Development of a prediction model for foot ulcer recurrence in people with diabetes using easy-to-obtain clinical...

We aimed to develop a prediction model for foot ulcer recurrence in people with diabetes using easy-to-obtain clinical variables and to validate its predictive performance in order to help risk assessment in this high-risk group.

We used data from a prospective analysis of 304 people with foot ulcer history who had 18-month follow-up for ulcer outcome. Demographic, disease-related and organization-of-care variables were included as potential predictors. Two logistic regression prediction models were created: model 1 for all recurrent foot ulcers (n=126 events) and model 2 for recurrent plantar foot ulcers (n=70 events). We used 10-fold cross-validation, each including five multiple imputation sets for internal validation. Performance was assessed in terms of discrimination using area under the receiver operating characteristic curve (AUC) (0-1, 1=perfect discrimination), and calibration with the Brier Score (0-1, 0=complete concordance predicted vs observed values) and calibration graphs.

Predictors in model 1 were: a younger age, more severe peripheral sensory neuropathy, fewer months since healing of previous ulcer, presence of a minor lesion, use of a walking aid and not monitoring foot temperatures at home. Mean AUC for model 1 was 0.69 (2SD 0.040) and mean Brier Score was 0.22 (2SD 0.011). Predictors in model 2 were: a younger age, plantar location of previous ulcer, fewer months since healing of previous ulcer, presence of a minor lesion, consumption of alcohol, use of a walking aid, and foot care received in a university medical center. Mean AUC for model 2 was 0.66 (2SD 0.023) and mean Brier Score was 0.16 (2SD 0.0048).

These internally validated prediction models predict with reasonable to good calibration and fair discrimination who is at highest risk of ulcer recurrence. The people at highest risk should be monitored more carefully and treated more intensively than others.

NTR5403.