Automated Quantification of Neuropad Improves Its Diagnostic Ability in Patients with Diabetic Neuropathy

Abstract EN

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.