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Sudomotor Testing of Diabetes Polyneuropathy.

Sarah-Maria Krieger | Manja Reimann | Rocco Haase | Elena Henkel | Markolf Hanefeld | Tjalf Ziemssen
Frontiers in neurology | 2018

Objective: The performance of the Sudoscan technology for diagnosing diabetic polyneuropathy (DPN) was evaluated against the quantitative sudomotor axon reflex test (QSART). Furthermore, the association of Sudoscan with two clinical neuropathy scoring systems was evaluated. Methods: Forty-seven patients with type 2 diabetes (20 without DPN, 27 with DPN) and 16 matched controls were examined for neuropathic symptoms and for the extent of sensory deficits. Sweat latency and volume by QSART and the skin electrochemical conductance (ESC) by Sudoscan were measured. Results: The feet and hand ESC was significantly lower in patients with DPN as compared to controls. Patients with DPN had also lower hand ESC than patients without DPN. Sensitivity and specificity of feet and hand ESC for detecting DPN were 70/85% and 53/50% respectively. QSART could not differentiate between the three groups. ESC was inversely related to neuropathic symptoms and sensory impairment. ESC was significantly correlated with sensory impairment and pain. Conclusions: Sudoscan shows a good performance in detecting subjects with DPN and it correlates well with clinical signs and symptoms of neuropathy. Significance: This study provides evidence that Sudoscan has high potential to be used as screening tool for DPN and possibly also for small fiber neuropathy in diabetic patients. HIGHLIGHTS - The sudomotor function test Sudoscan shows a good performance to detect diabetes peripheral neuropathy.- Sudoscan measures significantly correlate with clinical signs and symptoms of neuropathy.- The Sudoscan technology may help to secure clinical diagnosis of small fiber neuropathy.

Pubmed ID: 30319533

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RRID:SCR_002865

Software package used for interactive, or batched, statistical analysis in social science, health sciences and marketing. Software platform offers advanced statistical analysis, a library of machine-learning algorithms, text analysis, open-source extensibility, integration with big data and deployment into applications.Versions that were produced by SPSS Inc. before the IBM acquisition (Versions 18 and earlier) would be given origin or publisher of SPSS Inc. in Chicago.

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