical study leverages the International Classification of Functioning, Disability and Health (ICF), peer-reviewed research, and a panel of experts to identify CDEs specific to LSS.

PURPOSE: The study aimed to define CDEs for disease characteristics and outcomes of LSS using the World Health Organization's ICF taxonomy, and to facilitate the selection of assessment instruments for research and clinical care.

DESIGN: This is a scoping review using a modified Delphi approach with a technical expert panel composed of clinicians and scientists representing the academia, policy and advocacy stakeholders, and professional associations with expertise in LSS.

METHODS: This is a scoping review to identify measures that assess LSS symptoms. Thirty-one subject matter experts (SMEs) prioritized ICF codes and evaluated instruments measuring specific domains. We used a modified Delphi technique to evaluate item-level content and achieve consensus.

RESULTS: SMEs prioritized 53 ICF codes; 3 received 100% endorsement, 27 received ≥90% endorsement, whereas the remaining 23 received ≥80% endorsement. Prioritized ICF codes represent diverse domains, including pain, activities and participation, and emotional well-being. The review yielded 58 instruments; we retained 24 for content analysis.

CONCLUSIONS: The retained instruments adequately represent the ICFs activities and participation, and body function domains. Body structure and environmental factors were assessed infrequently. Adoption of these CDEs may guide clinical decision making and facilitate comparative effectiveness trials for interventions focused on LSS.