Predicting the Path to Attrition: Multidomain Risk Assessment in Diabetic Foot Ulcer Offloading Randomized Controlled Trials.

Diabetes CareSep 1, 2026
Clinical ResearchEndocrinologyOpen access

Aminreza Khandan, Mohammad Dehghan Rouzi, David G Armstrong et al.

✦ AI-curated · Sources linked

In 30 seconds

This secondary analysis of a 12-week randomized controlled trial examined whether baseline wound, functional, and psychosocial measures predicted attrition among people with diabetic foot ulcers using offloading boots. Participants were assigned to removable boots, removable boots plus education, or smart removable boots. Of 210 participants, 76 (36%) withdrew or were lost to follow-up. Slower gait and greater depressive symptoms independently predicted attrition, while integrated smart-boot metrics showed strong discrimination (AUC 0.81). The authors suggest multidomain risk assessment may help guide retention strategies.

Key findings

  • Of 210 participants, 76 (36%) withdrew or were lost to follow-up.
  • Slower gait independently predicted attrition (OR 1.16).
  • Higher depressive symptoms independently predicted attrition (OR 1.52).
  • Integrated smart boot metrics discriminated attrition risk with an AUC of 0.81.

Why it matters

Attrition can affect the completeness and interpretation of diabetic foot ulcer trial results. These findings indicate that functional and psychosocial measures may help identify participants at greater risk of leaving follow-up, while smart-device metrics may contribute to risk assessment. The results support considering retention as a multidomain issue rather than relying only on wound characteristics.

What to keep in mind

This was a secondary analysis of one 12-week trial; the abstract does not report external validation of the attrition-risk approach.

Source

Published in Diabetes Care. This summary was written by xxcode from the publication's abstract and metadata. It is not peer reviewed and is not a substitute for the original article. For clinical decisions, review the original publication.

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AI-generated summaries may contain errors or omissions. Verify clinically important information with the original publication.

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