The STEADY diabetic foot ulcer registry: methods, insights, and future directions.

Abstract

Background: Diabetic foot ulcers (DFUs) are a major cause of morbidity, amputation, and mortality among individuals with diabetes, with disproportionate impact on underserved populations. Comprehensive real-world data on DFU management and outcomes are lacking.

Objective: To describe the design and methodology of the STEADY (Structured Evaluation and Analysis of Diabetic Foot Ulcers in the US) registry, a national prospective cohort study of patients with DFUs in the United States whose objective is to evaluate DFU treatment patterns, outcomes, and health care resource utilization in real-world settings, to assess comparative effectiveness, cost effectiveness, and safety of DFU therapies and therapy combinations, and to advance disease management through risk- and site-stratified treatment optimization models.

Methods: STEADY is a 10-year prospective multicenter observational study with an aim of enrolling 5000 adults with active DFUs in the United States. Data sources include electronic case report forms, electronic medical records (EMRs), patient-reported outcomes via mobile app, and optional insurance claims. Primary and secondary end points will include time and incidence of partial and complete wound closure; wound and disease characteristics; rates of recurrence, infection, ischemic events, and amputation; health care utilization, including surgical procedures; health-related quality of life; work productivity; and additional patient reported outcomes. Descriptive, survival, and comparative effectiveness analyses will be performed. Data governance ensures full regulatory compliance and robust data security and integrity, supporting the potential use of the registry dataset as a synthetic control arm in future clinical research.

Conclusion: STEADY leverages an artificial intelligence (AI)-enabled platform to integrate multisource data, including wound photography, social determinants of health, patient reported outcomes and caregiver information. The platform uses AI for transcription and interpretation of patient and provider dictation, supports patient-controlled EMR synchronization for comprehensive longitudinal tracking across providers, offers participant incentives to enhance engagement, and ensures rigorous, automated data quality assurance at all stages.