Individualised screening of diabetic foot: creation of a prediction model based on penalised regression and assessment of theoretical efficacy

A large proportion of people with diabetes do not receive proper foot screening due to insufficiencies in healthcare systems. Introducing an effective risk prediction model into the screening protocol would potentially reduce the required screening frequency for those considered at low risk for diabetic foot complications. The main aim of the study was to investigate the value of individualised risk assignment for foot complications for optimisation of screening. From 2015 to 2020, 11,878 routine follow-up foot investigations were performed in the tertiary diabetes clinic. From these, 4282 screening investigations with complete data containing all of 18 designated variables collected at regular clinical and foot screening visits were selected for the study sample. Penalised logistic regression models for the prediction of loss of protective sensation (LOPS) and loss of peripheral pulses (LPP) were developed and evaluated. Using leave-one-out cross validation (LOOCV), the penalised regression model showed an AUC of 0.84 (95% CI 0.82, 0.85) for prediction of LOPS and 0.80 (95% CI 0.78, 0.83) for prediction of LPP. Calibration analysis (based on LOOCV) presented consistent recall of probabilities, with a Brier score of 0.08 (intercept 0.01 [95% CI −0.09, 0.12], slope 1.00 [95% CI 0.92, 1.09]) for LOPS and a Brier score of 0.05 (intercept 0.01 [95% CI −0.12, 0.14], slope 1.09 [95% CI 0.95, 1.22]) for LPP. In a hypothetical follow-up period of 2 years, the regular screening interval was increased from 1 year to 2 years for individuals at low risk. In individuals with an International Working Group on the Diabetic Foot (IWGDF) risk 0, we could show a 40.5% reduction in the absolute number of screening examinations (3614 instead of 6074 screenings) when a 10% risk cut-off was used and a 26.5% reduction (4463 instead of 6074 screenings) when the risk cut-off was set to 5%. Enhancement of the protocol for diabetic foot screening by inclusion of a prediction model allows differentiation of individuals with diabetes based on the likelihood of complications. This could potentially reduce the number of screenings needed in those considered at low risk of diabetic foot complications. The proposed model requires further refinement and external validation, but it shows the potential for improving compliance with screening guidelines.