Automated quantification of neuropad improves its diagnostic ability in patients with diabetic neuropathy Academic Article uri icon

Overview

MeSH Major

  • Autonomic Nervous System
  • Diabetic Neuropathies
  • Foot Ulcer

abstract

  • Neuropad is currently a categorical visual screening test that identifies diabetic patients at risk of foot ulceration. The diagnostic performance of Neuropad was compared between the categorical and continuous (image-analysis (Sudometrics)) outputs to diagnose diabetic peripheral neuropathy (DPN). 110 subjects with type 1 and 2 diabetes underwent assessment with Neuropad, Neuropathy Disability Score (NDS), peroneal motor nerve conduction velocity (PMNCV), sural nerve action potential (SNAP), Deep Breathing-Heart Rate Variability (DB-HRV), intraepidermal nerve fibre density (IENFD), and corneal confocal microscopy (CCM). 46/110 patients had DPN according to the Toronto consensus. The continuous output displayed high sensitivity and specificity for DB-HRV (91%, 83%), CNFD (88%, 78%), and SNAP (88%, 83%), whereas the categorical output showed high sensitivity but low specificity. The optimal cut-off points were 90% for the detection of autonomic dysfunction (DB-HRV) and 80% for small fibre neuropathy (CNFD). The diagnostic efficacy of the continuous Neuropad output for abnormal DB-HRV (AUC: 91%, P = 0.0003) and CNFD (AUC: 82%, P = 0.01) was better than for PMNCV (AUC: 60%). The categorical output showed no significant difference in diagnostic efficacy for these same measures. An image analysis algorithm generating a continuous output (Sudometrics) improved the diagnostic ability of Neuropad, particularly in detecting autonomic and small fibre neuropathy.

publication date

  • January 2015

Research

keywords

  • Academic Article

Identity

Language

  • eng

PubMed Central ID

  • PMC4443893

Digital Object Identifier (DOI)

  • 10.1155/2015/847854

PubMed ID

  • 26064991

Additional Document Info

start page

  • 847854

volume

  • 2015