  Background: Diabetic Sensorimotor polyneuropathy (DSPN) is a major long-term
complication in diabetic patients associated with painful neuropathy, foot
ulceration and amputation. The Michigan neuropathy screening instrument (MNSI)
is one of the most common screening techniques for DSPN, however, it does not
provide any direct severity grading system. Method: For designing and modelling
the DSPN severity grading systems for MNSI, 19 years of data from Epidemiology
of Diabetes Interventions and Complications (EDIC) clinical trials were used.
MNSI variables and patient outcomes were investigated using machine learning
tools to identify the features having higher association in DSPN
identification. A multivariable logistic regression-based nomogram was
generated and validated for DSPN severity grading. Results: The top-7 ranked
features from MNSI: 10-gm filament, Vibration perception (R), Vibration
perception (L), previous diabetic neuropathy, the appearance of deformities,
appearance of callus and appearance of fissure were identified as key features
for identifying DSPN using the extra tree model. The area under the curve (AUC)
of the nomogram for the internal and external datasets were 0.9421 and 0.946,
respectively. From the developed nomogram, the probability of having DSPN was
predicted and a DSPN severity scoring system for MNSI was developed from the
probability score. The model performance was validated on an independent
dataset. Patients were stratified into four severity levels: absent, mild,
moderate, and severe using a cut-off value of 10.5, 12.7 and 15 for a DSPN
probability less than 50%, 75% to 90%, and above 90%, respectively.
Conclusions: This study provides a simple, easy-to-use and reliable algorithm
for defining the prognosis and management of patients with DSPN.
