DIAGNOSTICS ALGORITHM OF DIABETIC POLYNEUROPATHY IN PREDICTION OF CLINICAL COURSE

Area of Science:

• Neurology
• Endocrinology
• Medical Diagnostics

Background:

• Diabetic polyneuropathy (DPN) is a common complication of type 2 diabetes.
• Accurate assessment of DPN severity and clinical course prediction are crucial for patient management.

Purpose of the Study:

• To evaluate the impact of a novel algorithm for DPN diagnostics.
• To assess the algorithm's effectiveness in determining DPN severity and predicting clinical outcomes at the NEFU medical institute clinic.

Main Methods:

• The study included 50 patients with sensory-motor DPN and type 2 diabetes.
• Patients were categorized based on diabetes duration (≤10 years and >10 years).
• Diagnostic methods included clinical neurological examination, neurophysiological tests (polymodal sensitivity analysis, computer pallesteziometry, stabilometry, electroneuromyography).

Main Results:

• A significant correlation was found between clinical and neurophysiological parameters of DPN and the duration of type 2 diabetes.
• The implemented algorithm demonstrated improved efficacy in diagnosing DPN severity.

Conclusions:

• The duration of type 2 diabetes is a key factor influencing clinical and neurophysiological DPN parameters.
• The novel diagnostic algorithm enhances the accuracy of DPN assessment and aids in predicting the disease's clinical progression.