Aims: Pedal sensory loss due to diabetes-related neuropathy can be graded by testing static two-point discrimination (S2PD), moving two-point discrimination (M2PD), static one-point discrimination (S1PD; eg, 10-g monofilament), and vibration sense and is included in the Rotterdam Diabetic Foot (RDF) Study Test Battery. The aim of this study is to investigate if decision tree modelling is able to reduce the number of tests needed in estimating pedal sensation. Methods: The 39-item RDF Study Test Battery (RDF-39) scores were collected from the prospective RDF study and included baseline (n = 416), first follow-up (n = 364), and second follow-up (n = 135) measurements, supplemented with cross-sectional control data from a previous study (n = 196). Decision tree analysis was used to predict total RDF-39 scores using individual test item data. The tree was developed using baseline RDF study data and validated in follow-up and control data. Spearman correlation coefficients assessed the reliability between the decision tree and original RDF-39. Results: The tree reduced the number of items from 39 to 3 in estimating the RDF-39 sum score. M2PD (hallux), S2PD (first dorsal web, fifth toe), vibration sense (interphalangeal joint), and S1PD (first dorsal web, fifth toe) measurements proved to be predictive. The correlation coefficients to original scores were high (0.76 to 0.91). Conclusions: The decision tree was successful at reducing the number of RDF Test Battery items to only 3, with high correlation coefficients to the scores of the full test battery. The findings of this study aids medical decision making by time efficiently estimating pedal sensory status with fewer tests needed.

decision making, decision tree analysis, item reduction, risk assessment, sensory testing,
Diabetes/Metabolism Research and Reviews
Department of Plastic and Reconstructive Surgery

Rinkel, W.D, van der Oest, M.J.W. (Mark J. W.), & Coert, J.H. (2020). Item reduction of the 39-item Rotterdam Diabetic Foot Study Test Battery using decision tree modelling. Diabetes/Metabolism Research and Reviews. doi:10.1002/dmrr.3291